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Getting Started with Amazon Neptune

Databases | 7 Videos | About 9 Hours

Learning Path

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Table of Contents

1. Create and Connect to Amazon Neptune Database

• Options to create a database, and configure security
• How to connect to Neptune using workbench and API
• Bulk load data into the cluster for querying

2. Data Modeling for Amazon Neptune

• The basics of the RDF and property graph data models
• How to apply these models when designing an Amazon Neptune graph application
• How to evolve your application graph data model to accommodate new use cases and queries

3. Writing Your Graph Queries Against Amazon Neptune

• The basics of Gremlin and SPARQL query syntax to insert, query and delete data
• Advanced query concepts such as federated query, traversal strategies
• Best practices writing Gremlin and SPARQL queries

4. Building End-to-End Graph Use Cases with Amazon Neptune

• How Neptune can be used to build an identity graph
• How Neptune can be used to build knowledge graph
• How Neptune can be used in fraud detection solution

5. Managing Your Amazon Neptune Database

• Monitor your Neptune cluster using CloudWatch
• How to right size your Neptune cluster
• Info about snapshots, backups, and software upgrades

6. Troubleshooting Amazon Neptune - Query Performance

• Graph data model and indexes used by Neptune
• Workload performance benchmarking and profiling a Gremlin or SPARQL query
• Troubleshooting query performance

7. Integrating Amazon Neptune with Other AWS Services

• How to integrate Amazon Neptune with other AWS services using Neptune Streams
• How to use AppSync with Amazon Neptune
• How to use AWS Glue with Amazon Neptune

Description

Amazon Neptune is a fast, reliable, fully managed graph database service that makes it easy to build and run applications that work with highly connected data sets. The core of Amazon Neptune is a purpose-built, high-performance graph database engine optimized for storing billions of relationships and querying the graph with milliseconds latency. You can build knowledge graphs, fraud graphs, identity graphs, recommendation engines, master data management, and network security applications using Neptune. In this video series you will learn how to get started with Amazon Neptune. You will learn the use cases and the basics of Neptune including creating and managing your cluster, building popular graph models Property Graph and W3C’s RDF, writing queries using Apache TinkerPop Gremlin and SPARQL, troubleshooting performance, and integrating with tools and services such as Elasticsearch and AWS Glue.

Learning Objectives

Objective One

Learn how to build your graph applications by creating and managing your cluster

Objective Two

Build your Property Graph model or RDF model and query your graph using Gremlin and SPARQL respectively

Objective Three

Integrate Neptune with services such as Elasticsearch, Glue or Kinesis

• Learn more about Neptune
• Technical resources for developers
• AWS Reference Architectures for Using Graph Databases
• AWS Database blog for Neptune
• Neptune tools and utilities on GitHub

Who Should Watch?

• IT Professionals
• Database Architects

• United States
• United Kingdom

Amazon Neptune review: A scalable graph database for OLTP

Amazon’s graph database service offers acid properties, immediate consistency, and auto-scaling storage for billions of relationships.

Contributor, InfoWorld |

Amazon Neptune

Graph databases , such as Neo4j , Apache Spark GraphX, DataStax Enterprise Graph, IBM Graph, JanusGraph, TigerGraph , AnzoGraph , the graph portion of Azure Cosmos DB, and the subject of this review, Amazon Neptune, are good for several kinds of applications involving highly connected data sets, such as providing recommendations based on social graphs, performing fraud detection, providing real-time product recommendations, and detecting incursions in network and IT operations. These are areas where traditional, relational databases tend to become inefficient and slow because of the need for complex SQL joins operating on large data sets.

Neptune is a fully managed graph database service with ACID properties and immediate consistency, which has at its core a purpose-built, high-performance graph database engine that is optimized for storing billions of relationships and querying the graph with milliseconds latency. Neptune supports two of the most popular open source graph query languages, Apache TinkerPop Gremlin and W3C SPARQL . The popular Cypher Query Language (CQL) used in Neo4j started off proprietary, but later became open source .

Gremlin and SPARQL address different kinds of graph database. Gremlin, like CQL, is for property graph databases; SPARQL is for Resource Description Framework (RDF) triples, designed for the web. Gremlin is a graph traversal language; SPARQL is a query language with SELECT and WHERE clauses.

The Amazon Neptune implementation allows both the Gremlin and SPARQL languages in a single database instance, but they can’t see each other’s data. The reason for allowing both is to let new users figure out which works better for their needs.

The Neptune documentation has samples using the Gremlin-Groovy, Gremlin-Java, and Gremlin-Python variants of Gremlin. Neptune allows Gremlin in the console, HTTP REST calls, Java, Python, .Net, and Node.js programs. On the SPARQL side, Neptune supports the Eclipse RDF4J console and workbench, HTTP REST calls, and Java programs.

Amazon Neptune features and benefits

As a fully-managed transactional graph database as a service, Amazon Neptune not only offers freedom from the need to maintain the database hardware and software, it also offers easy scaling, greater than 99.99% availability, and multiple levels of security. Neptune database clusters can have up to 64 TB of auto-scaling storage in six replicas of your data across three availability zones, and more if you enable high availability by using read replicas in additional zones.

The Neptune data storage layer is backed by SSDs, fault-tolerant, and self-healing. Disk failures are repaired in the background without loss of database availability. Neptune automatically detects database crashes, and restarts—typically in 30 second or less—without needing to perform crash recovery or to rebuild the database cache, since the cache is isolated from the database processes and can survive a restart. If an entire primary instance fails, Neptune will automatically fail over to one of up to 15 read replicas. Backups are continuously streamed to S3.

You can scale Amazon Neptune clusters up and down either by modifying instances, or (to avoid downtime) by adding an instance of the desired size and shutting down the old instance once a copy of the data has migrated and you have promoted the new instance to primary. Neptune VM instance sizes range from db.r4.large (two vCPUs and 16 GiB of RAM) to db.r4.16xlarge (64 vCPUs and 488 GiB of RAM).

Amazon Neptune implements security by running the engine in a VPC (virtual private cloud) network and optionally encrypting the data at rest using the AWS Key Management Service. In addition to encrypting the underlying storage, Neptune also encrypts the backups, snapshots, and replicas. Neptune is eligible to be used in HIPAA applications . Neptune does not require you to create specific indices to achieve good query performance, which is a welcome change from balancing query and write performance by carefully tuning the indices.

The diagram shows a high-level diagram of Amazon Neptune’s architecture. Note that the Neptune graph engine is between the cloud-native storage service layer and the Gremlin and SPARQL interfaces.

Amazon Neptune does not support analytic query algorithms, such as PageRank, which are featured in some other graph databases, such as Neo4j , TigerGraph , and AnzoGraph . Neptune is intended to be a low-latency transactional (OLTP) graph database for huge data sets, not an analytic (OLAP) database, and is simply not optimized for analytic use cases or queries involving more than three hops—and PageRank touches every item in the database.

Amazon Neptune does support aggregates, so it can do a little analysis, but not a lot. Like Neptune, Neo4j was also originally intended to be used for OLTP, but added an analytic query library in 2017. The fact that analysis capabilities are limited in Neptune is not necessarily a reason to rule it out, as a low-latency OLTP graph database with globally distributed read replicas and the ability to handle 64 TB of data is nothing to sneeze at.

Get started with Amazon Neptune

You can launch an Amazon Neptune cluster two ways: directly from the Amazon Neptune console , or by using an AWS CloudFormation template to create a Neptune stack. Note that the CloudFormation template provided is not suitable for production, as it is not very secure—it is intended as the basis for a tutorial.

You can start small and add capacity—larger VMs, or more read replicas—whenever your application needs it. The storage grows automatically, and you only pay for the storage you use.

In the following screenshots, I show some of the lifecycle of a Neptune image created from the Neptune console. I start with the creation of a cluster.

When you create a Neptune cluster or instance, you need to specify the instance class to use. These range from two to 64 CPUs, with 15 GiB to 488 GiB of RAM.

Do you want high availability for your cluster? It’s not hard at all.

