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Data Warehouse Best Practices — preparing your data for peak performance

From fourteenth-century Genoan merchants tracking months-long trading missions to your local daycare predicting customer volume over the holidays, smart business practices demand ledgers full of data. As technology advances and your business scales up, you will require increasingly advanced solutions to remain competitive in the data landscape. One of the best modern tools for maximizing the observability and analytic power of your data is the data warehouse. This article will guide you through data warehouse best practices and illustrate how to get the most value from your data warehouse.

If you are not already familiar with a data warehouse, be sure to first read our introduction to data warehouses for a good foundation in the concept. To quickly review: data warehouses are high-powered repositories for collecting, contextualizing, and clarifying data from complex sources. They are designed to help you make informed business choices while retaining and distributing valuable insights throughout an operation.

As useful as they are, data warehouses don’t pop out of the box fully formed and ready to power up profits. This article provides a checklist to supplement your understanding of data warehouse development, along with tips on keeping the implementation and design of your data systems friction-free from the beginning.

Step 0: understand your use case

Before anything else, take the time to understand how improving the observation of your data will increase revenues or lift key metrics. Throughout your data warehouse development process, it’s important to keep in mind your expectations of how your business will be improved by your data warehouse design and implementation. This is especially vital in your initial planning stages.

A strong understanding of the fundamentals of data warehouses is useful for finding out how you can benefit from a warehouse. Common reasons for developing a data warehouse include:

  • Automating and expanding data analytics
  • Unifying and sanitizing data from multiple, potentially complex sources
  • Segmenting front-facing production from resource-intensive internal operations
  • Reducing current or future technical debt with a data system designed to scale
  • Expanding data access to internal teams and reducing the technical knowledge required to gain insight

In general, data warehouses increase available data insights, reducing the labor costs of those insights while increasing strategic organization to provide across-the-board efficiency boosts. You can likely find a way to apply these advantages, but it’s also worth considering other tools that you can use to accomplish the same goals. Before jumping into a data warehouse, consider similar solutions at a smaller scale (a relational database) or a larger scale (big data). You know your business best — take the time to find the right tool for the job.

Step 1: build a data model

If you’ve decided that a data warehouse is the right tool for your business context, it’s time to cultivate a robust understanding of your data space. A data model is a document that describes your varied data sources and their connectivity and dependencies. It is vital to be comprehensive in modeling your data, as this leads to good database and data warehouse design, synchronizes different teams to get the most from data systems, and can help to avoid costly retraced steps down the line.

The time you spend building a comprehensive data model for your warehouse will pay itself off several times over. By reflecting on how different datasets relate to each other, you will be able to better standardize, collate, and interpret that data. A data model should also allow you to better anticipate the expected inputs and outputs of the model — that is, the specific expectations of the analysis produced by the warehouse.

Documenting your data model has additional tangential benefits. Many people also find that they gain a new understanding of the complexity of their data space while documenting their models. A solid data model can also help coworkers generate related artifacts, like API documentation or sketches of marketing funnels. Even if you find you do not need a data warehouse after building a data model, you may find that constructing the model was itself worth the effort.

Step 2: draw your flow diagram

A common method for rendering your data model is a format like a data flow diagram (DFD). Tried and true, the DFD is a high-level flowchart that allows you to record your own understanding of the data system you are instrumentalizing while also creating clear documentation that’s legible to non-experts.

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From PowerPoint to diagrams.net, there are a variety of tools for creating a DFD.

Building a warehouse also requires an understanding of potentially diverse data sources. By enumerating your inputs and their locations in the company data structure, you are preparing yourself for when the time comes to gather all those threads in the warehouse.

At the same time, visualizing the flow of data through your company enables you to design changes. One of the best practices in data warehouse design is pruning messy data flows to reduce technical debt while you are improving your data infrastructure. A DFD is a valuable step in this process, enabling you to visualize the process for yourself and helping you to convince others of the benefits of retooling their data systems.

Step 3: define the nuts and bolts of your warehouse architecture

Once you have both a good view of your data and concrete expectations of your data warehouse system, it’s time to think about the actual implementation of your warehouse. Data warehouses are an established and mature technology; as such, there are a variety of different warehouse products that tinker with the details to deliver different benefits and specializations. Below, we will outline some of the common decision points in data warehouse design that you should keep in mind as you select a particular warehouse system.

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A generalized diagram of data warehouse design

Incorporating uncertainty in your integration layer

Your data warehouse starts with a set of (likely) relational databases, which are ready to be centralized into the data system. An important component of your data warehouse is the interface that funnels these diverse sources into your loading systems and eventually the warehouse. In order to cover varied data sources and scale well into the future, this integration layer should be completely source agnostic, accommodating read operations on any of your existing data sources and responding well to any data sources you could imagine adding.

ETL versus ELT — some loading considerations

Once properly sourced, data has to be sanitized and stored in your warehouse system. There are two schools of thought here: ETL (extract, transform, load) and ELT (extract, load, transform).

ETL

A more traditional version of loading, ETL transforms — sanitizes, joins, and otherwise prepares — the data on a dedicated server before it enters the warehouse and is made accessible to the data user. ETL used to be a universal standard, as the computer systems running warehouse software would be too bogged down by the transformation process to operate efficiently. ETL is still useful when computational resources are limited, vast on-site resources are available, or the data transformation stage is especially computationally expensive. Additional demands for ETL come from legal requirements to protect data or any requirement that certain data should never be exposed to warehouse users.

ELT

In cloud-based data warehouses, ELT is a preferred loading system. Loading data into the warehouse first and then doing transformations on the fly makes the warehouse more responsive and flexible to the end user. This has much higher computational costs, especially in terms of sudden spikes in resource demand. Due to massive increases in scalable cloud technology, it is increasingly possible to accommodate ELT pressures in modern warehouse systems. For users with neither on-site resources nor unusual transformation demands, ELT is usually a better approach to loading.

Data deployment and resources

Like the split between loading procedures, the actual warehouse resources are another consideration for your architecture. If you have available on-site resources (including tech support) for your loading and warehouse servers, so much the better! In other cases, it might be worth considering moving toward a scalable cloud solution that can support your needs out of the box. On-site solutions allow you control over your own data, resource costs, and security considerations. On the other hand, cloud solutions are generally easier to implement and offer a more flexible approach for small-scale operations.

Step 4: researching your data warehouse

Now is a good time to review your data model and reflect on who you expect to be using the data warehouse. Before looking at popular options for warehouses, think about usability and integration with the diversity of teams you expect will be looking for value in the warehouse. Not everyone has the technical knowledge to integrate with complex warehouse systems, and not all data is meant to be shared. A good warehouse system will handle these concerns. Part of finding the right fit is matching your various teams and data demands to the tool you select.

Diving deeper into data storage

Applying data warehouse best practices is one thing, but understanding the complex and ever-updating landscape of data storage technology is another. Be sure to stay up to date with other articles from our learning center.

https://www.rudderstack.com/learn/data-warehouse/data-warehouses-vs-data-lakes

https://www.rudderstack.com/learn/data-warehouse/data-warehouse-vs-data-marts

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