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How to Think About Data Warehouse Design

Brenna Buuck
By Posted in Engineering June 1, 2022

Data warehouses facilitate an extremely valuable aspect of every modern business: data analytics. And like every aspect of the modern business, technological evolution has made today’s data warehouse a sophisticated, streamlined operation, allowing organizations to achieve incredible results from data analysis initiatives. But as organizations plan data warehouse initiatives, they’ll quickly find that each data warehouse is going to be unique, requiring a healthy level of preparation, design, and due diligence. 

This piece addresses some of the most common design considerations an organization should consider as they develop their data warehouse. We’ll explore three distinct phases: architecting a data warehouse for your business, designing the data warehouse, and rolling out the data warehouse. By understanding these unique phases of the data warehouse journey, organizations will be equipped with a foundation to build more robust, operationally efficient data warehouses.

Architecting the Data Warehouse to Your Business

Before diving into design and development, it’s essential to understand the scope of design considerations and business considerations that influence the design and implementation of your data warehouse. 

Here, iterative design, including highlighting business needs, aligning those needs to functional requirements of the data warehouse, and achieving key stakeholder buy-in from both technical leadership as well as business leadership, are all critically important while bringing your unique data warehouse from concept to implementation. In the following sections, we’ll explore some of the key design steps that are critical as you embark on your journey to data warehouse design. 

Setting Up Your Workspace

Building an effective data warehousing strategy typically involves creating three environments — Development, Testing, and Production. This partitioning is key to building healthy data warehousing processes as it allows organizations to build, validate, and run key procedures within the data warehouse pipeline, increasing the likelihood that bugs, errors, or misconfigurations are identified early in the development process and eliminated from production. Remember that every one of these development stages will also include the data warehouse processes such as staging, data integration, data visualization, and other key data warehouse processes.

Development Environment

The development environment is designated for developing the data warehouse implementation. This environment is known to be in a constant state of flux, where development initiatives are implemented and validated to align with design goals and security constraints. Running a development environment provides organizations with a partitioned environment to explore features and new designs without influencing the dedicated environments of Testing and Production.

Testing Environment

The testing environment is used to test new features instantiated in the development environment. Here, you can run tests against common use cases and fringe use cases to identify when and where errors propagate in newly introduced features or design changes committed by the development team. At this stage, it’s critical that the data warehouse not only functions in a manner that aligns with the organization’s unique requirements but also adheres to the security requirements and initiatives enacted by the organization.

Production Environment

The production environment is the last stage in developing a well-rounded data warehouse and is reserved for running production operations. Here, any changes introduced from the Development or Testing stages have been vetted and validated, and are introduced into production in a manner that can be highly monitored to ensure data integrity can be tracked and production errors can be contained and accounted for easily. 

Data Engineers Handbook for Snowflake

Designing the Data Warehouse

Not all data warehouses are designed to be the same. Based on different business requirements and technical parameters, each business organization is going to take a unique approach to designing and deploying its data warehouse. In the following section, we’ll explore some of the common design considerations taken into consideration when designing a data warehouse, providing organizations with a valuable blueprint for productivity.

Data Warehouse Modeling

Data warehouse modeling is the first ideation stage of data warehousing dedicated to outlining the various components, data warehouse schemas, databases, tables, content within the tables, ETL strategy, OLAP strategy, visualization strategy, and design features required of the data warehouse. This modeling phase isn’t as critical as it was in the past with the advent of cheap storage, however it can be an opportunity to formalize and collect business requirements and allow key technical and non-technical stakeholders to validate the design approach. 

Data Extraction, Transfer, and Loading Process Design

Data integration is a technique used to pull data from multiple sources, clean, transform it, and make that data available for analytics. Traditionally, designing a data integration pipeline is partitioned into stages known as Creating a Reference Dataset, Extraction, Validation, Transformation, and Staging. 

Creating a Reference Dataset

Creating a reference dataset defines what data types in what formats are permissible to receive from a source dataset. This rule set acts as a filter of what data can be accepted and what data will be removed from the data pipeline.