After picking an instance size, you decide whether you want to enable high availability for your cluster. It really is just a single click.

In the advanced settings, there are multiple panels. Fortunately, most of the defaults should serve your needs.

The first two sections of the advanced settings define the Amazon Neptune cluster’s network security and port. In addition, you can enable database authentication via IAM users and roles.

You can enable data encryption and either use an AWS-supplied key or one of your own. You have fine control over the priority of read replicas to become primary in the case of a fail-over, and you can specify the retention period for backups.

Finally, you’ll see a warning before you press the button to launch the database. If you really need to see the explanation, right-click on the link and display it in another tab. (I’m hoping this bug will be fixed.)

After the advanced screens you’ll see a warning about permissions being granted. They’re probably OK, but don’t left-click on the “Learn more” link — when I did so, I lost all my settings and had to start the cluster creation again from scratch.

Once you have a working cluster, you can perform several actions on the instances.

Our primary Amazon Neptune instance and read replica are running and available. You can modify, reboot, delete, or fail-over each one. You can also create a read replica, take a snapshot, or restore the primary to a point in time.

At the cluster level, you can bring up a summary.

Amazon Neptune cluster overview. This is where you can find the cluster writer and reader endpoints.

At the instance level you can see performance graphs.

By default, you can see CPU, memory, and network graphs for an Amazon Neptune instance.

Loading data into Amazon Neptune

To load data into Amazon Neptune , you first load files into Amazon S3 in one of the correct formats : CSV for Gremlin, and triples, quads, RDF/XML, or Turtle for RDF. The loader supports gzip compression of single files.

You’ll need to create an IAM role and S3 VPC endpoint to give Neptune permission to access your S3 bucket unless they were already created, for example by a CloudFormation template. There’s a Neptune loader API callable through a REST endpoint (e.g. from a curl command) that can take on the IAM role and bulk load the data into your cluster. There’s also a converter for GraphML to CSV on GitHub. The data loading walkthrough is for any supported data format.

Query Amazon Neptune with Gremlin

You can use the Gremlin console and a REST endpoint to connect to and query your Amazon Neptune instances in Gremlin from an EC2 VM in the same VPC as the database. More usefully for applications, you can query Neptune using Gremlin in Java , Python , .Net , and Node.js .

There’s an entire book on Gremlin, Practical Gremlin: An Apache TinkerPop Tutorial , by Kelvin Lawrence. The book uses TinkerGraph for its database, but the same Gremlin syntax works for Neptune with small exceptions that are documented by Amazon.

Gremlin queries describe how to navigate graph vertices and edges. An example for the air routes database discussed in the book finds all the ways to fly from Austin, Texas (airport code AUS) to Agra, India (airport code AGR) with two stops:

If you want to try the examples in the book on Amazon Neptune, you’ll first need to copy the edges and nodes CSV files to an S3 bucket using the AWS CLI cp command, and load the data from there into Neptune.

Query Amazon Neptune with SPARQL

You can use the RDF4J console , RDF4J workbench , and a REST endpoint to connect to and query your Amazon Neptune instances in SPARQL from an EC2 VM in the same VPC as the database. More usefully for applications, you can query Neptune using SPARQL in Java . The SPARQL 1.1 Query Language specification defines how to construct queries. If you search the web for “sparql tutorial” you’ll find a number of free written and video tutorials on the subject. These should all work with Neptune once you have loaded the data.

SPARQL looks less like functional code than Gremlin does, and more like SQL. For example:

Amazon Neptune performance and scaling

Amazon Neptune was designed for low-latency graph queries of up to three hops on huge (up to 64 TB) databases. It supports up to 15 low-latency read replicas across three availability zones to scale read capacity and, according to Amazon, can execute more than 100,000 graph queries per second.

Its instance sizes go from two to 64 vCPUs with 15 GiB to 488 GiB of RAM, roughly doubling both at each step. At most, Neptune can use 64 vCPUs times 16 instances for a total of 1024 vCPUs, and 488 GiB RAM times 16 instances for a total of 7808 GiB of RAM. That’s an overall possible scaling growth of 512x, considering both instance size and numbers of read replicas. The costs track the resource scaling pretty much exactly.

If you’re looking for a transactional graph database as a service, Amazon Neptune is a viable choice. Given that Neptune supports both the Gremlin and SPARQL query languages, you should be able to find developers to build applications against it. On the other hand, Neptune’s lack of Cypher support may discourage existing Neo4j users, and its lack of OLAP and graph algorithm support will discourage people who want a single graph database for transactions and analysis.

Amazon Neptune’s wide range of capacities (up to 16x size and 16 instances), high maximum transaction rate (100,000 queries per second), and pay-as-you-go pricing should fit most business requirements. However, its lack of on-premises options may preclude its use at companies with policies against putting their sensitive data in the cloud and companies that prefer incurring and writing down capital expenses to ongoing operating expenses.

Cost: $0.348 to $5.568 per Instance-Hour depending on instance size, $0.10 per GB-month for storage, $0.20 per million I/O requests, $0.023 per GB-month for backup storage, $0.12 per GB for outbound data transfer; various price breaks apply. 

Platform: Amazon Web Services; supports Apache TinkerPop Gremlin or SPARQL queries; access through AWS console, HTTP REST, and Java, as well as RDF4J Workbench (SPARQL) and Python, .Net, and Node.js (Gremlin). 

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Amazon Neptune is a fully managed graph database service with ACID properties and immediate consistency. The purpose-built, high-performance graph database engine at its core is optimized for storing billions of relationships and querying the graph with milliseconds latency.

• Fully managed transactional graph database as a service with four 9’s availability
• Supports the open source Gremlin and SPARQL query languages
• Up to 64 TB of auto-scaling storage in six replicas of your data across three availability zones
• Supports up to 15 read replicas, which can be geographically distributed
• Wide range of instance sizes with easy scaling
• Does not support analytic query algorithms such as PageRank
• Gremlin and SPARQL data stores are isolated from each other
• Does not support graph queries with more than three hops
• Graph Databases
• Amazon Web Services
• Software Development
• Cloud Computing
• Enterprise Review

Martin Heller is a contributing editor and reviewer for InfoWorld. Formerly a web and Windows programming consultant, he developed databases, software, and websites from 1986 to 2010. More recently, he has served as VP of technology and education at Alpha Software and chairman and CEO at Tubifi.

Copyright © 2019 IDG Communications, Inc.

Amazon Neptune: The Ultimate Guide to AWS’s Graph Database Service

Amazon Neptune stands out as a pivotal service within AWS’s extensive portfolio, offering a fully managed graph database engineered to manage highly connected datasets efficiently. This guide delves into the core aspects of Amazon Neptune, highlighting its features, use cases, and the value it brings to modern applications requiring intricate data relationships.

Table of Contents

Understanding amazon neptune, what is amazon neptune.

Amazon Neptune is a fast, reliable, and fully managed graph database service provided by Amazon Web Services (AWS) . It is specifically designed for storing and querying highly connected data, making it an ideal choice for applications that rely on complex datasets such as social networks, recommendation engines, and fraud detection systems. Neptune supports popular graph models like Property Graph and W3C’s RDF, along with their respective query languages, Apache TinkerPop Gremlin and SPARQL.

What is a Graph Database?

A graph, in the context of database systems, is a structure consisting of nodes (also known as vertices) and edges (links or relationships). Each node represents an entity, such as a person, place, thing, or concept, while edges depict the relationships or interactions between these entities. This flexible and intuitive model mirrors the interconnected nature of data in real-world applications, from social networks where users are connected by friendships to business ecosystems where transactions link customers, products, and vendors. Graphs excel in scenarios where relationships are as critical as the data points themselves, enabling queries that explore the depth and breadth of connections to uncover patterns, insights, and opportunities hidden within the data. By prioritizing relationships, graph databases like Amazon Neptune facilitate a deeper analysis of data relationships, making them indispensable tools for applications that rely on the intricate interplay between diverse data elements.

