data warehouse presentation topics

What is a Data Warehouse? A tutorial for beginners

So you’ve heard of a data warehouse , but now what? Do you need one? How can a data warehouse work for you and your business? We’ll look into the basics of a data warehouse , uses of a data warehouse and how it can be a great asset for you and your company.

But first, we have to look into data, and how it can be used to drive your business forward.

How Data Can Drive Your Business Forward

Organizations today have access to more data than ever before. This data is flowing in from many different areas – retail point-of-sale (PoS), CRM information, data from social networks, or even manufacturing data.

Combined with the ability of modern computers to process this massive amount of data, valuable lessons about past events, current performance and future opportunities can be gleaned. Companies are combining this data from the various disparate sources to form a fuller picture upon which decisions can be made.

The Harvard Business Review published a paper entitled “The Evolution of Decision Making: How Leading Organizations Are Adopting a Data-Driven Culture." The paper notes that “in a rapidly changing global business environment, the pressure on organizations to make accurate and timely decisions has never been greater. The ability to identify challenges, spot opportunities, and adapt with agility is not just a competitive advantage but also a requirement for survival.” Data, the paper goes on to note, offers key business intelligence (BI) information, that can be leveraged to make faster, better decisions.

This access to data to drive better decision making affects the entire organization. It could be the CEO deciding on which geographical market to pursue next, the product team deciding on new features, or marketing looking at the results of the latest campaign. In fact, the more accessible the data is, the better the synergies and opportunities that become available.

Getting all of this information from different sources, and making it accessible to users, is challenging. Not lease because of:

• Data is sorted in different ways by different systems
• Data might be updated at different times for each data source
• Data might not make sense to the end user, in its current form

All of these challenges—and many more—can be solved through the use of a data warehouse . So what exactly is a data warehouse ?

Data Warehouse

A data warehouse is any system that collates data from a wide range of sources within an organization. Data warehouses are used as centralized data repositories for analytical and reporting purposes.

Lately, data warehouses have increasingly moved towards cloud-based warehouses and away from traditional on-site warehouses. There are a number of advantages to using a cloud-based data warehouse , including:

• Scalability : unlike on-site warehouses, scalability can be achieved with the click of a button
• Cost : no hardware or upfront licensing costs
• Time to market : it’s quick and easy to get a data warehouse up and running in the cloud
• Performance : cloud data warehouses are optimized for analytics
• Maintenance : when on-site data warehouses run into problems, they requires significant resources (time, manpower, money) to keep them effective
• Functionality : adding new data sources, for example, to an on-premise data warehouse can be quite an undertaking, whereas cloud data warehouses are often set up to easily accept new sources

In talking about what a data warehouse is, it's helpful to understand what a data warehouse isn't .

1. A data warehouse is not a database .  

The “ data warehouse vs database ” question is often asked. Databases are commonly used for transactional processing (called “OLTP," or “online transaction processing”). Database software needs to provide easy access to information and fast querying so that transactions can be carried out efficiently. They are often referred to as operational systems, meaning they are used to process day-to-day transactions in an organization.

A data warehouse on the other hand is used for online analytical processing (OLAP), which uses complex queries to analyze , rather than process, transactions. These data warehouse concepts are important in understanding the value of a data warehouse . In short, a data warehouse is built to store large quantities of data and enable fast, complex queries across all this data, while a database was is primarily used to store current transactions and enable fast access to specific transactions for ongoing business processes.

2. A data warehouse is not a data mart .

A data mart is a subset of a data warehouse oriented to a specific business line. Data marts contain repositories of summarized data collected for analysis on a specific section or unit within an organization, for example, the sales department.

A data warehouse on the other hand is a large centralized repository of data that contains information from many sources within an organization. The collated data is used to guide business decisions through analysis, reporting, and data mining tools. Whereas in the past, organizations would need to decide whether to build specialized data marts and how these would fit into the data warehouse , today with cloud-based data warehouse services being so cost-effective, scalable, and extremely accessible, organizations of all sizes can leverage cloud infrastructure and build a centralized data warehouse . For more detailed information, and a data warehouse tutorial , check this article .

3. A data warehouse is not a data lake .

A data lake is a highly scalable storage system that holds structured and unstructured data in its original form and format. A data lake does not require planning or prior knowledge of the data analysis needed—it assumes that analysis will happen later, on-demand. A data warehouse , however,  contains structured, processed, mature data, and is more likely to be used by a business professional than a data scientist.

How a data warehouse can help you

So you now have all of your data in a data warehouse . Now what? And what are the advantages of a data warehouse ?

This is where the really interesting part comes in. Using BI tools, for example,  you can now query your data and take out key learnings – many of which would not have been obvious without this data warehouse /BI combination. Using a BI tool on top of your data warehouse lets you visualize the data, and see patterns, trends and correlations.

In addition to the benefits of using a BI tool to drive data-driven decisions, you will have all of your data stored, across the organization, in one place and in a structured manner. The need of a data warehouse is critical for anyone that wants a data-oriented business approach.

Data warehouse example

One of the best ways to see a data warehouse in action, and appreciate the benefits of a good data warehouse , is to look at a data warehouse example and the uses of a data warehouse .

At Foursquare, the company leverages a data warehouse to ensure that critical, up-to-date and aggregated information is available to anyone that needs it throughout the organization. Jon Hoffman, a Foursquare software engineer, notes that “anyone in the company can set up any queries they like — from how users are reacting to a feature, to growth by demographic or geography, to the impact sales efforts had in different areas". Foursquare leverages this to drive data-oriented decisions across the organization.

Data warehouse tools

There are many data warehouse tools available that can make the data warehousing process a lot smoother and easier. Some of these deal with moving data to the data warehouse (the most commonly used is the ETL process), while others deal with various other parts of the process including testing the data in the data warehouse to ensure it is correct.

While these tools help you to achieve different things, using a solution like Panoply can take care of both with one simple platform. Built for analytics professionals, by analytics professionals, Panoply puts analysis-ready data at your fingertips so you can focus on finding insights, not maintaining infrastructure.

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Data Warehouse Concepts in 2024: Building a Library

Table of contents:.

Data warehouse concepts can be presented as a grand library, where books (data) from various sources are collected, organized, and standardized, allowing researchers (users) to access a centralized knowledge repository consistently and efficiently, enabling deeper insights and analysis.

Data Warehouse Concepts Influence Strategic Plans

Data warehousing is crucial for businesses as it provides a centralized and integrated view of data from various sources , enabling better decision-making and strategic planning.

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It improves data quality and consistency, ensuring reliable and accurate information for reporting and analysis. The data warehouse definition also facilitates historical data storage, allowing companies to track trends, patterns, and historical performance, supporting long-term business analysis and forecasting.

Key Data Warehouse Concepts

Data warehouse concepts are the foundational principles, strategies, and techniques that call for the design, data warehouse development, and implementation of a data warehouse. They encompass various aspects — data integration, modeling, transformation, storage, and retrieval — to provide a unified and reliable repository for reporting. Key concepts in data warehousing include dimensional modeling, ETL (Extract, Transform, Load) processes, data cleansing, metadata management , and query optimization techniques. It's a brief introduction to the data warehouses.

