Data Warehousing Guide: Everything You Need to Know in 2024

Updated August 28th, 2023
Data warehousing guide

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In this data warehousing guide, a comprehensive compilation of articles designed to deepen your understanding of data warehouses.

In today’s data-driven world, having a robust and efficient data warehousing solution is pivotal for making informed business decisions.

Whether you’re a beginner looking to understand the basics or a seasoned professional seeking advanced insights, this guide covers every aspect of data warehousing to provide a well-rounded view.

Each article within this guide has been carefully curated to offer actionable insights and valuable knowledge, equipping you with the tools to successfully navigate the complexities of data warehousing.

Table of contents #

  1. What is data warehousing?
  2. What is the primary purpose of a data warehouse?
  3. What is a cloud data warehouse?
  4. What are the benefits of cloud data warehousing?
  5. What is data governance in a data warehouse?
  6. How to choose the right data warehouse?
  7. How is a data warehouse different from a database, a data mart, a data fabric & a data catalog?
  8. Bottom line
  9. Related reads

What is data warehousing? #

According to Gartner, a ”data warehouse is a storage architecture capable of storing data extracted from various resources such as transaction systems, and relational databases.”

The process of setting up such a repository is data warehousing.

A data warehouse acts as a central repository for data aggregated from various sources. Data teams can use this data for analytics and BI. The data warehouse can be on-premise or cloud-native and mainly stores historical data.

According to Ralph Kimball’s book on data warehousing, warehouses emerged to tackle recurring business themes such as:

  • We have mountains of data in this company, but we can’t access it.
  • We need to slice and dice the data every which way.
  • You’ve got to make it easy for business people to get at the data directly.
  • We want people to use information to support more fact-based decision-making.

And if you want to learn more about the history of data warehousing, click here → The rise of data warehousing in the 80s!

What is the primary purpose of a data warehouse? #

Data warehouses are designed to feed information into decision support systems, business intelligence (BI) software, data dashboards, and other types of analytics and reporting tools.

The data stored in a given warehouse is formatted for a specific purpose, which is defined before engineers start modeling data and begin loading it into the warehouse.

As such, the primary purpose of a data warehouse is to enable an organization to easily access and analyze relevant data to extract key business insights and plan for the future.

Ultimately, data warehousing helps businesses improve their bottom line by enabling historical insight, ensuring data quality and consistency, boosting the power and speed of data analysis processes, and facilitating greater scalability across the entire organization.

Now, let’s understand what a cloud data warehouse is.

What is a cloud data warehouse? #

A cloud data warehouse is a type of data warehouse that is managed and hosted by a cloud service provider (CSP).

Because cloud data warehousing does not have the physical constraints associated with on-premise data warehousing (where physical servers must be purchased, set up, and maintained), it is significantly faster, cheaper, and more scalable.

Some of the most popular cloud data warehousing solutions available today include Amazon Redshift, Snowflake, Google BigQuery, and Microsoft Azure SQL Data Warehouse.

If your business is considering migrating its data warehousing to a cloud-based solution, it is critical to have a clear migration strategy in place.

This includes outlining why the business will benefit from the desired end state, selecting a cloud data warehouse that fits your needs, and determining how you will redesign your current data model.

Here is more guidance on how to handle the initial preparation and discovery phases to kick off a cloud data warehouse migration.

To learn more about data warehouse, its example and how the modern data warehouse fits into the modern data stack, check out this article → Data Warehouse: Definition, Concept, Components, and Architecture

What are the benefits of cloud data warehousing? #

Cloud data warehousing has several benefits to offer, such as:

  • Low cost of ownership in managing a cloud data warehouse
  • Better speed and performance with enterprise-grade security
  • Increased storage and compute capabilities
  • Scalability and elasticity to meet changing demands and analytics use cases
  • Backup and disaster recovery

According to The Forrester Wave: Cloud Data Warehouse, Q1 2021, the most common cloud data warehousing use cases are:

  • Customer analytics
  • AI/ML-based analytics
  • Vertical-specific analytics
  • Real-time analytics
  • Customer intelligence
  • Data science
  • BI acceleration
  • Data collaboration

And if you want to learn more about the evolution of cloud computing & big data, the cloud data warehousing tools available today and more;

Read moreCloud Data Warehouses: Cornerstone of the Modern Data Stack

Now, let’s understand how data governance comes into the picture of data warehouse with examples.

What is data governance in a data warehouse? Explained with examples #

Data governance in the context of a data warehouse refers to the overarching framework for managing, improving, monitoring, and ensuring the quality, security, and availability of data stored in the data warehouse.

Data governance helps ensure that data assets are formally managed throughout the enterprise, making it easier to share, understand, and protect information.

To illustrate that, let’s look at a few scenarios that show how data warehouse governance might work in reality:

  1. Data quality management
  2. Data security and privacy
  3. Compliance and regulation
  4. Metadata management
  5. Data lifecycle management
  6. Change Management

Let us look into each of the above scenarios in detail:

Scenario #1 - Data quality management #

  • An e-commerce company’s data warehouse collects information from multiple sources, including customer transactions, website analytics, and third-party market research.
  • The data governance team has implemented rules to ensure data quality, such as checking for duplicates and validating entries against known patterns. For example, ensuring email addresses are in the right format.
  • They also routinely monitor data quality metrics and conduct periodic audits.
  • When data quality issues are identified, they’re logged, investigated, and resolved according to established procedures.

