What are Data Warehouses?

The data warehouse is one of the most important components of an enterprise data stack. They’re specifically designed with business intelligence (BI) use cases in mind. Companies often use data warehouses to consolidate large volumes of data for big data analytics, visualization, and reporting. By comparison, databases are built more for storing everyday operational data. And data lakes are typically used to capture raw data for preliminary evaluation, rather than in-depth analytics. It’s common for organizations to use all three types of data repositories given their different capabilities.

Specifically, data warehouses are useful for studying consumer behavior, application usage, and market trends. They enable strategic decision-making and help identify untapped opportunities. They can also prepare data for more cutting-edge artificial intelligence and machine learning (AI/ML) applications. And, they’re often the foundation for innovation and enterprise-wide understanding of business operations.

How Do Data Warehouses Work?

Data warehouses ingest data from other sources, like relational databases and transactional systems, on a consistent schedule. Data analysts can then access this information in a number of ways – through SQL clients, BI tools, and other analytics platforms. The data that users need to access frequently is kept in fast storage, while all other information is kept in more cost-effective object storage.

They’re typically architected with three tiers – a front-end for reporting and analytics, a middle tier for the analytics engine, and a bottom tier for loading and storing data. They may also consist of multiple databases organized into tables and columns, with each column containing a description of the type of data stored there.

How a data warehouse integrates into the broader data ecosystem depends on the unique needs of the business. For some, it makes sense to capture data in a data lake first for preparation before moving any information to a data warehouse. Others may prefer to have data fall directly into data warehouses. In either case, information stored in them can flow into ML services or other analytics applications.

On-prem vs. Cloud Data Warehouses

Many organizations today implement data warehouses on the cloud rather than on-premises. The cloud offers several advantages. First, companies can easily scale data warehouses with demand, avoiding unused capacity or performance bottlenecks. Cloud providers will typically help manage them with automatic software updates, patches, and backups. Furthermore, reputable cloud providers like Amazon Web Services (AWS) make it easy to build robust disaster recovery processes and redundancy into critical data infrastructure.

On the downside, there is always risk in trusting third parties with key architectural components. Should a provider’s data center or region go down, any customers who rely on that infrastructure may have little ability to recover themselves. There’s also complexity involved in integrating data warehouses with other important services. As the business changes, cloud engineers must continually ensure the architecture is configured correctly for the company’s unique needs.

Fortunately, AWS has data warehouse solutions that give IT teams tremendous flexibility around how they ingest and use data at scale. These solutions integrate seamlessly with other data processing and machine learning services, opening up incredible opportunities for innovation.

AWS Data Warehousing

AWS’ purpose-built data warehouse service is called Amazon Redshift. Redshift can handle near real-time analytics and complex queries across enormous data volumes. It comes with a serverless option and doesn’t require ETL depending on the specific implementation. Redshift also integrates with services like Amazon SageMaker for deeper machine learning use cases and Amazon QuickSight for BI needs.

Outside of Redshift, AWS provides plug-and-play solutions for ingesting streaming data through services like Amazon MSK and Amazon Kinesis, as well as integrations with leading data clouds like Snowflake. So, not only is Redshift a high-performing and cost-effective data warehouse, it works well with many of today’s most popular data solutions and data management practices.

Maximize Performance with ClearScale and AWS

Even with this understanding of how data warehouses work and what AWS provides, it’s hard to know how to implement them effectively for specific use cases in a broader data management ecosystem. Working with an AWS Premier Tier Services partner like ClearScale can help solve this problem.

Since 2011, ClearScale has helped companies overcome challenges and unlock new data capabilities on AWS. Data warehouses, in particular, have become increasingly important for modern businesses as big data technologies have improved. Our cloud experts know how to design data warehouses on the cloud in support of long-term enterprise objectives.

For example, we worked with an enterprise forms automation provider to implement a new data warehouse and reporting layer. The client wanted to understand customer behavior in greater depth and update its data collection, storage, and reporting capabilities. We set up a data lake, a data ingestion layer, and Amazon Redshift for users to query structured and semi-structured data. With this revamped data infrastructure, our client was able to cut data management costs and conduct deeper analyses of user behavior.

Need to upgrade or set up a new data warehouse? Schedule a call today to learn more about your options and how ClearScale can point you in the right direction.