Data Warehouse Market Size, 2015 - 2021, Revenue

Data Warehouse Concepts Features

The main data warehouse characteristics are as follows:

• Designing to focus on specific subject areas — sales, customers, or products.
• Integrating data from various sources, including operational databases and external and legacy systems.
• Storing historical data, enabling the tracking of changes and trends over time.
• Data warehouses are non-volatile, meaning they are not changed or updated once data is loaded.
• The primary purpose of a data warehouse is to support decision-making processes.
• Optimizing for query performance.
• Data warehouses separate the analytical workload from operational systems, preventing interference with transactional performance and ensuring a dedicated environment.

These data warehouse characteristics collectively make them reliable and efficient repositories for historical, integrated, and subject-oriented data warehouses.

Business intelligence will give a clear picture for feature selection.

Data warehouse concepts for knowledge repository.

Like a library collects and organizes books from various sources, a data warehouse gathers and consolidates data from multiple operational systems. It acts as a centralized hub where data, like books, is carefully organized, classified, and standardized, making it easily accessible to users who seek valuable information. Similar to how a library supports research and learning by providing a curated collection, a data warehouse enables businesses to analyze and make informed decisions based on a comprehensive and reliable pool.

Data warehouse concepts vs. traditional databases

While traditional databases excel at transactional processing, data warehouses are designed to analyze, consolidate, and organize large volumes of historical data.

Data Warehouse Concepts — Organized Book Collection

As a library's components work together to provide a rich and organized collection of information, the data warehouse components collaborate to create a consolidated and structured data repository, supporting data integration, storage, modeling, analysis, and reporting for effective decision-making within an organization.

Data warehouse source systems

Source systems represent the books and publications the library acquires from different authors and publishers. Data warehouses gather data from multiple operational systems, external sources, and legacy systems, acting as the repository for these diverse sources.

ETL (Extract, Transform, Load) in data warehouse

The ETL data warehouse process is likened to a library's acquisition and cataloging process. Librarians extract relevant information from books, transform it into a standardized format, and load it into the library's catalog. The ETL extracts data from source systems, applies transformations and cleansing, and loads it into the data warehouse in a consistent and usable format.

Data warehouse storage

There are shelves and stacks of books in a library. The data in a warehouse is organized and stored in a structured manner, often using techniques such as indexing, partitioning, and compression to optimize storage efficiency and query performance.

Metadata management with data warehouse

It is equivalent to the library's cataloging system, where librarians maintain records of the books, including information about the author, title, subject, and location. In a data warehouse, metadata management calls for capturing and organizing information about the data: source, lineage, definitions, and transformations applied, — facilitating data discovery and understanding.

Reporting and analysis from the data warehouse

In a data warehouse, they are like reading and research activities in a library. Users access the data through tools and analytical applications to generate reports, perform ad-hoc queries, and conduct in-depth analysis for business intelligence in data warehouses and decision support.

The Benefits of Data Warehouse Concepts

The benefits of data warehousing include improved decision-making, enhanced data quality, efficient access and analysis, integrated view, historical trend analysis, scalability and flexibility, business intelligence and reporting, and regulatory compliance and auditing.

Data warehouse: the ability to easily access data

Improved data accessibility is the enhanced ability of users to efficiently access data from a data warehouse, enabling quick and convenient retrieval and analysis of relevant information for reporting, decision-making, and business intelligence purposes.

Storing data in a unified location

Centralized storage and access refers to consolidating data in a single location or system, such as a data warehouse. It allows efficient and standardized access to data from various sources, eliminating silos and providing a consistent information view. Users can retrieve, query, and analyze the data from a central location, enabling streamlined data management .

Breaking down of isolated repositories in data warehouse

The elimination of data silos is the breaking down of disconnected repositories within a company. Data silos occur when different departments or systems maintain separate databases or sources, resulting in fragmentation, duplication, and inconsistencies. Companies can create a cohesive and consistent view by integrating and centralizing data into a data warehouse. It improves data accessibility, sharing, collaboration, and better insights and eliminates redundant efforts.

Overall reliability

Enhanced data quality and consistency touch on the improvements made in accuracy, reliability, completeness, and uniformity. Quality makes the overall fitness for use of data, while consistency ensures that data is standardized and coherent across different sources and data warehouse systems. They are achieved through various processes: cleansing, validation, standardization, and integration for eliminating errors, redundancies, and inconsistencies in the data warehouse. Increased data quality permits us to make more informed decisions, perform reliable analyses, and confidently rely on the data for reporting.

Make informed decisions with the data warehouse

Decision-makers access timely and accurate information, perform complex queries, conduct in-depth analysis, and gain valuable insights into business operations, customer behavior, market trends, and relevant factors by exploiting the data stored in the data warehouse.

Handling sophisticated queries from the data warehouse

Data warehouses are designed to license complex data retrieval and analysis, allowing users to perform operations — aggregations joins, filtering, sorting, and calculations — across large volumes of data. The support for complex queries and analysis enables users to explore data from different angles, drill down into specific details, conduct multidimensional research, and derive meaningful insights. Data warehouses empower users to gain a deeper understanding of their data, uncover patterns, trends, and relationships, and make informed decisions based on detailed analysis by supporting complex queries.

Exploration of stored data

Data warehouses retain historical data, allowing companies to perform retrospective analyses and gain insights into past performance, customer behavior, market dynamics, and other historical trends. Historical data analysis helps forecast future outcomes, detect anomalies, and evaluate past strategies' effectiveness.

Visually appealing data

Data warehouses often integrate with business intelligence tools and reporting platforms, allowing users to create interactive reports, dashboards, charts, graphs, and other visual data representations. Users explore data visually, perform interactive analysis, and present findings concisely, facilitating communication of insights across the company by leveraging visualization and reporting capabilities.

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Increasing data volumes and user demands.

Scalability and performance are crucial aspects of data warehouse concepts, as they ensure that the data warehouse can handle increasing data demands, support growing user requirements, and deliver efficient and responsive data access and analysis capabilities.

Speed of querying and accessing

• Indexing creates data warehouse structures — B-trees or hash tables — allowing quick and efficient data lookup based on specific columns. Data warehouses can significantly reduce the time required to search data by indexing frequently queried columns.
• Optimization techniques focus on improving query performance and reducing execution time. It analyzes query execution plans and optimizes the order of operations to minimize the amount of data processed. Data warehouses often use query optimization algorithms and techniques (cost-based optimization and query rewriting) to identify the most efficient execution plan for a given query.

Accommodating multiple users

• Data warehouses employ data partitioning, compression, and indexing strategies to manage and process massive datasets efficiently. This scalability enables it to store and analyze vast amounts of data without compromising performance or integrity.
• Data warehouses allow multiple users to query and analyze data simultaneously. Users across the company access the data warehouse concurrently without experiencing performance degradation. Data warehouses efficiently handle queries and workloads from multiple users by employing techniques — parallel processing and resource allocation.

Continuous optimization

Performance monitoring and tuning in data warehouse concepts make continuously assessing and optimizing the performance of a data warehouse system to ensure responsive operations.

