Why Data Streaming Planning and Architecture Helps to Prevent These Critical Mistakes
Everything in the world is cyclical, just like data streaming.
Use cases like streaming very large files aren’t new. However, as data becomes more and more important within an organization’s ecosystem, and as the real-time needs of businesses grow, data streaming is becoming more commonplace. In some cases, the ability to leverage the value of an organization’s data depends on powering its applications with streaming.
Looking at your technology needs today, you may not see an immediate need for real-time data usage. But IT leaders who lay the groundwork to accomplish data streaming will be ahead of the curve, whether it’s being used to address current needs or real-time data needs in the future.
Common Mistakes Made with Data Streaming
Successfully implementing data streaming requires first understanding the potential mistakes and knowing what plans must be in place to prevent them.
Using Data Streaming Without a Need
All of the new data streaming technology and publicity make it seem like a great solution to every data need. Unfortunately, when data streaming technology is used in situations where it’s not needed, it can add unnecessary complexity to integration or data agility solutions.
Full-fledged solutions like Apache Kafka make it possible to scale out an entire server cluster to handle large volumes of data. But in the end, if there is not a need for real-time data availability, you may not need to stream data in the first place.
Take, for example, a use case to use system event logs for business intelligence purposes, helping to identify trends for the organization’s operations, clients, and data. Those event logs can be delivered in real-time to the data store, but how often are the reports on the data run? For one of our clients, the desire was to use streaming, but the reality was that the data report was only run weekly. Engineering the solution to use data streaming would have been the equivalent of buying a Ferrari when all you really needed was a Chevy.
Lesson Learned: For some applications, a simple queueing system used to order and deliver these logs to the data store, rather than a real-time streaming solution, is a more appropriate choice. Queues help guarantee delivery of messages, and if messages stack up in a queue, it is not a big deal if there is no real-time requirement.
Underpowering Your Data Streaming Solution
Some needs end up on the opposite end of the spectrum. If you underestimate the amount of power you’ll need for data streaming, it will harm both your operations and your systems.
Streaming can require a lot of power from an infrastructure perspective. Not planning for the right amount of bandwidth or horsepower - to carry forward the previous analogy - is like putting a Chevy engine into a Ferrari. You won’t be able to get the right level of performance out of your infrastructure to power your data streaming or scale when the need arises, even though the solution looks like a Ferrari on the outside.
When planning for streaming, you can architect an environment with a single server that allows you to stream ten (10) interfaces well, but scaling that architecture would require more horsepower on the server or a clustered environment to accommodate horizontal scaling.
Example: Let’s say you have three (3) servers in a cluster, and that cluster is maxing out on compute power and memory with your current streaming demands. Additional servers are needed for the cluster every X number of interfaces streamed through the solution. It’s essential to plan to scale.
Lessons Learned: It’s important to architect your environment such that it can easily scale. You have a few options. You can use technologies like Kubernetes, even hosted on Amazon EKS, which can automatically scale across Pods. You can also plan to use a hosted streaming service such as Confluent or Amazon Kinesis to apply automatic scaling and not worry about the infrastructure by applying a serverless model.
Planning Prevents Data Streaming Mistakes
There are several considerations to keep in mind when you start planning your data streaming architecture so that you can prevent these mistakes.
Your streaming solution will need enough horsepower. That means you will need to plan to scale, and as a result, you will need to plan your future infrastructure budget to meet those demands.
For streaming implementation, take the time to design a solution that meets your real-time needs today while also planning to easily hook in new interfaces with transformations for new data flows through the systems.
Ensuring that you have the right expertise available for your infrastructure planning is also vital. With a serverless model, working with AWS or Confluent experts ensures a properly designed serverless streaming solution. If you plan to architect a solution within your own cloud environment, work with skilled cloud engineers, preferably on a provider like AWS, to create a solution that can scale automatically as your needs change and grow.
Every project should take governance into account. For data streaming, this planning should include:
- Inventory and management of servers and infrastructure
- Interface management
- Data access and security
- Compliance needs, such as PII or sensitive data
Prioritize Business Needs
Don’t be tempted to use streaming when it’s not needed. Ensure that the business requirements dictate that data streaming is necessary before you invest in the planning and infrastructure required to support this solution.
Streaming might seem like a new, shiny object, but we always remind customers: you need to use the right tool for the job. Sometimes, that means not using a data streaming solution.
If your business requirements dictate data needs that benefit from streaming and your budget aligns with that need, then it’s time to mobilize your resources and begin the design process for a real-time streaming solution. If you need help getting the process started or ensuring you have the right architecture and design, Big Compass is here to advise and guide you to a solid and sustainable data streaming solution.