Scenario #2 - Data security and privacy #

  • A healthcare organization holds highly sensitive patient data in its data warehouse.
  • Governance policies dictate strict role-based access control, meaning each user has access only to the data necessary for their role.
  • All data is encrypted, both in transit and at rest.
  • Furthermore, the data is anonymized to protect patient privacy. Regular security audits ensure these measures are effective and up to date.

Scenario #3 - Compliance and regulation #

  • A multinational financial firm must comply with various regulations, including GDPR and Sarbanes-Oxley.
  • They’ve designed their data warehouse governance to incorporate these rules.
  • This includes features like data retention schedules, which ensure:
    • Data is not kept longer than legally allowed, and
    • Mechanisms for honoring data subject rights, such as data deletion or portability requests.

Scenario #4 - Metadata management #

  • A media streaming company uses metadata extensively to recommend content to its users.
  • They have robust metadata management procedures in their data governance policy.
  • This includes standards for how metadata should be collected and formatted, and procedures for updating and maintaining it.
  • As a result, they can easily track the source, quality, and usability of their data.

Scenario #5 - Data lifecycle management #

  • An insurance company’s data warehouse holds years of claim data.
  • Their data governance includes policies for how long different types of data should be retained, and when and how data should be archived or deleted.
  • This includes making regular backups to prevent data loss, and running regular checks to identify and resolve data corruption.

Scenario #6 - Change management #

  • A rapidly growing tech startup is constantly adding new features, which often require changes to their data warehouse.
  • Their governance policy includes a clear process for managing these changes.
  • This involves:
    • Identifying the need for change
    • Evaluating potential impacts
    • Testing the change in a controlled environment, and
    • Finally, implementing it in the live warehouse, all while communicating clearly with all relevant stakeholders.

In each of these scenarios, the key to effective data warehouse governance is a comprehensive, well-documented set of policies and procedures, combined with regular monitoring and auditing to ensure compliance.

Data governance in a data warehouse is often part of a broader data governance strategy within an organization. It helps to maximize the value of data, minimize risks, and enable better decision-making.

Want to learn more about data governance in warehouse?

Then this one is a must-read for you → Data Warehouse Governance: Building a Strong Foundation for Quality, Security, and Compliance

How to choose the right data warehouse: An evaluation framework! #

Before we begin, make sure that you’ve mapped your business requirements and cloud data warehousing use cases. This will dictate the evaluation framework criteria for your organization. While mapping, you should also look at the essential compliance and security factors.

Now, let’s look at the six major characteristics of data warehouse solutions to evaluate any data warehouse platform.

Here are 6 key features to factor in during your cloud data warehouse evaluation:

  • Data warehousing costs
  • Elasticity
  • Volume
  • Data formats
  • BI Support
  • Pricing model

But, why do they matter?

Check out the article, to learn more about them and the best-in-class cloud data warehouses in 2024 → Best Cloud Data Warehouse Solutions!

How is a data warehouse different from a database, a data mart, a data fabric & a data catalog? #

Let’s understand how they are different.

Ready to dive in? Let’s begin!

#1 Data warehouse vs database: The difference #

A data warehouse is a specialized system designed to support analytical processing and historical data analysis.On the other hand, a database is a general-purpose system focused on real-time data management and transactional processing for operational applications.

Businesses and their data teams use both a data warehouse and a database to ensure they can efficiently manage their operational data while enabling in-depth analysis and decision-making based on historical data trends and patterns.

Learn moreData Warehouse vs Database: Differences, Features, and Use Cases

#2 Data warehouse vs data mart: The difference #

A data mart is a location within a data warehouse that focuses on a specific business unit, department, or project. It represents a smaller, more focused fragment of a company’s data warehouse.

Meanwhile, a data warehouse is a central data repository that holds large amounts of structured data from different areas of the organization. So, a data mart is a subset of a data warehouse.

Since a data mart is akin to a niche database within a warehouse, the data within data marts is structured, just like in a warehouse.

Learn moreData Mart vs. Data Warehouse: Should You Use Either or Both?

#3 Data fabric vs data warehouse: What’s the difference? #

A data warehouse is a type of data repository used to store large amounts of structured data from various data sources. On the other hand, a data fabric is a composable, flexible and scalable way to maximize the value of data in an organization.

Both data fabric and data warehouses are two concepts that are often used in the world of data management, and they serve different purposes and use cases.

Learn moreData Fabric vs Data Warehouse: Differences, Practical Examples & How They Complement Each Other

#4 Data catalog vs. data warehouse: The difference #

The main difference between a data catalog and a data warehouse is that most modern data platforms use data warehouses to store structured data and data catalogs to find, understand, trust, and use that data.

So, data catalog vs. data warehouse, how do both contribute to the composition of a data stack?

  1. The data catalog forms the access, context, and collaboration layer
  2. The data warehouse is part of the storage layer

Together, the data catalog and data warehouse help you store, find, access, interpret, and use the right data as and when you need it.

Learn moreData Catalog vs. Data Warehouse: Differences, and How They Work Together?

Bottom line? #

In this collection of articles, you have gained valuable insights and a clearer understanding of the multiple facets involved in data warehousing.

In this ever-evolving field, staying informed is crucial. Whether you’re planning a new data warehouse project or optimizing an existing one, the information contained in this guide aims to be both a starting point and a continued reference for your journey.

As the demands for data storage and analytics continue to grow, understanding how to manage and leverage a data warehouse effectively will remain a key competency for businesses of all sizes. Thank you for letting us be a part of your data warehousing journey.

And don’t forget to check out the articles listed below.

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