• Performance monitoring monitors query execution times, system resource utilization, data load times, and system availability. It identifies areas of improvement and provides insights into the overall health and performance of the data warehouse.
• Performance tuning optimizes query plans, modifying indexing strategies, refining data partitioning techniques, adjusting memory and storage configurations, and fine-tuning resource allocation. Performance tuning aims to minimize query response times.
• Query optimization in a data warehouse analyzes query execution plans, identifying inefficient operations or joins and making adjustments to optimize performance. Rewriting, indexing, caching, and parallel processing techniques are employed to enhance query execution.
• Capacity planning assesses the data warehouse system's current and future data and user demands and ensures sufficient resources and infrastructure are in place to handle the anticipated workload.

Companies can proactively address performance bottlenecks, optimize system resources, and ensure the data warehouse operates at its full potential by implementing performance monitoring and tuning practices.

Data Warehouse Concepts Like Structured Library Systems

In a library, books serve as the primary source of information. In data warehousing, sources — databases, operational systems, external files — act as the "books" that contain valuable data. Librarian (ETL) collects, organizes, and categorizes books. A data warehouse's Extract, Transform, and Load processes play a similar role. Bookshelves provide storage space for books. A data warehouse is a centralized repository that stores structured data for analysis. Libraries use a cataloging system to organize books based on titles, authors, and subjects. In data warehousing, metadata does the same. Here's how to explain data warehouse architecture.

Special data warehouse frameworks

Data warehouse architectures are frameworks that organize and structure data for efficient storage, retrieval, and analysis to support business intelligence and decision-making processes.

Kimball architecture

The traditional Kimball data warehouse architecture is based on comprehensive data warehouse methodologies developed by Ralph Kimball for designing and implementing data warehouses. It encompasses several key principles and components, including dimensional modeling in the data warehouse, star warehouse schemas, ETL (Extract, Transform, Load) processes, and a focus on business intelligence and decision support. It emphasizes simplicity, flexibility, and user accessibility, allowing for efficient data retrieval and analysis to support business reporting and decision-making needs.

Data warehouse in a cloud computing

Cloud warehousing is deploying and managing a data warehouse in a cloud environment. It utilizes cloud-based infrastructure, storage, and services to store, process, and analyze large volumes of data. In a cloud warehousing setup, the data warehouse is hosted on cloud platforms such as Amazon Web Services (AWS data warehouse), Microsoft Azure data warehouse, Google Cloud Platform (Google data warehouse), or Snowflake. Instead of maintaining on-premises hardware and infrastructure, organizations use the scalability, flexibility, and cost-effectiveness of data warehouse cloud computing.

Cloud data warehouse deployments are growing, according to IDG

The modern data warehouse architecture types

The modern data warehouse architecture, also known as the Inmon architecture, is an approach to data warehousing proposed by Bill Inmon. It focuses on integrating data from various sources into a centralized repository called the "data warehouse." Key characteristics of the modern architecture include:

• Normalization
• Centralized Data Warehouse
• Data Integration and Transformation
• Metadata in data warehouse management

The Inmon architecture provides a structured and unified approach to data warehousing, enabling organizations to build a consistent and reliable foundation for data analysis.

Specific needs in data warehouse basics and concepts

When building a data warehouse, several important considerations should be considered. During the data warehouse design process, companies develop a robust and effective data warehousing solution that meets the business's specific needs and enables valuable insights and decision-making.

Dimensional modeling

It is a popular modeling technique used in data warehouse design. It provides a structure for organizing and representing data optimally for querying and analysis. Dimensional modeling focuses on capturing the business context and data hierarchies, making it easier for end users to understand and navigate the data.

Two primary types of data warehouse tables

In dimensional modeling, two primary types of tables are used:

• Fact tables in the data warehouse contain measurable data from different types of facts in the data warehouse that represents business events or transactions. They typically include foreign keys referencing the related dimension tables and numerical measures (facts) defining the interest metrics. Fact tables are large and contain millions or billions of rows.
• The dimension table in the data warehouse contains the descriptive attributes that provide the context for the measures in the fact table. Dimension tables store the qualitative information associated with the data: customer details, product information, periods, geographical locations, etc. Dimension tables are smaller and have fewer rows.

It describes the influence of facts and dimensions in the data warehouse. It has three main types of dimensions: conformed, slowly changing, and role-playing.

The level of detail

Granularity shows the level of detail at which data is captured and stored in a data warehouse. It represents the extent to which individual records are provided in the data warehouse. Granularity can vary based on the specific requirements of the business. In data warehouse design, there are two main types of data granularity:

• Fine-grained granularity means storing data at a detailed level, often capturing individual transactions or events. Fine-grained data allows for a more comprehensive analysis but can result in larger volumes.
• Coarse-grained granularity implies aggregating data to a higher summarization level. It reduces the volume of data by consolidating multiple transactions into summarized values and enables faster querying but may sacrifice some level of detail.

Data hierarchies represent the relationships and levels of the company within a dimension. Hierarchies define the different levels of detail and their logical order, allowing users to navigate through the data at various levels of granularity. Hierarchies provide a structured way to drill down into data, enabling multidimensional analysis.

Data Warehouse Concepts — Well-known Examples

The data warehouse examples have emerged due to the growing need for companies to manage large volumes of data effectively. These data warehouses were developed and implemented to address specific data warehouse requirements and leverage the benefits of centralized storage and analysis. Some of the data warehouse products are listed below.

Amazon Redshift data warehouse

It is a fully managed data warehousing service provided by Amazon Web Services (AWS). It offers fast query performance and scalability, making it suitable for large-scale data warehouse analytics and reporting. Redshift imposes data warehouse meaning on columnar storage and parallel query execution to handle massive volumes.

Google BigQuery

BigQuery is a serverless, fully managed data warehouse provided by Google Cloud Platform (GCP). It gives fast query processing and scalability, enabling organizations to analyze large datasets cost-effectively. BigQuery forces a distributed architecture and supports data warehouse SQL queries for data exploration and analysis.

Snowflake in data warehouse

It is a data warehouse on a cloud platform known for its scalability, performance, and ease of use. It provides a fully managed service that separates computing and storage, enabling organizations to scale resources independently. Snowflake data warehouse offers robust data warehouse security features and supports ANSI SQL queries for data analysis.

Microsoft Azure Synapse Analytics (formerly Azure SQL Data Warehouse)

Data warehouse in Azure Synapse Analytics is integrated analytics for data warehouse service that combines data warehousing and big data capabilities. It offers a unified experience for ingesting, preparing, managing, and serving data for analytics purposes . Azure Synapse Analytics supports various data integration and processing warehouse technologies, including Spark and SQL.

Pros and Contras of Data Warehouse Concepts

A well-organized library offers resources to support informed decision-making, and a data warehouse provides the same advantage in analysis. A data warehouse empowers users to make data-driven decisions by consolidating data and providing tools for reporting and analysis. Here are some advantages and disadvantages of the data warehouse.

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Advantages of data warehouse.

