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Data Warehouses Explained

Do you manage multiple data sources? You may benefit from a data warehouse. What are their benefits, features, and limitations?

One of the decisions to make is whether to build a data warehouse from scratch or to use a data-warehouse-as-a-service (DWaaS) provider. DWaaS providers like Redshift, BigQuery, Panoply, and Azure tend to have lower infrastructure costs and include maintenance, allowing organizations to focus more on uncovering valuable insights in their business data, rather than on overhead expenses. The cost of implementing a data warehouse can range from a few hundred dollars to several million dollars, depending on the complexity of the data and the size of the organization.

In this guide, we will take a closer look at what a data warehouse is, the benefits it can bring, and some of the limitations to be aware of.

What is a data warehouse?

A data warehouse is a central repository for all of an organization's data. It is designed to bring together data from many sources and make it available to users and customers for analysis and reporting. Data warehouses are used by organizations to gain insights and make better decisions.

This data is typically stored in a structured format, such as a relational database, and is used for reporting and analysis. The data in a data warehouse is often sourced from multiple sources, such as transactional systems, log files, and external data sources. The data is then transformed and loaded into a warehouse for analysis.

The benefits of a data warehouse

  1. Improved data access and reporting: A data warehouse makes it easy for users to access and report on data from multiple sources. This allows organizations to gain a more comprehensive understanding of their data.

  2. Better decision-making: With all of an organization's data in one place, it is easier for decision-makers to derive insights and make better decisions.

  3. Increased performance: Data warehouses are optimized for reporting and analysis, which can improve overall organizational performance.

  4. Improved data quality: Data is typically cleaned and transformed before it is loaded into a data warehouse, which improves data quality. This is important because inaccurate data can lead to bad decisions.

  5. Better data governance: Data warehouses can help to improve data governance by providing a centralized location for data management and access control. This helps to ensure that data is accurate, up-to-date, and secure.

  6. Cost savings: Data warehouses can help organizations to save money by reducing the need for expensive data silos and manual data integration.

  7. Scalability: Data warehouses are designed to scale to meet the needs of growing organizations. This means that organizations can easily expand their data storage and processing capabilities as their needs change.

A diagram showing the components of a data warehouse

Data warehouse definition and explanation

What does a data warehouse allow an organization to achieve?

A data warehouse allows an organization to achieve a number of benefits. The most notable of these benefits is the ability to access and report on data from multiple sources in one place. This allows decision-makers to make informed decisions. Additionally, data warehouses are designed to handle large volumes of data and provide fast query performance, which can improve the overall performance of an organization.

Data warehouse architecture

The architecture of a data warehouse consists of a system of components that work together to store, process, and retrieve data for business intelligence and analytics. These components include a metadata repository, a staging area, sandboxes, and tiers.

The metadata repository is used to store information about the data in the warehouse, such as the structure of the data and the relationships between tables. This information is used to understand and organize the data in the warehouse, making it easier to query and analyze.

The staging area is a temporary location where data is stored before it is loaded into the warehouse. Data in the staging area is typically cleaned, processed, and transformed to ensure it is in a format that can be easily loaded and queried in the warehouse.

Sandboxes are used as a testing and development environment for new data and report designs. They are private, secure, and safe areas where users can experiment with new datasets or ways of analyzing data without affecting the production data in the warehouse.

Tiers are used to separate different types of data and improve performance. Typically, data is separated into tiers based on access frequency, with frequently accessed data stored in faster storage and infrequently accessed data stored in cheaper storage. This allows for optimized query speed and cost-efficiency.

Every organization's data warehouse architecture will differ based on requirements.

The limitations of a data warehouse

Data warehouses can be:

Expensive: Building and maintaining a data warehouse from scratch can be expensive, especially when dealing with large volumes of data.

Complex: The process of designing, building, and maintaining a data warehouse can be complex and require specialized skills.

Time-consuming: The process of loading and transforming data can be time-consuming, especially when dealing with large volumes of data.

Inflexible: The structure of a data warehouse is typically fixed, which can make it difficult to adapt to changing business requirements.

Limited: Data warehouses are optimized for reporting and analysis, which can limit their usefulness for other types of data processing tasks.

