The 4 Benefits of Hybrid Design Patterns for Data Visualization

Data visualization is a godsend for business users. Presenting data graphically accelerates the business’s ability to spot patterns, understand trends, and gain rapid, actionable insights.

It’s no wonder that data visualization is projected by Gartner to grow by nearly 10% annually. But the usefulness of data visualization also means you’re getting more requests for data access. You need to be responsive to your customer’s needs, which includes keeping the data used in visualization tools fresh and relevant.

Answering the “fresh and relevant” need is where the challenge begins. Vendors claim to have tools that will make data access easier, faster, and more meaningful. In truth, however, you likely already have some tools you need to enable your users to create meaningful dashboards, charts, and views.

It isn’t new tools you need to evaluate. Instead, it’s time to re-evaluating your approach to data loading, access, and democratization by combining very common tools – ETL and APIs.

Examining ETL and APIs for Data Visualization

Feeding data into visualization tools typically uses one of two toolsets.


ETL – or Extract, Transform, and Load – is the tried and true way to move data. Nearly every business has an ETL solution in their toolbelt and it’s a familiar skill within IT groups.

The modern demands for data reporting strain what ETL can do. It’s very good at moving large amounts of data between a target and a source system, making it the perfect choice for initial data loads. Unfortunately, it’s not a flexible solution, not ideal for small, frequent updates, and tends to be an “all or nothing” process – either the load completes smoothly, or it fails.


APIs are the cool, new data loading solution – at least when compared to ETL. Using APIs for data integration extends a company’s path to broad API enablement. You probably already have APIs aggregating data from multiple sources that can be reused to load data into a visualization tool. But API’s best feature is also its biggest shortcoming – transactional movement of data. The transactional nature of APIs streamlines frequent updates of data. That same feature also makes APIs a poor choice for large volumes of data.

Each solution has its own strengths and weaknesses. But together – Traditional ETL + Runtime APIs – they combine the best of both worlds.

The 4 Benefits of Leveraging a Hybrid Approach for Data Visualization

Putting together these two powerful tools gives you a flexible path to meeting the business’s thirst for more data access and better insights, without expanding the organization’s tool fatigue. Adopting a hybrid approach to data integration offers 4 strong benefits.

Flexibility: Different business processes have different data needs. Some need updates in near real-time. Others can wait days for updates to occur, but when it happens it involves a deluge of data. A hybrid approach lets you apply the right integration pattern for the business need, and expedites your ability to respond to new requirements.

Reuse: You already have several ETL processes and API services that load and aggregate data. By combining the two integration patterns in a new hybrid architecture you can stop reinventing the wheel every time you need to implement a data visualization requirement.

Use existing tools: Reuse results in a reduction in custom code needed. By extension, there are fewer processes and services to maintain, so you aren’t adding to your technical debt.

Use existing skills: By leveraging existing integration patterns, you avoid ramp-up time and training on new tools. You’re not increasing the complexity of your toolset, and teams can use their existing knowledge of familiar solutions to implement new data visualization solutions.

Creating a Strategy to Hybridize Data Access For Visualization

To use an ETL / API hybrid approach for keeping the information in your data visualization tool fresh you’ll need to start by understanding your business requirements. Identifying data sources, how much data must be moved, how often, and when allows you to identify the best pattern for a requirement.

From there, the next step requires a review of the tools and infrastructure being used on that data. How will you move it into the visualization tool? Is the data pulled together and exposed with an API, or is there a database that’s accessed to load the information?

The third step in defining your strategy is to have an inventory of your existing ETL processes and APIs. This not only aids standardization, but it accelerates the implementation of new requests.

That inventory is key to the next step – discovering gaps and building the missing pieces. You can then build out your target architecture for your hybrid solution.

Want to refresh how your data is viewed? Reach out to Big Compass to help strategize your new hybrid architecture to make your data easy to understand that result in actionable insights.