• A data warehouse provides a centralized repository where data from various sources is consolidated and integrated. It unifies the view of the company's data, making it easier to analyze and gain insights across different business functions and departments.
• Data warehousing involves data validation, cleansing, and transformation processes. It ensures that the warehouse's data is high quality, consistent, and standardized.
• With a well-designed data warehouse, users can retrieve and analyze data using familiar tools and data warehouse techniques. Self-service capabilities empower users to explore and query data independently, reducing reliance on IT teams.
• Data warehouses are optimized for query performance, enabling users to retrieve and analyze data quickly. Additionally, data warehouses scale resources as needed to accommodate growing volumes and user demands.
• Data warehousing stores historical data over time, allowing us to analyze trends, identify patterns, and make informed decisions based on historical insights. Historical data helps forecast, monitor performance, and evaluate long-term trends and business impact.
• Companies create a comprehensive view of their operations, customers, and markets. This integration enables better data warehouse business intelligence by providing a holistic understanding of the business and facilitating cross-functional analysis with the data warehouse.
• Data warehousing enables us to conduct advanced analytics, perform complex calculations, and generate dashboards for better decision-making. Data-driven insights from the warehouse support strategic planning initiatives and help stay competitive.
• Data warehousing facilitates regulatory compliance by providing a centralized and controlled environment for data management. It helps organizations meet legal, regulatory, and industry-specific requirements, ensuring privacy and security.

Disadvantages of data warehousing

While data warehousing offers numerous benefits, it's important to be aware of the potential disadvantages and challenges that companies may encounter.

• Implementing a data warehouse means significant upfront and ongoing costs. These costs include hardware data warehouse infrastructure, software licenses, integration tools, skilled personnel, and maintenance expenses.
• Designing and implementing a data warehouse is a complex and time-consuming process. It requires careful planning, modeling, extraction, transformation, and loading (ETL) processes, and integration with various sources.
• Data warehousing typically entails extracting, transforming, and loading data from various sources into the warehouse. There may be a delay between when data is generated and when it becomes available for analysis in the warehouse.

The potential data integration challenges can pose disadvantages for data warehousing, including complexities in consolidating and harmonizing diverse sources, quality issues, and ensuring seamless integration across systems.

Data Warehouse Concepts: Unstored and Cataloged Libraries

A data lake is a storage repository that holds raw, unprocessed data from various sources, while a data warehouse is a structured and organized collection of processed data optimized for querying and analysis. A data lake resembles a library's unsorted books or document collection. A data warehouse is a well-organized library where texts are stored in a structured manner for easy access and analysis.

Data lakes prioritize flexibility, scalability, and the exploration of raw data, while data warehouses focus on structured and processed data for efficient analysis.

Big Data and Data Warehouse Concepts

Consider big data as a collection of books from different sources representing diverse genres, languages, and formats. These books constantly flow into the library, each containing a wealth of information and insights waiting to be discovered. Data warehousing is designed to efficiently manage and store selected books from the big data, ensuring easy access, analysis, and retrieval. The relationship between the collection and the library is symbiotic.

• Big data refers to the vast amount of data generated from various sources, including structured, semi-structured, and unstructured data. On the other hand, a data warehouse focuses on structured and organized data.
• Hadoop and NoSQL databases collect, store, and process large-scale and diverse sets. Data Warehousing integrates selected data from big data sources, along with info from other structured systems, into a central repository.
• Big data technologies provide scalability and distributed computing capabilities to handle massive volumes and complex processing requirements. Data Warehousing solutions also offer scalability but are optimized for efficient querying.
• Big data analytics focuses on extracting insights from large and diverse datasets, leveraging data mining, machine learning, and sentiment analysis techniques. Data Warehousing supports analytical operations, including reporting, ad-hoc querying, and multidimensional analysis.
• Data warehousing emphasizes governance practices. Big data initiatives mean less structured and more exploratory analysis. However, as selected data is integrated into a data warehouse, data governance practices are applied to ensure consistency, accuracy, and security.
• Companies adopt hybrid approaches that combine big data and warehousing. They leverage big data warehouse platforms for storage, processing, and exploration and then selectively move processed and relevant data into a data warehouse for structured analysis and reporting.

Big data and data warehousing are interrelated in the data management and analytics landscape.

Data warehouse vs. data mining

The difference between data mining and data warehousing lies in their focus and purpose: mining means analyzing large datasets to discover patterns and insights, while data warehouse is the structured storage and organization of integrated data. Mining can be performed within a data warehouse, leveraging structured and consolidated data for analysis. If we define a data warehouse in data mining, it refers to the use of structured and compact data stored in a data warehouse for performing mining analysis.

The Best Implementation of Data Warehouse Concepts

The optimal implementation of data warehousing calls for careful planning and design to align with business or data warehouse goals and requirements. It should include detailed modeling and schema design to ensure efficient storage and retrieval. A robust ETL (Extract, Transform, Load) process and governance practices should be established to warrant accuracy, consistency, and security in the data warehouse.

Data Warehouse Concepts Requires Strategic Thinking

Several specific storage strategies are suited for data warehousing. DATAFOREST , in the course of its long-term activity in this area, often uses hybrid approaches to storing a large amount of data, depending on the conditions set by the business. Structured and transactional data may reside in a relational database, while large-scale analytical datasets may be stored in a distributed file system. The choice of storage strategy depends on data volume, type, query performance needs, scalability requirements, and the architecture and goals of the data warehousing solution.

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What is a data warehouse?

If we define a data warehouse, it is a centralized and structured repository that integrates info from various sources, enabling efficient analysis, reporting, and decision-making. It's only a short definition of the data warehouse.

Why do we need a data warehouse?

We need a data warehouse to efficiently integrate, store, and analyze data from multiple sources, providing a comprehensive and reliable view of organizational statistics for better strategic insights.

How does data warehousing improve data accessibility and consolidation?

Data warehousing improves data accessibility and consolidation by providing a centralized and structured repository that integrates details from multiple sources, enabling easier access, analysis, and reporting across the company with data warehouse concepts. It is the business importance of data warehouses.

In what ways does data warehousing enhance decision-making processes?

Data warehousing enhances decision-making processes by providing consolidated, reliable, and timely insights, enabling organizations to make informed and data-driven decisions. It’s the feature of the data warehouse introduction. This consequence suggests a structure of a data warehouse.

What is an enterprise data warehouse?

An enterprise data warehouse is a centralized and comprehensive repository that consolidates proof from various business functions and systems, enabling cross-functional analysis and reporting. It has unique enterprise data warehouse architecture.

Provide examples of industries or use cases where data warehousing is beneficial.

Industries or use cases where data warehousing is beneficial include retail for sales analysis and customer segmentation, healthcare for patient integration and analytics, and finance for risk management and regulatory compliance by data warehouse concepts. It’s the promotion to build a data warehouse.

What is OLAP in a data warehouse?

OLAP (Online Analytical Processing) in a data warehouse calls for multidimensional analysis, allowing users to navigate, drill down, and perform interactive analysis on aggregated and summarized info from different dimensions and hierarchies with data warehouse OLAP conception.

What are the potential challenges in implementing and managing a data warehouse?

The potential challenges in implementing and managing a data warehouse include complex integration processes, ensuring quality and consistency, and managing the scalability and performance of the system as data volumes and user demands increase.