Extraction

Extraction is the stage dedicated to extracting data from the data source. Here, data sources can come in various formats including XML, JSON, Parquet, Avro, and CSV files. 

Validation

Once data is added to the validation stage of the data pipeline, it can be validated against set constraints if they exist. For example, if you set up a field constraint which expects an integer, but the data being passed is a string it may suggest that the dataset contains errors and should undergo further validation. This stage acts like another filtering stage to ensure the right data is progressing in the data pipeline.

Transformation

The Transformation stage is designed to apply a series of processes to the dataset to be ready for loading into the data warehouse. Operations such as removing redundant data and checking data integrity are performed in the transformation stage.

Staging

Before the data is loaded into the data warehouse, it is often loaded into a staging database. This staging database mimics the schema of the data warehouse and can be used as a secondary copy of the data as well as a source database for rollbacks. That being said, many modern cloud data warehouses make rollbacks simple and easy to perform. Once the data is safely loaded into the staging database it can be copied to the data warehouse on the cadence defined by the organization. 

Designing the Front-end

All of the processes we’ve covered thus far, although critically important, only address back-end operations. On top of these technology stacks, there of course needs to be a front-end visualization component so that organizations can interact with the data warehouse. Also known as business intelligence tools or BI, these front-end visualization tools will present data managed in the data warehouse intuitively and insightfully.

Often, organizations will work with cloud BI tools that offer managed services and automatically connect to data warehouse tools. However, some organizations opt to design their data warehouse front-end in-house should they require specific customized functionality that isn’t available in the marketplace. 

Optimization

Implementing the data warehouse is half the battle. Now, it’s time to optimize. Optimization can happen in two levels, at the data level and at the query level. Among other techniques, data level optimization can be done by pre-aggregation or ingesting target data in a less detailed form which saves you the cost and time to compute later on. The most dramatic form of data level optimization is deleting or archiving old or inaccurate data before it ever hits your warehouse. 

The intention of query optimization on the other hand is to identify certain database structuring strategies that enhance the performance of the data warehouse. Here, there are many strategies that organizations can implement to enhance the querying performance of a given data warehouse; however, there are two common query optimization approaches that should be at the top of any organization’s list for data warehouse optimization.

Denormalization

One of the most computationally expensive operations to perform regarding data warehouse operations is the JOIN. Joining tables requires a considerable amount of pre-configuration and a decent amount of run-time operations, which simply means resource and time toll. To account for this, organizations should consider doing pre-joins or denormalizing data by creating redundant tables if it means removing a necessary JOIN operation. 

Sorting and Indexing

An often overlooked straightforward way to enhance data warehouse performance is by adding indexing and sorting to columns used as predicates in search queries. By introducing these features early in the process, subsequent query performance can benefit greatly. Further, keep in mind there’s no exact science to this. It’s valuable to test different methodologies to see how performance is impacted. One great way to instantiate some of these operations is to include these sorting tests in the test phase, then later introduce the optimized model to production. 

Rolling Out Your Data Warehouse

In an era when data warehouses can be stood up in minutes in the cloud it can still be important to consider post-deployment operations like design, testing, validation and optimization. This is because data warehousing is a continually evolving process and whether it’s through additional optimization designs or developing new use cases managing a data warehouse is always a foundational component of the data analytics journey.

By working with a smart data pipeline interface with a drag-and-drop approach to data pipelines, an organization can greatly reduce the operational overhead required to deploy and manage  data pipelines. Which means your data arrives in your warehouse more quickly with less overhead. 

How StreamSets Helps You Build Your Data Warehouse

With StreamSets, you don’t need to spend months planning your data warehouse migrations. With multi-table updates and a schemaless design, StreamSets smart data pipelines can migrate full data sets and data warehouses/data marts without needing to build the destination schema. Simply hit play and you can reliably build your new data warehouse in the cloud.

StreamSets supports many legacy data warehouse sources and modern data warehouse destinations for easy portability of data warehouse migration across hybrid and multi-cloud environments. 

Try StreamSets Free

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