Data warehouse examples

These are some examples of data warehouse products:

Amazon Redshift: A cloud-based data warehouse service provided by Amazon Web Services (AWS). It allows users to store and analyze large amounts of data with ease and scalability.

Microsoft Azure Synapse Analytics: Formerly known as Azure Data Warehouse, it is a cloud-based data integration and analytics service provided by Microsoft. It allows users to store and analyze large amounts of data in a secure and scalable environment.

Google BigQuery: Google Cloud's data warehousing service that allows real-time analysis of large data sets using SQL.

IBM Db2 Warehouse: A data warehousing solution from IBM that provides secure and scalable storage and analysis of large data sets.

Oracle Autonomous Data Warehouse: A fully-managed and self-driving cloud-based data warehousing service from Oracle that enables the storage and analysis of large data sets.

Teradata Vantage: A data warehousing platform that allows users to store, access, and analyze large amounts of data in a secure and scalable environment.

Snowflake: A cloud-based data warehousing service that offers full management and scalability for storing and analyzing large amounts of data.

Is a data warehouse hard?

The purpose of a data warehouse is to house standardized, structured, consistent, integrated and pre-processed data, extracted from various operational systems in an organization, and to serve as an analytical tool to support decision making and reporting. Unfortunately, as data is manipulated and multiple copies of data are created for different purposes, the result is a very complex system that is incredibly difficult, time-consuming, and expensive for organizations to create and maintain.

Data warehouses, data lakes, and databases

Data warehouses, data lakes, and databases are all different types of data storage systems that can be used for data analysis and management. A data warehouse is designed for reporting and analysis, while a data lake is designed for storing raw data in its original format. A database is a general-purpose storage system that can be used for a variety of tasks.

It is important to note that data warehouses and data lakes can complement each other and an organization may use both to fit their specific needs. A data warehouse can be used to store and analyze structured data while a data lake can be used to store and process raw, unstructured data. On the other hand, a database is used to store, manage, and retrieve data and is used in a variety of applications.

To learn more about the differences, read Databases vs. Data Warehouses vs. Data Lakes.

Conclusion

It is important to evaluate different data warehouse options and consider factors such as scalability, performance, cost, and ease of use before making a decision. Additionally, it's important to consider whether a data warehouse will integrate with other tools and systems already in place within your organization.

In summary, a data warehouse can bring a number of benefits to an organization, including improved data access and reporting, better decision-making, increased performance, improved data quality, better data governance, cost savings, and scalability. By having a centralized repository for all of your organization's data, it allows users to access and report on data from multiple sources in one place, making it easier to gain insights and make better decisions.

However, if you need to access unstructured, raw data, instead of a data warehouse, it may be beneficial to use the MongoDB Atlas Data Lake.

FAQs

What are 5 components of a data warehouse?

  1. Data source
  2. ETL tools
  3. Data warehouse database
  4. OLAP tools
  5. End-user tools

Is SQL a data warehouse?

SQL (Structured Query Language) is a programming language that is used to manage and query relational databases. While SQL can be used to create and query a data warehouse, it is not a data warehouse itself.

What are ETL tools in data warehousing?

ETL stands for Extract, Transform, and Load. ETL tools are used to extract data from various sources, transform the data into a format that can be loaded into a data warehouse, and then load the data into the warehouse.

Who needs a data warehouse?

Data warehouses are typically used by businesses and organizations to gain insights and make better decisions. They are particularly useful for organizations that have large amounts of data spread across multiple sources and need to be able to access and report on that data in one place.

Who is responsible for creating a data warehouse?

Data warehouses are typically created by data architects, data analysts, and other IT professionals with specialized skills in data warehousing.

What are two basic types of warehouses?

Two basic types of warehouses are data warehouses and data marts. A data warehouse is a central repository for all of an organization's data, while a data mart is a smaller, focused repository of data that is designed to meet the specific needs of a particular group or department within an organization.

What is OLAP in data warehousing?

OLAP stands for Online Analytical Processing. OLAP tools are used to analyze data in a data warehouse and provide users with fast, multidimensional views of the data.

Which data warehouse is best?

The best data warehouse for an organization will depend on their specific needs and requirements. Some popular data warehouse options include Amazon Redshift, Microsoft Azure Synapse Analytics, Google BigQuery, and IBM Db2 Warehouse.