How does the cost of data warehousing compare to other data management approaches?

Data warehousing costs tend to be higher than other data management approaches due to factors such as infrastructure setup, maintenance, integration efforts, and skilled personnel requirements for key features of the data warehouse.

Are any specific skills or expertise required to implement and maintain a data warehouse?

Implementing and maintaining a data warehouse requires specific skills and expertise such as modeling, ETL (Extract, Transform, Load) processes, database administration, governance, analytics, and an understanding of the organization's business requirements and architecture in data warehouse concepts. It means the development of data warehouse functions and components of data warehouse architecture and data warehouse principles.

How does data warehousing address the issue of data integration and consistency?

The concept of a data warehouse addresses the issue of integration and consistency by providing a centralized repository where data from disparate sources is transformed, standardized, and organized, ensuring a unified and consistent view of the facts for analysis and reporting purposes. That’s why businesses need data warehouses.

What is a star schema in a data warehouse?

Star schema in a data warehouse is a modeling technique that organizes data into a central fact table surrounded by dimension tables resembling a star shape, allowing for efficient querying and analysis with data warehouse concepts. It is a crucial function of the data warehouse.

What is the difference between a database and a data warehouse?

Looking at the format data warehouse vs. database, it's clear that a database is designed for transactional processing and day-to-day operations, while a data warehouse is optimized for analytical processing and decision support by consolidating and integrating info from various sources. It is the difference between a database and a data warehouse.

What is a dimension table in a data warehouse?

A dimension table in a modern data warehouse is a table that contains descriptive attributes or dimensions that provide context and additional information about the data in the fact table, facilitating analysis and reporting. It increases data warehouse functionality when it is in a data warehouse strategy.

What does a data warehouse allow the organization to achieve?

A data warehouse allows organizations to achieve improved analysis, enhanced reporting capabilities, and informed decision-making based on a consolidated and reliable view of their data.

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An Introduction to Data Warehousing

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15 Data Warehouse Project Ideas for Practice with Source Code

Learn how data is processed into data warehouses by gaining hands-on experience on these fantastic solved end-to-end real-time data warehouse projects.

The worldwide data warehousing market is expected to be worth more than $30 billion by 2025. Data warehousing and analytics will play a significant role in a company’s future growth and profitability. Data warehouse solutions will provide every business a considerable advantage by evaluating all of the data they collect and making better decisions. Understanding business data will help make intelligent business decisions that determine whether an organization succeeds or fails. The demand for Big Data and Data Analytics will continue to grow in the coming days, leading to a greater need for Data Warehouse solutions. 

It’s essential to understand why data warehousing projects fail before getting an idea of the different data warehousing projects to explore from beginner to advanced level in your learning path. So let's get started!

Table of Contents

What is data warehousing, why data warehouse projects fail, data warehouse projects for beginners, data warehouse projects for intermediate, data warehouse projects for advanced, data warehouse project tools.

Data warehousing (DW) is a technique of gathering and analyzing data from many sources to get valuable business insights. Typically, a data warehouse integrates and analyzes business data from many sources. The data warehouse is the basis of the business intelligence (BI) system, which can analyze and report on data.

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To put it in other words, Data Warehousing supports a set of frameworks and tools that help businesses organize, understand, and use their data to make strategic decisions.

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The significant roadblocks leading to data warehousing project failures include disconnected data silos, delayed data warehouse loading, time-consuming data preparation processes, a need for additional automation of core data management tasks, inadequate communication between Business Units and Tech Team, etc.

• Delayed Data Warehouse Loading

 Data must first be prepared and cleaned before being placed into the warehouse. Cleaning data is typically time-consuming, so this creates an immediate crisis. IT professionals are often disappointed by the time spent preparing data for loading. The ability of enterprises to quickly move and combine their data is the primary concern. Movement and ease of access to data are essential to generating any form of insight or business value. This often exhausts an organization's time and resources, resulting in a more protracted and expensive project in the end. Furthermore, poor data loading might result in various issues, including inaccuracies and data duplication.

Lower End-User Acceptance Rate

End-user acceptability is another factor that frequently leads to the failure of data warehouse projects. New technologies can be fascinating, but humans are afraid of change, and acceptance may not always be the case. Any project's success depends on how well people are mutually supportive. The first step in encouraging user acceptance and engagement is to create a data-driven mindset. End users should be encouraged to pursue their data-related interests. Non-technical users will benefit from self-service analytics because it will make it easier to access information fast. These transitional efforts will aid the success and utilization of your data warehouse in the long run and lead to better decision-making throughout the organization.

Automation of core management activities

If you carry out a process manually, valuable time, resources, and money are invested instead of automating it, thereby wasting business opportunities. You can automate manual, time-consuming operations, which helps you save money while shortening the time to see results. Automation can accelerate all data management and data warehousing steps, including data collection, preparation, analysis, etc.

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15 Data Warehouse Project Ideas for Practice

This section will cover 15 unique and interesting data warehouse project ideas ranging from beginner to advanced levels.

From Beginner to Advanced level, you will find some data warehouse projects with source code, some Snowflake data warehouse projects, some others based on Google Cloud Platform (GCP), etc.

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Snowflake Real-time Data Warehouse Project

In this Snowflake Data Warehousing Project, you'll learn how to deploy the Snowflake architecture to build a data warehouse in the cloud. This project will guide you on loading data via the web interface, SnowSQL, or Cloud Provider. You will use Snowpipe to stream data and QuickSight for data visualization .

Source code- Snowflake Real-time Data Warehouse Project  

Slowly Changing Dimensions Implementation using Snowflake

 This project depicts the usage of Snowflake Data Warehouse to implement several SCDs. Snowflake offers various services that help create an effective data warehouse with ETL capabilities and support for various external data sources. Use Python's faker library to generate user records and save them in CSV format with the user's name and the current system time for this project. Fake data is made with the faker library and saved as CSV files. NiFi is used to collect data, and Amazon S3 sends the data. New data from S3 is loaded into the staging table using a Snowpipe automation tool. Data manipulation language changes are stored in the staging table using Snowflake streams to determine the operation to be done.  Initiate tasks and stored procedures depending on the changes to implement SCD Type-1 and Type-2.

Source Code- Slowly Changing Dimensions Implementation using Snowflake

New Projects

Fraud Detection using PaySim Financial Dataset

In today's world of electronic monetary transactions, detecting fraudulent transactions is a significant business use case. To overcome this issue, PaySim Simulator is used to create Synthetic Data available on Kaggle. The data contains transaction specifics such as transaction type, transaction amount, client initiating the transaction, old and new balance, i.e., before and after the transaction, and the same in Destination Account along with the target label, and is fraudulent. This data warehouse project uses the PaySim dataset to create a data warehouse and a classification model based on transaction data for detecting fraudulent transactions.

Source Code- Fraud Detection using PaySim Financial Dataset

Anime Recommendation System Data Warehouse Project

The anime recommendation system is one of the most popular data warehousing project ideas. Use the Anime dataset on Kaggle, which contains data on user preferences for 12,294 anime from 73,516 people. Each user can add anime to their completed list and give it a rating. The project aims to develop an effective anime recommendation system based on users' viewing history. Use the Anime dataset to build a data warehouse for data analysis. Once the data has been collected and analyzed, it becomes ready for building the recommendation system.