How Amazon Neptune Works

Amazon Neptune is designed to offer a robust, scalable solution for managing graph data. At its core, Neptune stores and processes data in formats that are optimized for high connectivity and complex relationship traversals, enabling rapid query responses and data retrieval. The service employs a distributed, fault-tolerant architecture that ensures data availability and resilience, automatically replicating data across multiple Availability Zones in an AWS Region. This architecture not only facilitates high availability but also enables Neptune to provide consistent performance at scale, regardless of the size of the dataset or the complexity of the queries. By leveraging fast, parallel bulk loading from Amazon S3 , Neptune allows for efficient data ingestion, making it easier to get started with large datasets. Additionally, Neptune’s query processing engine is designed to optimize query execution, automatically translating high-level graph queries into efficient, low-level operations. This sophisticated processing capability, combined with Neptune’s support for both Gremlin and SPARQL query languages, empowers developers to build flexible, powerful applications that can navigate complex data relationships with ease.

Key Features and Benefits of Amazon Neptune

High performance and scalability.

Neptune is engineered from the ground up for high performance and is capable of handling over 100,000 graph queries per second. It supports automatic scaling, allowing for up to 15 read replicas across three Availability Zones to extend read capacity and maintain low-latency access to data.

Fully Managed Service

As a fully managed service, Neptune abstracts away the complexities of hardware provisioning, software patching, and setup, enabling developers to focus on building their applications. AWS takes care of the operational burden, offering a seamless experience for managing graph databases.

Security and Compliance

Hosted within Amazon Virtual Private Cloud (VPC), Neptune ensures data isolation and secure connections. It integrates with AWS Identity and Access Management (IAM) for access control and supports encryption at rest and in transit, meeting the stringent security and compliance requirements of various industries.

Seamless Integration with AWS Ecosystem

Neptune is designed to work seamlessly with the broader AWS ecosystem, including services like Amazon S3 for data storage, AWS Lambda for executing code in response to triggers, and Amazon Kinesis for real-time data streaming. This integration enables developers to build comprehensive, cloud-native applications that leverage the strengths of AWS services.

Serverless and Global Database Capabilities

Neptune offers a serverless option, eliminating the need to manage database capacity and allowing you to pay only for the resources your application consumes. Additionally, the Neptune Global Database feature enables the deployment of a single graph database across multiple AWS Regions , reducing latency and improving disaster recovery capabilities.

Machine Learning Integration

With Neptune ML, developers can enhance their applications with machine learning capabilities. Powered by Amazon SageMaker , Neptune ML automates the heavy lifting involved in selecting, training, and optimizing machine learning models, making it easier to generate predictions directly from graph data.

Multi-Model Support

Neptune supports multiple graph models, including Property Graph and RDF (Resource Description Framework), along with their respective query languages, Apache TinkerPop Gremlin and SPARQL. This multi-model support allows developers to choose the most appropriate data model for their specific application needs, whether they’re working with highly connected data sets or leveraging semantic web data.

Continuous Backup and Point-in-Time Recovery

Neptune provides continuous backup to Amazon S3, enabling point-in-time recovery for your databases. This feature ensures data durability and recoverability, allowing you to restore your database to any second within the backup retention period, which can be crucial for maintaining data integrity in the event of accidental deletions or database corruption.

These key features make Amazon Neptune a powerful and versatile choice for developers and organizations looking to harness the power of graph databases to manage complex and highly connected data sets efficiently.

Use Cases for Amazon Neptune

Amazon Neptune’s versatility shines across various domains, demonstrating its capability to handle complex, highly connected datasets with ease. Here’s an expanded look at its use cases, incorporating insights from the additional resources.

Building Identity Graphs

Identity graphs play a crucial role in understanding customer behavior across multiple platforms and devices. Amazon Neptune facilitates the creation of comprehensive identity graphs by linking customer identifiers such as devices, email addresses, and social media profiles. This unified view enables personalized marketing strategies and targeted advertising, enhancing customer engagement and conversion rates. By leveraging Neptune, organizations can achieve a 360° view of their customers, understanding their journey and preferences in unprecedented detail. This capability is particularly beneficial for advertising technology companies, marketing agencies, and businesses aiming to enhance customer data platforms with a focus on privacy regulation compliance.

Enhancing Fraud Detection Mechanisms

Fraud detection is a critical concern for businesses worldwide, with organizations losing billions annually to fraudulent activities. Neptune’s graph database structure is ideally suited for uncovering complex fraud schemes, including those perpetrated by sophisticated fraud rings. By visualizing the relationships between transactions, accounts, and other entities, Neptune enables real-time detection of fraudulent patterns. This approach is not only more efficient but also more effective than traditional methods, allowing for the proactive prevention of fraud across various sectors, including banking, insurance, and e-commerce.

Leveraging Machine Learning for Graph Data

Neptune ML revolutionizes the way predictions are made using graph data. By employing graph neural networks (GNNs), Neptune ML significantly enhances the accuracy of predictions, surpassing traditional machine learning methods that struggle with the complex relationships inherent in graph data. This capability is crucial for applications ranging from recommendation systems to fraud detection, where understanding the nuanced connections between data points can lead to more informed and accurate outcomes. Neptune ML democratizes access to advanced machine learning techniques, enabling developers to deploy sophisticated models without deep expertise in data science.

Securing IT Infrastructure with Security Graphs

Security graphs represent a novel approach to managing and enhancing the security of IT infrastructure. By mapping out the relationships between assets, users, and access permissions, Neptune helps organizations identify vulnerabilities and potential attack vectors within their systems. This comprehensive view is invaluable for proactive threat detection, incident response, and compliance with security policies. Security graphs are particularly effective in layered security environments, where understanding the interplay between different security measures can reveal gaps and redundancies. With Neptune, organizations can model their security posture as a graph, enabling more effective defense strategies against both internal and external threats.

Getting Started with Amazon Neptune

Creating and managing a neptune database.

Setting up a Neptune database involves a few straightforward steps, starting with the AWS Management Console . Users can configure their database instances, including security settings and network access, and begin loading data to start building their graph applications.

Querying Data with Gremlin and SPARQL

Neptune supports two powerful query languages: Gremlin for Property Graphs and SPARQL for RDF models. These languages enable developers to efficiently query and manipulate highly connected data, uncovering insights that would be challenging to obtain with traditional databases.

Integrating Neptune with AWS Services

Neptune’s value is further amplified when integrated with other AWS services, such as Amazon S3 for data storage, AWS Lambda for serverless computing, and Amazon SageMaker for machine learning. These integrations allow for building sophisticated, scalable applications that leverage the full power of AWS’s cloud ecosystem.

Best Practices for Using Amazon Neptune

Adhering to best practices is crucial to maximize the benefits of Amazon Neptune in managing highly connected datasets. These guidelines not only ensure optimal performance but also enhance the security, scalability, and maintainability of your graph database applications.

Optimize Data Modeling

Data modeling is a critical step in the effective use of Neptune. Design your graph model to reflect the queries you anticipate running. This involves structuring nodes, edges, and properties in a way that aligns with your application’s access patterns. For Property Graphs, use labels and property keys efficiently to facilitate fast retrieval. For RDF graphs, leverage named graphs and efficient IRI (Internationalized Resource Identifiers) strategies to categorize and access your data effectively.

Utilize Indexing Strategically

While Neptune automatically manages indexing, understanding how your queries interact with these indexes can lead to performance improvements. For Gremlin, ensure that your queries are using property-based filtering early in the traversal to take advantage of Neptune’s indexing. In SPARQL queries, use FILTER clauses judiciously and consider graph-specific optimizations for faster query execution.

Manage Connections Wisely

Connection management is pivotal in ensuring the high availability and responsiveness of your Neptune database. Implement connection pooling to reduce the overhead of establishing connections to Neptune. This is particularly important for applications with high request rates. Additionally, monitor your connection usage and adjust your pool size based on the workload to avoid throttling.

Scale Effectively

Leverage Neptune’s scalability features to accommodate your application’s growth. Use read replicas to distribute query load and enhance read throughput, especially for read-intensive applications. Monitor your database’s performance metrics through Amazon CloudWatch to identify when scaling is needed, whether it’s adding more replicas or resizing your instances.

