Learn how to – 3 Steps to Scaling Data Streaming in Your Enterprise

Learn how to – 3 Steps to Scaling Data Streaming in Your Enterprise.

Analytics platforms lie at the heart of every business these days. However, analytics are only as good as the data you feed into your models. Many companies choose to batch their data loads to prevent infrastructure stress. However, this approach limits real-time insights, something that is critical in a fast-paced environment.

Data streaming is the need of the hour, but setting up infrastructure and processes is challenging. Installing data streaming can seem intimidating as well. However, the truth is your company can ease into streaming by building a proof of concept first, followed by scaling the solution.

Here’s how you can build a data streaming PoC that works.

Understand why

Building a PoC might seem tedious at first since your solution might not be able to handle all of your organization’s needs. However, PoCs offer a host of benefits and allow you to scale into a streaming solution easily. For starters, PoCs help you deal with the large volumes of data organizations collect these days.

They expose gaps in data strategies and infrastructure. For instance, streaming PoCs will reveal problems in your ETL and upstream processes that depend on monthly or daily uploads. Streaming requires you to solve problems beyond your systems, and a PoC is a cost-effective way of discovering the limits of your systems.

PoCs also give you the room to fail forward cost-effectively. You won’t spend time building solutions that require huge investment only to discover they don’t address your needs. PoCs also help you understand the amount of stress your systems can take. 

For instance, if the constant stream of data overloads ancillary systems and increases costs, you can weigh those costs against the benefits real-time insights give you. For instance, your Spark-based ETL process might need a delta lake to hold your data, leading to increased costs. Thus, you can quantify your ROI and scale accordingly. You might also discover that the price of streaming is not worth it to your organization.

Make sure you communicate these benefits to the major stakeholders in your company to secure their buy-in. Building a PoC takes time and is a process that is riddled with failure. Buy-in from the right people ensures everyone in the organization understands that failure is the way forward in such situations.

Set the tone

Once you’ve secured buy-in, it’s time to set up the initial stages of your project. Let your team know the purpose of the PoC project. Modern dev teams are used to hitting goals that are tied to success metrics. For instance, delivering a product feature on time requires it to pass a battery of user tests successfully.

However, a PoC project is exploratory and doesn’t lend itself neatly to KPIs. The framework here is to build, measure, learn and scale. It’s best to start with a small team so that you don’t fall into analysis paralysis. Determine the resources you need and the dependencies you will create.

You must also communicate that failure is more than a possibility. After all, you’re learning how to do something for the first time and will make mistakes. Create a learning framework that removes the pain of failure. For instance, define success parameters and use them as scaling triggers. If a PoC fails those parameters, initiate analysis and learn from it. Build a new PoC with those lessons and repeat the process.

Most of all, let your team know why they’re involved in the PoC process. Once they understand the bigger picture, you’ll find them coming together to solve more problems and build better solutions.

Choose your tools

A problematic aspect of building a streaming PoC is choosing your tools. Most tools on the market are designed to work with large quantities of data, something that is unsuited to your needs. It’s best to choose a data ingestion and aggregation platform that has a friendly UI with SQL-enabled data retrieval.

This solution will help you analyze data quickly and pull analysis reports as needed. When choosing a data streaming platform, the popular choices such as the ones from Apache, AWS, and Google work well. Apache’s Kafka is probably the best choice, but this depends on what your organization needs and your PoC’s scale.

As always, start small and build on your successes. A common complaint against PoC work models is that they don’t introduce complex tools into your workflows, leading to lengthy onboarding times when you scale and go live with streaming.

This is true, but PoCs deliver major learnings in other areas. For instance, your teams will begin receiving reports in real-time and can process insights faster. You will begin expanding your PoC’s scope as a result. You’ll also receive insights into your data schema design and data duplication issues.

These issues will help you prevent costly production issues and minimize downtime. Another major lesson your DBAs will learn is that system rollbacks are not a solution in a streaming environment. Thus, you have the opportunity to install monitoring processes along with support personnel.

PoC is the way to go

Installing data streaming might seem intimidating, but the PoC method gives you an easy way of scaling and failing in a controlled fashion. The result is a better solution and robust processes that support your needs.