Source code- Anime Recommendation System Data Warehouse Project

Marketing Data Warehouse for Media Research Company

Customer relationship management and sales systems, for example, might cause marketing data to get diffused across various systems within an organization.

Create a marketing data warehouse for this project, which will serve as a single source of data for the marketing team to work with. You can also combine internal and external data like web analytics tools, advertising channels, and CRM platforms. Use the Nielsen Media Research company dataset for building this data warehouse. All marketers will access the same standardized data due to the data warehouse, allowing them to execute faster and more efficient projects. Such data warehouses enable organizations to understand performance measures, including ROI, lead attribution, and client acquisition costs.

Source Code- Marketing Data Warehouse for Banking Dataset

Data Warehouse Design for E-commerce Environments

You will be constructing a data warehouse for a retail store in this big data project. However, it concentrates on answering a few particular issues about pricing optimization and inventory allocation in terms of design and implementation. In this hive project, you'll be attempting to answer the following two questions:

Were the higher-priced items more prevalent in some markets?

Should inventory be reallocated or prices adjusted based on location?

Source Code- Data Warehouse Design for E-commerce Environments

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Data Warehouse Project for Music Data Analysis

This project involves creating an ETL pipeline that can collect song data from an S3 bucket and modify it for analysis. It makes use of JSON-formatted datasets acquired from the s3 bucket. The project builds a redshift database in the cluster with staging tables that include all the data imported from the s3 bucket. Log data and song data are the two datasets used in the project. The song_data dataset is a part of the Million Song Dataset , and the log_data dataset contains log files generated based on the songs in song_data. Data analysts can use business analytics and visualization software to understand better which songs are most popular on the app.

Source Code- Data Warehouse Project for Music Data Analysis

Global Sales Data Warehouse Project

The primary goal of this Global Sales Data Warehouse project is to minimize raw material manufacturing costs and enhance sales forecasting by identifying critical criteria such as total sales revenue on a monthly and quarterly basis by region and sale amount. The Data Warehousing Project focuses on assessing the entire business process. The data warehouse provides essential information such as daily income, weekly revenue, monthly revenue, total sales, goals, staff information, and vision.

Source Code- Sales Data Warehouse Project  

Data Warehouse Project for B2B Trading Company

This project aims to employ dimensional modeling techniques to build a data warehouse. Determine the business requirements and create a data warehouse design schema to meet those objectives. Using SSRS and R, create reports using data from sources. Based on the data warehouse, create an XML schema. Use Neo4j technologies to design a data warehouse section as a graph database.

Source Code- Data Warehouse Project for B2B Trading Company

Heart Disease Prediction using Data Warehousing

One of the most commonly seen diseases today is heart disease. In this data warehousing project, you'll learn how to create a system that can determine whether or not a patient has heart disease. The data warehouse assists in correlating clinical and financial records to estimate the cost-effectiveness of care. Data mining techniques aid in identifying data trends that may anticipate future individual heart-related issues. Furthermore, the data warehouse aids in the identification of individuals who are unlikely to respond well to various procedures and surgeries.

Source Code- Heart Disease Prediction using Data Warehousing

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GCP Data Ingestion using Google Cloud Dataflow

Data ingestion and processing pipeline on Google cloud platform with real-time streaming and batch loading are part of the project. This project uses the Yelp dataset, primarily used for academic and research reasons. We first create a GCP service account, then download the Google Cloud SDK. In subsequent operations, the Python program and all other dependencies are then downloaded and connected to the GCP account. It downloads the Yelp dataset in JSON format, connects to Cloud SDK through Cloud storage, and connects to Cloud Composer. It publishes the Yelp dataset JSON stream to a PubSub topic. Cloud composer and PubSub outputs connect to Google Dataflow using Apache Beam . Lastly, Google Data Studio is used to visualize the data.

Source Code-   GCP Data Ingestion using Google Cloud Dataflow

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Build Data Pipeline using Dataflow, Apache Beam, Python

This is yet another intriguing GCP project that uses PubSub, Compute Engine, Cloud Storage, and BigQuery. We will primarily explore GCP Dataflow with Apache Beam in this project. The two critical phases of the project are-

Reading JSON encoded messages from the GCS file, altering the message data, and storing the results to BigQuery.

Reading JSON-encoded Pub/Sub messages, processing the data, and uploading the results to BigQuery.

Source Code- Build Data Pipeline using Dataflow, Apache Beam, Python

In this next advanced-level project, we will mainly focus on GCP BigQuery. This project will teach you about Google Cloud BigQuery and how to use Managed Tables and ExternalTables. You'll learn how to leverage Google Cloud BigQuery to explore and prepare data for analysis and transformation. It will also cover the concepts of Partitioning and Clustering in BigQuery. The project necessitates using BQ CLI commands and creating an External BigQuery Table using a GCS Bucket, and it uses Client API to load BigQuery tables.

Source Code- GCP Project to Learn using BigQuery for Exploring Data

Anomaly Detection in IoT-based Security System

IoT devices, or network-connected devices like security cameras, produce vast amounts of data that you may analyze to improve workflow. Data is collected and stored in relational formats to facilitate historical and real-time analysis. Then, using existing data, instant queries are run against millions of events or devices to find real-time abnormalities or predict occurrences and patterns. For this project idea, create a data warehouse that will help this data be consolidated and filtered into fact tables to provide time-trended reports and other metrics.

Source Code- Anomaly Detection in IoT-based Security System

AWS Snowflake Data Pipeline using Kinesis and Airflow

This project will show you how to create a Snowflake Data Pipeline that connects EC2 logs to Snowflake storage and S3 post-transformation and processing using Airflow DAGs . Send customers' data and orders data to Snowflake via Airflow DAG processing and transformation and S3 processed stages in this project. You'll learn how to set up Snowflake stages and create a database in Snowflake.

Source Code- AWS Snowflake Data Pipeline using Kinesis and Airflow

Data warehousing optimizes ease of access, reduces query response times, and enables businesses to gain deeper insights from large volumes of data. Previously, building a data warehouse required a significant investment in infrastructure. The introduction of cloud technology has drastically cut the cost of data warehousing for enterprises.

There are various cloud-based data warehousing tools now available in the market. These tools provide high speed, high scalability, pay-per-use, etc. Since choosing the best Data Warehouse tool for your project can often seem challenging, we have curated a list of the most popular Data Warehouse project tools with their essential features-

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Microsoft Azure

Microsoft's Azure SQL data warehouse is a cloud-based relational database. Microsoft Azure allows developers to create, test, deploy, and manage applications and services using Microsoft-managed data centers. The platform is based on nodes and uses massively parallel computing (MPP). The design is well suited for query optimization for concurrent processing. As a result, you can extract and visualize business information considerably more quickly. Azure is a public cloud computing platform that provides IaaS, PaaS, SaaS, among other services.