Ensure Data Security

Data security in Neptune involves multiple layers, including network security, access control, and data encryption. Use Amazon VPC to isolate your database and control access with security groups. Implement fine-grained access control with IAM policies and database authentication. Enable encryption at rest and in transit to protect your data from unauthorized access.

Backup and Recovery

Regularly back up your Neptune database to safeguard against data loss. Neptune’s continuous backup feature allows for point-in-time recovery, enabling you to restore your database to any second within the backup retention period. Test your backup and recovery procedures to ensure they meet your business continuity requirements.

Monitor and Audit

Continuous monitoring and auditing are essential for maintaining the health and security of your Neptune database. Use Amazon CloudWatch to track operational metrics and set alarms for anomalous activity. Enable logging and use AWS CloudTrail for auditing access and changes to your Neptune environment, helping you comply with regulatory requirements and internal policies.

Leverage Neptune Features and AWS Ecosystem

Take full advantage of Neptune’s features, such as the Gremlin and SPARQL query languages, to efficiently interact with your graph data. Integrate Neptune with other AWS services like Amazon SageMaker for machine learning, AWS Lambda for serverless computing, and Amazon Elasticsearch Service for advanced search capabilities to build comprehensive, cloud-native applications.

By following these best practices, you can optimize your use of Amazon Neptune, ensuring that your graph database applications are secure, scalable, and performant. This will enable you to derive maximum value from your highly connected datasets, driving insights and innovation in your organization.

Amazon Neptune represents a significant advancement in managing highly connected data, offering unparalleled performance, scalability, and ease of use. Whether for driving recommendation engines, enhancing fraud detection, or building comprehensive knowledge graphs, Neptune provides a robust, secure, and fully managed graph database solution. As businesses continue to navigate the complexities of modern data landscapes, Neptune stands ready to support their most challenging graph database needs.

Additional Resources

• Amazon Neptune Pricing (Understand the cost structure of Amazon Neptune and how pricing works for different configurations and usage patterns.)
• Amazon Neptune FAQs (Find answers to frequently asked questions about Amazon Neptune, covering a wide range of topics from features to management.)
• AWS Learning Path: Getting Started with Amazon Neptune (A learning resource for beginners to get started with Amazon Neptune, including tutorials and best practices.)
• Bulk Load Data to Neptune – Gremlin Format (Learn how to prepare and bulk load data into Neptune using the Gremlin format for graph data.)
• Accessing Neptune Graphs using Gremlin (A guide on how to access and query your graph data in Neptune using the Gremlin query language.)
• Bulk Load Data to Neptune – openCypher Format (Instructions on how to bulk load data into Neptune using the openCypher format, suitable for property graph models.)
• Accessing Neptune Graphs using openCypher (Explore how to use the openCypher query language to access and interact with your graph data in Neptune.)
• Bulk Load Data to Neptune – RDF Format (Discover how to bulk load RDF formatted data into Neptune, enabling efficient data ingestion for RDF graph models.)
• Accessing Neptune Graphs using SPARQL (A comprehensive guide to querying RDF graph data in Neptune using the SPARQL query language.)

These resources provide valuable information and practical guidance for anyone looking to leverage Amazon Neptune for graph database solutions, from beginners to advanced users.

Ready to get started with Amazon Neptune?

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Siemens use cases with metaphactory and Amazon Neptune presented at AWS re:Invent 2017

CASE STUDY - METAPHACTORY & AMAZON NEPTUNE

Siemens and metaphacts were among the first Amazon Neptune users and showed several use cases for enterprise knowledge graphs using the new service and metaphactory .

In a joint presentation on Dec 1, 2017 at AWS re:Invent , Siemens and metaphacts provided details on how they use Amazon Neptune, standalone graph stores, and the metaphactory platform to drive use cases for enterprise knowledge graphs. Leveraging this combined solution, Siemens powers an intelligent knowledge hub combining structured and unstructured data from different sources and business units in a unified data space. This knowledge hub can also be used as a smart knowledge factory generating new knowledge.

"We are looking forward to investigate Amazon Neptune in depth and we are glad to have a partner in metaphacts which understands our data management needs and has supported us in driving knowledge graph use cases for our business and for our customers", says Thomas Hubauer, Portfolio Project Manager for Knowledge Graph & Semantics at Siemens Corporate Technology.

Building digital twin

Gas turbine maintenance

Building automation

Factory monitoring

Press Release

Siemens and metaphacts showcase Enterprise Knowledge Graphs on Amazon Neptune »

Recorded session from AWS re:Invent 2017 »

Amazon Neptune

Amazon Neptune lets you build interactive graph applications that can query billions of connections in milliseconds. Complexity and difficulty in tuning SQL queries for heavily connected data are two of the most common drawbacks of this technology. Apache TinkerPop Gremlin and W3C’s SPARQL are two prominent graph query languages that you may use with Amazon Neptune instead to execute powerful searches on related data.

Using Amazon Neptune , you are able to construct interactive graph applications to query billions of connections in a matter of milliseconds. The most typical disadvantages of utilising this technology include its complexity as well as the difficulty in fine-tuning SQL searches for data that is densely related. You may run powerful searches on related data by using Amazon Neptune and one of the main graph query languages, such as Apache TinkerPop Gremlin or W3C’s SPARQL. Both of these languages are available from the W3C. When you accomplish this, your code will become simpler, and the creation of relationship-processing apps will move forward more quickly. It is anticipated that Amazon Neptune would achieve availability rates greater than 99.99 percent by combining the database engine with an SSD-backed virtualized storage layer that has been adapted specifically for database workloads. The self-healing Neptune storage is fault tolerant, and it is possible to fix disc faults in the background without having an effect on the availability of the database. The automatic identification and restart of Neptune’s databases are designed to eliminate the need for crash recovery or a full database cache rebuild in the event that either of these processes become necessary. In the event that the entire instance crashes, Neptune is designed to automatically fail over to one of up to 15 read replicas. Launching an Amazon Neptune database instance is a simple process that can be accomplished in a matter of seconds using the Neptune Management Console. Neptune automatically scales storage to provide constant performance by increasing storage capacity and rebalancing input/output operations.

Amazon Neptune is not built on a relational database. Instead, it is a high-performance graph database engine that has been specifically designed for Amazon’s platform. Neptune is optimized for storing and querying complex graph data efficiently. It utilizes a scale-up architecture that is optimized for in-memory operations, allowing it to provide quick query evaluation capabilities even for large graphs.

How it Works

Effortless and Expandable

• Queries on graphs can benefit from high throughput and low latency.

It was built exclusively for Amazon and is a high-performance graph database engine To quickly evaluate queries over large graphs, Neptune uses a scale-up, in-memory optimised architecture, which effectively stores and navigates graph data. Neptune can be used with Gremlin or SPARQL to conduct fast and simple queries.

• Database Compute Resources can be easily scaled.

When scaling up or down your production cluster, you may do it with only a few clicks in the AWS Management Console. In most cases, scaling activities are completed within a few seconds.

• Automated Storage Scalability

Amazon Neptune will automatically raise the volume of your database as your storage needs grow. You have the option of using up to 64 terabytes of storage space. There’s no need to add more database storage to keep up with future expansion.

In addition to the automatic adjustment of database volume, it is worth noting that the minimum storage capacity for an Amazon Neptune database is 10GB. This ensures that you have a solid starting point for your storage needs. From there, as your usage increases, the database storage will seamlessly scale up in 10GB increments. This scalability allows your Amazon Neptune storage to grow organically without any impact on database performance.

With a maximum storage limit of 64TB, you can rest assured that your database can handle extensive data storage requirements. The incremental growth in storage capacity is designed to accommodate your evolving needs, eliminating the need for proactive planning or adding more storage space manually.

• Read Replicas with Low Latency.

Increase read throughput by creating up to 15 database read replicas for high-volume application requests. Amazon Neptune replicas offer cost savings and the avoidance of writing to replica nodes. The latency time for read requests can be reduced to single-digit milliseconds on numerous occasions by using more processing power. Applications don’t have to worry about keeping track of new and removed copies because Neptune only has one endpoint for read queries.

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Quality and Reliability

• Incident Management as well as Repair.

Your Amazon Neptune database and underlying EC2 instance are always in good shape, so you can relax. The instance that runs your database is responsible for restarting it and any associated processes. Because the database redo logs do not need to be replayed, instance restart times with Neptune recovery are often under 30 seconds. It also shields the database buffer cache from the processes of the database, allowing it to withstand a database restart.

• Using Read Replicas in Multi-AZ Deployments

In the event of an instance failure, Neptune will immediately transition to one of up to 15 Neptune replicas located in any of three Availability Zones. If there are no Neptune replicas, Neptune will attempt to automatically construct a new database instance in the event of a failure.

• Self-healing and fault-tolerant Storage

Replication occurs six times across three availability zones for every 10GB of database storage. Amazon Neptune’s fault-tolerant storage can lose up to two copies of data without affecting database read availability in order to ensure database write availability. Neptune’s storage is self-healing for all of its data blocks and discs.

• Point-in-time recovery and background incremental backups.

To recover your instance, you can use Amazon Neptune’s backup tool. You can use this method to recover your database up to the last five minutes of its retention period. You can specify a retention time for your automated backups of up to 35 days. Amazon S3, a service meant to guarantee 99.999999999 percent uptime, is used to store automated backups. The Neptune backups have no effect on database performance.

• Snapshots of the database

In Amazon S3, database snapshots are replicas of your instance that are created on demand and kept until they are permanently removed. For time and space savings, they use automated incremental snapshots. You can utilise a Database Snapshot at any time to create a new instance of the database.

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APIs for the Open Graph

• Gremlin supports Apache TinkerPop’s Property Graph

Neptune offers robust support for querying with two powerful languages: Gremlin for Property Graphs and SPARQL for RDF models. Property graphs have recently gained popularity due to their familiarity with relational models, and Gremlin traversal language simplifies the exploration of these graphs. Amazon Neptune backs the Property Graph concept and provides a Gremlin Websockets server compatible with the latest TinkerPop version. Additionally, Neptune, an Amazon service, boasts comprehensive backup and restore functionality for databases, allowing users to easily build Gremlin traversals over property graphs by modifying the Gremlin service configuration for seamless integration into existing applications.

When managing highly connected datasets using Amazon Neptune, it is essential to consider a range of best practices to ensure optimal performance, security, and scalability. Data modeling plays a crucial role in the effective utilization of Neptune. By designing your graph model to align with anticipated queries and structuring nodes, edges, and properties efficiently, you can enhance the accessibility and retrieval speed of your data. Additionally, understanding and utilizing indexing strategically can lead to significant performance improvements. Whether it’s Gremlin queries or SPARQL queries, optimizing index usage can result in faster query execution and improved overall efficiency.

To ensure the security of your databases, Neptune allows you to produce and manage encryption keys using the AWS Key Management Service (KMS). By encrypting your Neptune databases, you can safeguard your data stored in the underlying storage and automated backups, snapshots, or replicas within the same cluster.

Regarding data security, Neptune offers robust security features, including network isolation with Amazon VPC and encrypted IPsec VPNs. You can safeguard your data from unauthorized access by implementing fine-grained access control with IAM policies and database authentication. Regularly backing up your Neptune database and testing recovery procedures are essential steps to protect against data loss and ensure business continuity.

Stay informed about critical database events through email and SMS alerts. Neptune allows subscribing to database events, such as automated failovers, through the intuitive AWS Management Console. This proactive approach keeps you updated and enables prompt actions in response to any database issues.

• W3C’s RDF 1.1 and SPARQL specifications are supported.

Neptune extends its support to W3C’s RDF 1.1 and SPARQL specifications. RDF is widely accepted in complex information domains, and Neptune enables users to access RDF datasets like Wikidata and PubChem through its SPARQL endpoint. Leveraging an HTTP REST interface, developers can effortlessly integrate Neptune’s RDF and SPARQL support into both new and existing graph applications.

Exceptionally Secure

• Isolation of the Network

Security in AWS Neptune is of paramount importance, and Amazon Web Services (AWS) offers a range of robust security features to protect your data. One key aspect of security is the isolation of the network, which can be achieved by utilizing Amazon Virtual Private Cloud (VPC) and encrypted IPsec VPNs. This allows you to connect to Amazon Neptune within your own virtual network, ensuring that your database remains segregated from other resources.

Another crucial security measure is managing permissions at the resource level. With Amazon Neptune, you have the ability to govern access to specific resources such as database snapshots and parameter groups. This enables you to control the actions that AWS Identity and Access Management (IAM) users and groups can perform on these resources. By tagging your Neptune resources, you can further refine permissions based on specific criteria. For instance, you can restrict changes or deletions of “Production” database instances to only Database Administrators, while allowing developers to modify “Development” instances, all through well-defined IAM rules.

Encryption is a vital component of database security, and Amazon Neptune provides robust encryption features. You can generate and manage encryption keys through AWS Key Management Service (KMS) to safeguard your databases. This ensures that not only the data stored in the underlying storage, but also automated backups, snapshots, and replicas within the same cluster, are protected with Neptune-encrypted database instances.

• Permissions at the Resource Level

This dynamic partnership enables Neptune to leverage certain management features that ensure optimal performance and security. For instance, Neptune benefits from Amazon RDS’s expertise in instance lifecycle management, allowing seamless management and scaling of Neptune instances. Additionally, Neptune leverages encryption-at-rest capabilities with Amazon Key Management Service (KMS) keys, providing robust data protection. Moreover, Neptune takes advantage of Amazon RDS’s security groups management, ensuring that access to Neptune instances is precisely controlled and secured.

Just remember Amazon RDS permissions and resources are required to use Amazon Neptune because Neptune leverages operational technology from Amazon RDS, including instance lifecycle management, encryption-at-rest using Amazon Key Management Service (KMS) keys, and security groups management. By utilizing the capabilities of Amazon RDS, Neptune can provide high-performance graph database services that are specifically designed for use on the Amazon platform.

It is possible to produce and manage the encryption keys used by Amazon Neptune to safeguard your databases using AWS Key Management Service (KMS). To ensure that any data stored in the underlying storage as well as automated backups, snapshots or replicas in the same cluster is safeguarded by Neptune-encrypted database instances.

Recording database events with Amazon Neptune has minimal impact on database performance. Studying logs now can help with database management, security, governance, and regulatory compliance issues in the future In addition, you can transmit audit logs to the Amazon CloudWatch service to keep track of what’s happening in your environment.

Completely Organized

• Simple to Use

It’s easy to use Amazon Neptune. Create a new Neptune database instance using the AWS Management Console. The database instance class you supplied is pre-configured in Neptune database instances. You may create a database and attach it to your app in just a few minutes. It is possible to fine-tune your database by using Database Parameter Groups.

• Operate easily

Amazon Neptune is a powerful and user-friendly graph database solution. It simplifies the process of building and managing high-performance graph databases. With Neptune’s API, you can create graph databases without the need for specific graph indexes, making it easier to work with your data. The API also provides a timeout and memory limit feature, ensuring that queries do not consume excessive memory or time out. To monitor the performance of your Neptune database instances, Amazon CloudWatch is used. This allows you to keep track of essential operational indicators such as CPU utilization, RAM usage, storage, query performance, and active connections. The AWS Management Console provides a convenient interface where you can access and analyze these metrics for your database instances running on Amazon Web Services.

Alternatively, users can quickly clone multi-terabyte database clusters. This cloning capability is beneficial for various purposes, including application development, testing, database upgrades, and analytical queries. By having immediate access to cloned data, you can efficiently perform software development tasks, updates, and analytics. The process of cloning an Amazon Neptune database is straightforward and can be done with just a few clicks in the Management Console. The clone is replicated across three Availability Zones, ensuring redundancy and high availability.