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Snowflake is a cloud-based data warehousing platform that runs on Amazon Web Services ( AWS ) or Microsoft Azure cloud architecture. You can use Snowflake to create an enterprise-grade cloud data warehouse. You can use the tool to gather and analyze data from both structured and unstructured sources. It uses SQL to perform data blending, analysis, and transformations on various data structures. Snowflake provides scalable, dynamic computing power at per-usage cost, and it enables you to scale CPU resources following user activity.

Google BigQuery

BigQuery is a cost-efficient serverless data warehouse with built-in machine learning features. It's a platform for ANSI SQL querying. Google BigQuery is a data analysis tool that allows you to process read-only data sets in the cloud and works with SQL-lite syntax to analyze data with billions of rows. You can use it in conjunction with Cloud ML and TensorFlow to build robust AI models . It can also run real-time analytics queries on vast amounts of data in seconds. This cloud-native data warehouse supports geospatial analytics.

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Amazon Redshift

The Amazon Redshift is a cloud-based, fully managed data warehouse. In seconds, the fully managed system can process vast amounts of data. As a result, it's well-suited to high-speed data analytics. Because it is a relational database management system (RDBMS), you can use it with other RDBMS applications. Using SQL-based clients and business intelligence (BI) tools with typical ODBC and JDBC connections, Amazon Redshift facilitates quick querying abilities over structured information. Also, Redshift supports automatic concurrent scaling, and the automation scales up or down query processing resources to match workload demand. You may also scale your cluster or switch between node kinds with Redshift. As a result, you can improve data warehouse performance while lowering operational costs.

Start Building Data Warehousing Projects to Get You a Real-World Data Job

As organizations explore new opportunities and products, data warehouses play a vital role in the process. They're rapidly evolving; especially cloud data warehouses are becoming popular among businesses. They assist companies in streamlining operations and gaining visibility across all areas. Furthermore, cloud data warehouses assist businesses in better serving their clients and expanding their market potential. This makes it even more crucial for data engineers to enhance their data warehousing skills and knowledge to stay ahead of the competition. If we’ve whetted your appetite for more hands-on real-time data warehouse project ideas, we recommend checking out  ProjectPro for Solved End-To-End Big Data and Data Warehousing Projects.

FAQs on Data Warehousing Projects

What is etl in data warehouse.

ETL (extract, transform, and load) is a data integration process that integrates data from several sources into a single, reliable data store that is then loaded into a data warehouse or other destination system. 

How to define business objectives for data warehousing projects?

For any data warehousing project, here are a few things you must keep in mind-

• the scope of the project,
• a data recovery plan,
• compliance needs and regulatory risks,
• the data warehouse's availability in production,
• plan for future and current needs, etc.

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Daivi is a highly skilled Technical Content Analyst with over a year of experience at ProjectPro. She is passionate about exploring various technology domains and enjoys staying up-to-date with industry trends and developments. Daivi is known for her excellent research skills and ability to distill

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Not all data are created equal; some are structured, but most of them are unstructured. Structured and unstructured data are sourced, collected and scaled in different ways and each one resides in a different type of database.

In this article, we will take a deep dive into both types so that you can get the most out of your data.

Structured data—typically categorized as quantitative data—is highly organized and easily decipherable by  machine learning algorithms .  Developed by IBM® in 1974 , structured query language (SQL) is the programming language used to manage structured data. By using a  relational (SQL) database , business users can quickly input, search and manipulate structured data.

Examples of structured data include dates, names, addresses, credit card numbers, among others. Their benefits are tied to ease of use and access, while liabilities revolve around data inflexibility:

• Easily used by machine learning (ML) algorithms:  The specific and organized architecture of structured data eases the manipulation and querying of ML data.
• Easily used by business users:  Structured data do not require an in-depth understanding of different types of data and how they function. With a basic understanding of the topic relative to the data, users can easily access and interpret the data.
• Accessible by more tools:  Since structured data predates unstructured data, there are more tools available for using and analyzing structured data.
• Limited usage:  Data with a predefined structure can only be used for its intended purpose, which limits its flexibility and usability.
• Limited storage options:  Structured data are usually stored in data storage systems with rigid schemas (for example, “ data warehouses ”). Therefore, changes in data requirements necessitate an update of all structured data, which leads to a massive expenditure of time and resources.
• OLAP :  Performs high-speed, multidimensional data analysis from unified, centralized data stores.
• SQLite : (link resides outside ibm.com)  Implements a self-contained,  serverless , zero-configuration, transactional relational database engine.
• MySQL :  Embeds data into mass-deployed software, particularly mission-critical, heavy-load production system.
• PostgreSQL :  Supports SQL and JSON querying as well as high-tier programming languages (C/C+, Java,  Python , among others.).
• Customer relationship management (CRM):  CRM software runs structured data through analytical tools to create datasets that reveal customer behavior patterns and trends.
• Online booking:  Hotel and ticket reservation data (for example, dates, prices, destinations, among others.) fits the “rows and columns” format indicative of the pre-defined data model.
• Accounting:  Accounting firms or departments use structured data to process and record financial transactions.

Unstructured data, typically categorized as qualitative data, cannot be processed and analyzed through conventional data tools and methods. Since unstructured data does not have a predefined data model, it is best managed in  non-relational (NoSQL) databases . Another way to manage unstructured data is to use  data lakes  to preserve it in raw form.

The importance of unstructured data is rapidly increasing.  Recent projections  (link resides outside ibm.com) indicate that unstructured data is over 80% of all enterprise data, while 95% of businesses prioritize unstructured data management.

Examples of unstructured data include text, mobile activity, social media posts, Internet of Things (IoT) sensor data, among others. Their benefits involve advantages in format, speed and storage, while liabilities revolve around expertise and available resources:

• Native format:  Unstructured data, stored in its native format, remains undefined until needed. Its adaptability increases file formats in the database, which widens the data pool and enables data scientists to prepare and analyze only the data they need.
• Fast accumulation rates:  Since there is no need to predefine the data, it can be collected quickly and easily.
• Data lake storage:  Allows for massive storage and pay-as-you-use pricing, which cuts costs and eases scalability.
• Requires expertise:  Due to its undefined or non-formatted nature, data science expertise is required to prepare and analyze unstructured data. This is beneficial to data analysts but alienates unspecialized business users who might not fully understand specialized data topics or how to utilize their data.
• Specialized tools:  Specialized tools are required to manipulate unstructured data, which limits product choices for data managers.
• MongoDB :  Uses flexible documents to process data for cross-platform applications and services.
• DynamoDB :  (link resides outside ibm.com) Delivers single-digit millisecond performance at any scale through built-in security, in-memory caching and backup and restore.
• Hadoop :  Provides distributed processing of large data sets using simple programming models and no formatting requirements.
• Azure :  Enables agile cloud computing for creating and managing apps through Microsoft’s data centers.
• Data mining :  Enables businesses to use unstructured data to identify consumer behavior, product sentiment and purchasing patterns to better accommodate their customer base.
• Predictive data analytics :  Alert businesses of important activity ahead of time so they can properly plan and accordingly adjust to significant market shifts.
• Chatbots :  Perform text analysis to route customer questions to the appropriate answer sources.