• Measuring and metric

Amazon Neptune uses Amazon CloudWatch to keep tabs on your database instances. The AWS Management Console displays over 20 critical database operational indicators, such as CPU, RAM, storage, query performance, and active connections, for database instances running on Amazon Web Services’s cloud platform.

• Auto-Patching

In order to maintain your database patched, use Amazon Neptune. It is possible to control patching with Database Engine Version Management (DEV).

• Database Event Alerts

Email and SMS alerts can be used to notify users of critical database events, such as an automated failover. Subscribing to database events can be done through AWS Management Console.

• Database Cloning

Neptune from Amazon allows for multi-terabyte database clusters to be quickly cloned in minutes. Application development, testing, database upgrades, and analytical queries can all benefit from cloning. Having immediate access to data improves software development and updates, as well as analytics.

You can clone an Amazon Neptune database with a few mouse clicks in the Management Console. Three Availability Zones are used to reproduce the clone.

Rapid Bulk Data Loading

• Bagging of Property Graphs

Amazon Neptune’s bulk loading capability makes it easy to load large amounts of data from S3. A REST interface is used to do this. CSV delimiter format is used to load data into the nodes and edges of the graph. Neptune Property Graph bulk loading has more information.

• RDF Bulk Load

It is possible to load RDF data stored in S3 using Amazon Neptune in a fast and efficient manner. A REST interface is used to do this. N-Triples (NT), N-Quads (NQ), RDF/XML, and Turtle RDF are all supported serialisations. Neptune RDF bulk loading documentation can be found here.

General Use Cases

1. Social Networking Neptune can be utilized to build social networking applications by efficiently processing large sets of user-profiles and interactions. It enables prioritizing the display of recent updates from family, friends, and user-liked content, enhancing the user experience.

2. Recommendation Engines With Neptune’s graph database capabilities, relationships between different information can be stored and queried swiftly. This functionality facilitates personalized and relevant recommendations, such as suggesting products to users based on their interests, friends’ preferences, and purchase history.

3. Fraud Detection Neptune’s real-time processing capabilities make it an excellent choice for fraud detection applications. By analyzing financial and purchase transactions, Neptune can identify patterns indicative of fraud, such as multiple people sharing the same IP address but residing at different physical addresses or multiple individuals linked to a single email address.

4. Knowledge Graphs Neptune allows the creation of knowledge graphs, which store information in a graph model and enable graph-based queries. This feature proves valuable for helping users navigate highly interconnected datasets. For example, in an art-related knowledge graph, users interested in one artwork can discover related works by the same artist or explore other pieces in the same museum.

5. Life Sciences In the life sciences field, Neptune provides a secure and efficient means of storing and querying information. It supports encryption at rest for sensitive data and can be used to model and search for relationships within biological and genetic data. For instance, Neptune can help identify potential gene-disease associations by analyzing protein pathways.

6. Network/IT Operations Neptune can be applied to network and IT operations for rapid analysis and understanding of anomalous events. By querying for graph patterns using event attributes, Neptune enables efficient identification and tracing of the root cause of issues. For example, if a malicious file is detected on a host, Neptune can assist in tracking its origin and propagation within the network.

Cost-Effectiveness

Pay Only for the Services You Utilize

Amazon Neptune pricing is designed to offer cost-effective solutions for your database needs. With Neptune, you eliminate the need for a substantial upfront expenditure, paying an hourly charge for each instance you start. This means you can create and stop instances as needed without incurring unnecessary costs. Additionally, you only pay for the storage you utilize, making it a cost-efficient option for managing your data.

When it comes to billing, Amazon Neptune ensures transparency and flexibility. No long-term commitments or upfront payments are required, allowing you to pay per hour for On-Demand instances. Discounts are available on read-write primary instances and Amazon Neptune replicas, which are utilized to boost reads and enhance failover. In terms of performance, Amazon Neptune stands out with its support for 15 read replicas, allowing for increased scalability and fault tolerance. Additionally, the database can handle 100,000s of queries per second, ensuring high throughput for demanding workloads. Neptune database storage charges are billed in GB-month increments, while I/O charges are billed in million-request increments. Remember, apart from the storage and I/Os you consume, there is no need to allocate resources in advance when using Neptune.

Storage charges are billed in GB-month increments, and I/O charges are billed in million-request increments, ensuring that you are only charged for the resources you use. Any database cluster snapshots and automated database backups customers request incur a per GB-month charge for backup storage. Data transfer charges are based on the volume of data sent in and out of the planet, providing a clear understanding of your data transfer costs.

In the Ready State, the Amazon Neptune Workbench charges by the instance hour and allows you to interact with your Neptune cluster using Jupyter notebooks hosted by Amazon SageMaker. With Amazon Neptune, you have the flexibility to scale your resources based on your needs, ensuring cost-effectiveness and efficiency in managing your database workloads

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Introduction to Amazon Neptune and its use cases

In this recipe, we will learn about Amazon Neptune. We will also learn about the use cases of Amazon NEptune. Last Updated: 08 Aug 2022

Recipe Objective - Introduction to Amazon Neptune and its use cases?

The Amazon Neptune is a widely used service and is defined as a fully managed graph database service that makes it simple to create and run applications that work with large, interconnected datasets. Amazon Neptune is powered by a purpose-built, high-performance graph database engine that can store billions of relationships and query them in milliseconds. Amazon Neptune supports the popular graph models Property Graph and W3C's RDF, as well as their query languages Apache TinkerPop Gremlin and SPARQL, making it simple to create queries that efficiently navigate highly connected datasets. Recommendation engines, fraud detection, knowledge graphs, drug discovery, and network security are just a few of the graph use cases that Neptune powers. With read replicas, point-in-time recovery, continuous backup to Amazon S3, and replication across Availability Zones, Amazon Neptune is highly available. With support for HTTPS encrypted client connections and encryption at rest, Neptune is safe. Users no longer have to worry about database management tasks like hardware provisioning, software patching, setup, configuration, or backups because Neptune is fully managed. Users don't have to worry about database management tasks like hardware provisioning, software patching, setup, configuration, or backups with Amazon Neptune. Neptune monitors and backs up its database to Amazon S3 in real-time, allowing for granular point-in-time recovery. Amazon CloudWatch can be used to track database performance.

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Benefits of Amazon Neptune

• Both Gremlin and SPARQL have open graph APIs, and Amazon Neptune provides high performance for both graph models and query languages. It allows users to choose between the Property Graph model and Apache TinkerPop Gremlin, an open source query language, and the W3C standard Resource Description Framework (RDF) model and SPARQL, a standard query language and thus it supports Open graph APIs. Amazon Neptune is a high-performance graph database designed specifically for Amazon. It is designed to handle graph queries. To scale read capacity and execute more than 100,000 graph queries per second, Neptune supports up to 15 low latency read replicas spread across three Availability Zones. As users' needs change, users can easily scale their database deployment from smaller to larger instance types and thus it offers high performance and scalability. Amazon Neptune is highly available, long-lasting, and compliant with the ACID (Atomicity, Consistency, Isolation, and Durability) standards. Neptune is designed to have a 99.99 per cent availability rate. It has fault-tolerant and self-healing cloud storage with six copies of users' data replicated across three Availability Zones. Neptune automatically backs up users' data to Amazon S3 and recovers from physical storage failures in real-time. Instance failover in High Availability typically takes less than 30 seconds and thus it offers high availability and durability. For the user's database, Amazon Neptune provides multiple levels of security, including network isolation via Amazon VPC, support for IAM authentication for endpoint access, HTTPS encrypted client connections, and encryption at rest via Amazon Key Management Service keys users create and control (KMS). Data in the underlying storage, as well as automated backups, snapshots, and replicas in the same cluster, are all encrypted on an encrypted Neptune instance and thus offer security.

System Requirements

• Any Operating System(Mac, Windows, Linux)

This recipe explains Amazon Neptune and the Use cases of Amazon Neptune.

Use cases of Amazon Neptune

• It has a use case in social networking

To create social networking applications, Amazon Neptune can quickly and easily process large sets of user-profiles and interactions. Neptune allows users to integrate social features into your apps by allowing highly interactive graph queries with high throughput. If users are creating a social feed for their app, for example, users can use Neptune to prioritise showing their users the most recent updates from their family, friends whose updates they like,' and friends who live close to them.

• It has a use case for Recommendation engine

Amazon Neptune lets users store relationships between data points like customer interests, friends, and purchase history in a graph and query it quickly to generate personalised and relevant recommendations. For example, users can use Neptune to make product recommendations to a user based on which products have been purchased by others who follow the same sport and have similar purchase histories. Alternatively, users can find people who share a friend but haven't met yet and make a friendship recommendation.

• It has a use case for fraud detection

Users can use Amazon Neptune to process financial and purchase transactions in near real-time, making it easy to spot fraud patterns. Neptune offers a fully managed service that runs fast graph queries to see if a potential buyer is using the same email address and credit card as a previously reported fraud case. If users are working on a retail fraud detection app, Neptune can help them create graph queries to quickly spot patterns like multiple people using the same personal email address or multiple people using the same IP address but living at different physical addresses.

• It has a use case for knowledge graphs

Amazon Neptune aids in the development of knowledge graph applications. A knowledge graph allows users to store data in a graph model and use graph queries to help users navigate large, interconnected datasets. Neptune supports open source and opens standard APIs, allowing users to quickly build knowledge graphs and host them on a fully managed service by leveraging existing information resources. If a user is interested in The Mona Lisa, for example, users can direct them to other works by Leonardo da Vinci or other works of art in The Louvre.

• It has a use case in Life sciences

Amazon Neptune enables users to create life sciences applications that store and navigate data, as well as process sensitive data with ease using encryption at rest. Neptune, for example, can be used to store disease models and gene interactions, as well as to search for graph patterns within protein pathways to discover other genes that may be linked to a disease. Chemical compounds can be represented as graphs, and patterns in molecular structures can be searched for. Neptune can also assist you in integrating data to solve problems in healthcare and life sciences research. Neptune can be used to create and store data across multiple systems, as well as to organise research publications by topic to find relevant information quickly.

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AWS Announces General Availability of Amazon Neptune

Amazon Neptune, a fast and reliable graph database, makes it easy for customers to build applications on highly connected datasets

Thousands of customers, including Samsung Electronics, Pearson, Intuit, Siemens, AstraZeneca, FINRA, LifeOmic, Blackfynn, and Amazon Alexa, participated in the preview, building new graph applications and battle-testing their production workloads

SEATTLE--(BUSINESS WIRE)--May 30, 2018-- Today, Amazon Web Services, Inc. (AWS), an Amazon.com company (NASDAQ: AMZN), announced general availability of Amazon Neptune, a fast, reliable, and fully managed graph database service. Amazon Neptune efficiently stores and navigates highly connected data, allowing developers to create sophisticated, interactive graph applications that can query billions of relationships with millisecond latency. In the preview, customers used Neptune to build social networks, recommendation engines, fraud detection, knowledge graphs, drug discovery applications, and more. With Amazon Neptune there are no upfront costs, licenses, or commitments required; customers pay only for the Neptune resources they use. To get started with Amazon Neptune, visit  https://aws.amazon.com/neptune .

For many years, the relational database was the only option available to application developers. While relational databases are great for applications that need referential integrity for highly structured data, today’s developers need a variety of databases to serve the needs of modern applications. Just as some modern applications need to store petabytes of unstructured data, access it with sub-millisecond latency, process millions of requests per second, and scale to support millions of users all around the world, others need to quickly understand and navigate billions of relationships between highly connected data. Applications (e.g. social applications, recommendation engines, and fraud detection) that try to navigate these relationships using SQL queries require join operations that quickly become complex, error prone, and slow to execute. Graph databases are becoming more attractive because they store the relationships between highly connected data as graphs, enabling applications to quickly query these relationships. However, many of today’s open source or community edition graph databases can’t offer the performance, reliability or scalability of commercial databases, while commercial options are often expensive, or complex to set up and maintain. Now, with Amazon Neptune, developers can query connected datasets with the speed and simplicity of a graph database, while benefiting from the scalability, security, durability, and availability of an AWS managed graph database service. The Amazon Neptune query processing engine is optimized for both of the leading graph models, Property Graph and W3C's RDF, and their associated query languages, Apache TinkerPop Gremlin and RDF/SPARQL, providing customers the flexibility to choose the right approach based on their specific graph use case. And, as a customer’s data increases, Neptune storage scales automatically, without downtime or performance degradation.

“The days of modern technology companies using relational databases for all of their workloads have come and gone,” said Raju Gulabani, Vice President, Databases, Analytics, and Machine Learning at Amazon Web Services, Inc. “As the world has become more connected, applications that navigate large, connected datasets are increasingly more critical for customers. We are delighted to give customers a high-performance graph database service that enables developers to query billions of relationships in milliseconds using standard APIs, making it easy to build and run applications that work with highly connected datasets.”

Amazon Neptune is highly available and durable, automatically replicating six copies of data across three Availability Zones and continuously backing up data to Amazon Simple Storage Service (Amazon S3). Amazon Neptune is designed to offer greater than 99.99 percent availability and automatically detects and recovers from most database failures in less than 30 seconds. Amazon Neptune also provides advanced security capabilities, including network security through Amazon Virtual Private Cloud (VPC), and encryption at rest using AWS Key Management Service (KMS).

Pearson, the world’s learning company, helps more than 75 million people each year embrace their passion and ambition for learning new skills. “Our goal is to significantly reduce complexity in several of our key systems and to standardize how our organization approaches heavily connected, graph data,” said Josh Walton, Senior Solutions Architect of Pearson. “We’ve long wanted to use a graph database, but until we evaluated Neptune, nothing fit our needs due to performance or cost. We tried Neptune during the preview and the promise it shows is exciting. Our performance test results have been exceptional and the pricing is right. We expect to be able to simplify our existing architecture and maintain current SLAs while also gaining the ability to scale beyond our current capacity, and speed up development time with all the packaged, fully managed goodies that come along with AWS managed services.”

Blackfynn is a life sciences software startup formed to change how Epilepsy, Alzheimer’s disease, Parkinson’s disease, ALS, and other neurological diseases are treated. The multidisciplinary team has deep roots in medicine, clinical development, neuroscience, engineering, data science and machine learning. “We look forward to using Amazon Neptune as an integral part of our data platform,” said Chris Baglieri, SVP of Engineering at Blackfynn. “Neptune will allow us to connect the dots between genomics, pathology, neurochemistry, device and patient clinical data, efficiently and at scale, helping us drive breakthrough discoveries.”

Intuit is powering prosperity for millions of small businesses and self-employed customers around the world. “We are exploring Amazon Neptune for heavy lifting of our Graph infrastructure management, freeing our engineering team to focus on optimizing the knowledge graph for customer-centric business problems,” said Sandeep Uttamchandani, Chief Data Architect, Intuit Small Business Group and Self-Employed Group. “Neptune is promising as a secure and highly available Graph database available out of the box!”

FINRA regulates a critical part of the securities industry – brokerage firms doing business with the public in the United States. FINRA takes in up to 100 billion market events per day that are tracked, aggregated, and analyzed for the purpose of protecting investors. “FINRA is in the process of migrating most of our databases to AWS,” said Saman Michael Far, Senior Vice President, FINRA. “We have evaluated Amazon Neptune, and we look forward to beginning to use it for our graph workloads.”

LifeOmic leverages the cloud, machine learning and mobile devices to enable precision medicine for healthcare providers, researchers, health IT companies and patients. LifeOmic’s JupiterOne is a DevSecOps solution built in the cloud, for the cloud to automate and simplify security and compliance such as HIPAA and HITRUST. “Amazon Neptune has accelerated the development of our JupiterOne DevSecOps software while reducing our operational costs, enabling us to bring enterprise grade security solutions to companies of all sizes,” said Erkang Zheng, Chief Information Security Officer at LifeOmic. “Security operations, analytics and compliance fundamentally boil down to the ability to ask the right questions and get the right answers quickly, but the speed of DevOps and the ephemeral nature of cloud make this difficult. With Neptune, we can harness the power of graph databases without infrastructure maintenance overhead. This enables us to focus on helping organizations build solutions that simplify security and fast track compliance and certifications such as HIPAA, HITRUST and NIST.”