While structured (quantitative) data gives a “birds-eye view” of customers, unstructured (qualitative) data provides a deeper understanding of customer behavior and intent. Let’s explore some of the key areas of difference and their implications:

• Sources:  Structured data is sourced from GPS sensors, online forms, network logs, web server logs,  OLTP systems , among others; whereas unstructured data sources include email messages, word-processing documents, PDF files, and others.
• Forms:  Structured data consists of numbers and values, whereas unstructured data consists of sensors, text files, audio and video files, among others.
• Models:  Structured data has a predefined data model and is formatted to a set data structure before being placed in data storage (for example, schema-on-write), whereas unstructured data is stored in its native format and not processed until it is used (for example, schema-on-read).
• Storage:  Structured data is stored in tabular formats (for example, excel sheets or SQL databases) that require less storage space. It can be stored in data warehouses, which makes it highly scalable. Unstructured data, on the other hand, is stored as media files or NoSQL databases, which require more space. It can be stored in data lakes, which makes it difficult to scale.
• Uses:  Structured data is used in machine learning (ML) and drives its algorithms, whereas unstructured data is used in  natural language processing  (NLP) and text mining.

Semi-structured data (for example, JSON, CSV, XML) is the “bridge” between structured and unstructured data. It does not have a predefined data model and is more complex than structured data, yet easier to store than unstructured data.

Semi-structured data uses “metadata” (for example, tags and semantic markers) to identify specific data characteristics and scale data into records and preset fields. Metadata ultimately enables semi-structured data to be better cataloged, searched and analyzed than unstructured data.

• Example of metadata usage:  An online article displays a headline, a snippet, a featured image, image alt-text, slug, among others, which helps differentiate one piece of web content from similar pieces.
• Example of semi-structured data vs. structured data:  A tab-delimited file containing customer data versus a database containing CRM tables.
• Example of semi-structured data vs. unstructured data:  A tab-delimited file versus a list of comments from a customer’s Instagram.

Recent developments in  artificial intelligence  (AI) and machine learning (ML) are driving the future wave of data, which is enhancing business intelligence and advancing industrial innovation. In particular, the data formats and models that are covered in this article are helping business users to do the following:

• Analyze digital communications for compliance:  Pattern recognition and email threading analysis software that can search email and chat data for potential noncompliance.
• Track high-volume customer conversations in social media:  Text analytics and sentiment analysis that enables monitoring of marketing campaign results and identifying online threats.
• Gain new marketing intelligence:  ML analytics tools that can quickly cover massive amounts of data to help businesses analyze customer behavior.

Furthermore, smart and efficient usage of data formats and models can help you with the following:

• Understand customer needs at a deeper level to better serve them
• Create more focused and targeted marketing campaigns
• Track current metrics and create new ones
• Create better product opportunities and offerings
• Reduce operational costs

Whether you are a seasoned data expert or a novice business owner, being able to handle all forms of data is conducive to your success. By using structured, semi-structured and unstructured data options, you can perform optimal data management that will ultimately benefit your mission.

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You’re invited to our virtual event! Digital Equity/Inequity in Seattle: Learning from 2024 Seattle Technology Access & Adoption Study community research

Join us for a data discovery and discussion session as Seattle IT presents Digital Equity/Inequity in Seattle: Learning from 2024 Seattle Technology Access & Adoption Study presentation and discussion on Tuesday, May 21, from 3-4 p.m. with an optional extended conversation from 4-4:30 p.m. This virtual Webex event is an opportunity for us to share what we’ve learned from the report released this year. We will share the survey and focus group results, what this means for our community, and how to explore our dashboards and data. We’ll be joined by the City’s Interim Chief Technology Officer Jim Loter, Community Technology Advisory Board members, Digital Equity Advisor David Keyes, and research partners at Olympic Research and Strategy and Inclusive Data.

The study, conducted every five years, provides valuable data and insight on internet access and use, devices, digital skills, civic participation, training needs, and safety and security concerns. Results help guide City and community programs to better serve residents and close the digital divide. This study received input from 4,600 diverse Seattle residents, including Native community members, in eight languages.

Some of the results include:

• One in 20 households have fewer than one internet device per household member.
• 1 in 6 Native households dealt with internet outages of a month or more.
• Nearly 44,000 households have significant needs for improvement in access, devices, uses, and skills using a new digital connectedness index.
• 11% of BIPOC households do not have internet access both at home and on-the-go.

To learn more about the survey, including the full summary report, Tableau data dashboards, focus group results, and more, visit Seattle.gov/tech .

Join the virtual conversation through Webex:

Meeting link:  https://seattle.webex.com/seattle/j.php?MTID=m94ced8fb50bb4aba27a7c7cce778399c

 Click “Join from your browser” if you prefer to not install the desktop Cisco Webex

Join by phone : 206-207-1700  

Meeting number or Access code: 24843642790

One touch call: 206-207-1700,,24843642790#

Meeting password: CJpPucmR783

(Meeting password to use from phones and video systems: 25778267)

Join by video system

Dial [email protected]

You can also dial 173.243.2.68 and enter your webinar number

Browse the Archive

• Administering Oracle Fusion Analytics Warehouse
• Configure Oracle Fusion Analytics Warehouse Data

3 Configure Oracle Fusion Analytics Warehouse Data

As the cloud account administrator with the Functional Administrator or System Administrator application role, you specify the data load and reporting configuration details, and create data pipelines for functional areas that determine how the source data from Oracle Fusion Cloud Applications is loaded and displayed in the data warehouse.

Any data you load into the autonomous data warehouse in Oracle Fusion Analytics Warehouse is subject to the data access controls that may not always match those in the source system. For example, if User1 doesn't have the rights to access some data in your source system, then when you bring that data over to the autonomous data warehouse in Oracle Fusion Analytics Warehouse , this User1 can access that particular data. If you want to have the same controls on the data, then you must ensure that this particular User1 has the same data access rights as your source system in your Oracle Fusion Analytics Warehouse user setup.

• Typical Workflow to Configure Data

About Data Pipelines for Functional Areas

• About Data Refresh Performance
• About Pipeline Parameters
• Set Up the Pipeline Parameters

Create a Data Pipeline for a Functional Area

Edit a Data Pipeline for a Functional Area

Activate a Data Pipeline for a Functional Area

• About Global Parameters
• Set Up the Global Report Parameters
• About Reporting Configurations
• Set Up the Reporting Configurations for Enterprise Resource Planning
• Set Up the Reporting Configurations for Human Capital Management
• Set Up the Reporting Configurations for Supply Chain Management

Deactivate a Data Pipeline for a Functional Area

Delete a Data Pipeline for a Functional Area

• Refresh a Data Pipeline for a Functional Area

Reload Data for a Data Pipeline

• Reset the Data Warehouse
• Reset the Cache
• Reset and Reload the Data Source
• View Load Request History
• View the Audit Log
• View Records Rejected in Extraction
• Prioritize Datasets for Incremental Refresh (Preview)
• About Augmenting Your Data
• Create a Dimension Alias
• About Managing Data Connections
• Disable Data Pipeline
• Schedule Frequent Refreshes of Data
• Schedule Periodic Full Reload of Functional Area Data
• Schedule Frequent Refreshes of Warehouse Tables
• Extend Data with Custom Data Configurations