“Amazon Neptune is a key part of the toolkit we use to continually expand Alexa’s knowledge graph for our tens of millions of Alexa customers—it’s just Day 1 and we’re excited to continue our work with the AWS team to deliver even better experiences for our customers,” said David Hardcastle, Director of Amazon Alexa, Amazon.

Amazon Neptune is available in the US East (N. Virginia), US East (Ohio), US West (Oregon), and EU (Ireland) regions, and will expand to additional Regions in the coming year.

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Deep Learning Optimization Algorithms

Training deep learning models means solving an optimization problem: The model is incrementally adapted to minimize an objective function.

The optimizers used for training deep learning models are based on gradient descent, trying to shift the model’s weights towards the objective function’s minimum.

A range of optimization algorithms is used to train deep learning models, each aiming to address a particular shortcoming of the basic gradient descent approach.

• Stochastic Gradient Descent (SGD) and Mini-batch Gradient Descent speed up training and are suitable for larger datasets.
• AdaGrad adapts learning rates to parameters but may slow down learning over time. RMSprop and AdaDelta build on AdaGrad’s approach, addressing its diminishing learning rates, with AdaDelta removing the need for a set learning rate.
• Adam combines the advantages of AdaGrad and RMSprop and is effective across a wide range of deep-learning tasks.

Optimization algorithms play a crucial role in training deep learning models. They control how a neural network is incrementally changed to model the complex relationships encoded in the training data.

With an array of optimization algorithms available, the challenge often lies in selecting the most suitable one for your specific project. Whether you’re working on improving accuracy, reducing training time, or managing computational resources, understanding the strengths and applications of each algorithm is fundamental.

In this article, we’ll survey the most commonly used deep learning optimization algorithms, including Gradient Descent, Stochastic Gradient Descent, and the Adam optimizer. By the end of this article, you’ll have a clear idea of how to choose the best algorithm for training your models.

What is a model-optimization algorithm?

A deep learning model comprises multiple layers of interconnected neurons organized into layers. Each neuron computes an activation function on the incoming data and passes the result to the next layer. The activation functions introduce non-linearity, allowing for complex mappings between inputs and outputs.

The connection strength between neurons and their activations are parametrized by weights and biases. These parameters are iteratively adjusted during training to minimize the discrepancy between the model’s output and the desired output given by the training data. The discrepancy is quantified by a loss function.

All deep learning model optimization algorithms widely used today are based on Gradient Descent. Hence, having a good grasp of the technical and mathematical details is essential. So let’s take a look:

• Objective : Gradient Descent aims to find a function’s parameters (weights) that minimize the cost function. In the case of a deep learning model, the cost function is the average of the loss for all training samples as given by the loss function. While the loss function is a function of the model’s output and the ground truth, the cost function is a function of the model’s weights and biases.
• Initialization : Start with random values for the model’s weights.
• Gradient computation : Calculate the gradient of the cost function with respect to each parameter. The gradient is a vector that points in the direction of the steepest increase of the function. In the context of optimization, we’re interested in the negative gradient, which points towards the direction of the steepest decrease.
• Update parameters : Adjust the model’s parameters in the direction opposite to the gradient. This step is done by subtracting a fraction of the gradient from the current values of the parameters. The size of this step is determined by the learning rate, a hyperparameter that controls how fast or slow we move toward the optimal weights.
• Mathematical representation : the update rule for each parameter 𝒘 can be mathematically represented as

where w represents the model’s parameters (weights) and 𝛼 is the learning rate. Δ𝑤𝐽 (w) is the gradient of the cost function 𝐽 (w) with respect to w .

The learning rate is a crucial hyperparameter that needs to be chosen carefully. If it’s too small, the algorithm will converge very slowly. If it’s too large, the algorithm might overshoot the minimum and fail to converge.

Challenges :

• Local minima and saddle points : In complex cost landscapes, Gradient Descent can get stuck in local minima or saddle points, especially in non-convex optimization problems common in deep learning. (Over the course of the article, we’ll encounter several strategies for overcoming this problem.)
• Choosing the right learning rate : Finding an optimal learning rate requires experimentation and tuning. (Below, we’ll see how adaptive learning rate algorithms can help alleviate this issue.)

How to Visualize Deep Learning Models

Stochastic gradient descent (sgd).

Stochastic Gradient Descent (SGD) is a variant of the traditional Gradient Descent optimization algorithm that introduces randomness into the optimization process to improve convergence speed and potentially escape local minima. To understand the intuition behind SGD, we can again invoke the analogy of a hiker descending a foggy valley. If Gradient Descent represents a cautious hiker who carefully evaluates the slope around them before taking a step, Stochastic Gradient Descent is akin to a more impulsive hiker who decides their next step based only on the slope of the ground immediately beneath their feet.

This approach can lead to a quicker descent but might involve more meandering. Let’s take a closer look at the specifics of Stochastic Gradient Descent:

• Objective : like Gradient Descent, the primary goal of SGD is to minimize the cost function of a model by iteratively adjusting its parameters (weights). However, SGD aims to achieve this goal more efficiently by using only a single training example at a time to inform the update of the model’s parameters.
• Initialization : Start with a random set of parameters for the model.
• Gradient computation : Instead of calculating the gradient of the cost function over the entire training data, SGD computes the gradient based on a single randomly selected training example.
• Update parameters : Update the model’s parameters using this computed gradient. The parameters are adjusted in the direction opposite to the gradient, similar to basic Gradient Descent.
• The parameter update rule in SGD is similar to that of Gradient Descent but applies to a single example i :

Here, w represents the model’s parameters (weights), 𝛼 is the learning rate, and ∆𝘸𝘑𝘪(𝘸) is the gradient of the cost function 𝐽i (w) for the ith training example with respect to w .

• Variance : The updates can be noisy due to the reliance on a single example, potentially causing the cost function to fluctuate. As a result, the algorithm does not converge to a minimum but jumps around the cost landscape.
• Hyperparameter tuning : Correctly setting the learning rate requires experimentation.
• Efficiency : Using only one example at a time, SGD significantly reduces the computational requirements, making it faster and more scalable than Gradient Descent.
• Escape local minima : The inherent noise in SGD can help the algorithm escape shallow local minima, potentially leading to better solutions in complex cost landscapes.
• Online learning : SGD is well-suited for online learning scenarios where the model needs to update continuously as new data arrives.

Mini-batch Gradient Descent

Mini-batch Gradient Descent strikes a balance between the thorough, calculated approach of Gradient Descent and the unpredictable, swift nature of Stochastic Gradient Descent (SGD). Imagine a group of hikers navigating through a foggy valley. Each hiker independently assesses a small, distinct section of the surrounding area before the group decides on the best direction to take.

Based on a broader but still limited view of the terrain, this collective decision-making process allows for a more informed and steady progression toward the valley’s lowest point compared to an individual hiker’s erratic journey. Here’s a deep dive into Mini-batch Gradient Descent:

• Objective : Similar to other gradient descent variants, the aim of Mini-batch Gradient Descent is to optimize the model’s parameters to minimize the cost function. It seeks to combine the efficiency of SGD with the stability of Gradient Descent by using a subset of the training data to compute gradients and update parameters.
• Initialization : Start with initial random values for the model’s parameters.
• Gradient computation : Instead of calculating the gradient using the entire dataset (as in Gradient Descent) or a single example (as in SGD), Mini-batch Gradient Descent computes the gradient using a small subset of the training data, known as a mini-batch.
• Update parameters : Adjust the parameters in the direction opposite to the computed gradient. This adjustment is made based on the gradient derived from the mini-batch, aiming to reduce the cost function.
• The parameter update rule for Mini-batch Gradient Descent can be represented as

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