Spark Vs. Flink: Comparing the Top Stream Computing Engines

Companies, governmental bureaus, agencies, and organizations rely upon stream computing to process and analyze big data. In modern data analytics, stream processing is becoming extremely important in aiding analysts to uncover previously undiscovered insights much more quickly than traditional batch processing.  Benefits of stream processing can include real-time analysis reports and the ability to filter data before storage. Receiving information faster for critical decisions and lowering storage costs can easily justify an investment in the best stream computing engine. Currently there are two Apache projects that compete to dominate this space: Spark and Flink.  For Onyx, Spark, with its more mature ecosystem and larger install base, was the clear choice. But first, let’s perform a very high level comparison of the two.

Compare Spark Vs. Flink Streaming Computing Engines

Apache introduced Spark in 2014. In early tests, it sometimes performed tasks over 100 times more quickly than Hadoop, its batch-processing predecessor. Known primarily for its efficient processing of big data and machine learning algorithms over distributed architectures, Spark grew to incorporate stream processing later. Apache Flink however, was built from the ground up to process streaming data. The newer rival arrived on the data science scene in 2016 and appeared to have a better stream engine and more supported options, such as the ability to work with Storm topologies.

During processing, both products operate on their data payloads in-memory and do not persist to resting datastores-while they are perfectly capable of writing to permanent storage, the point of stream processing is to keep data in memory and use it then and there.  Moreover, both are fault tolerant and designed to scale to data of enormous size and scope.  This helps data engineers, software engineers, and data scientists write big data programs with streaming data.  After mapping the data, they can plug in machine learning algorithms to form some kind of predictive or classification model.

Perhaps because it has been around longer, Spark still has a larger user base in production and more funding, though this is changing, especially where funding is concerned. Also, Spark upgraded to a continuous processing model that works more like Flink's stream processing, so that may help close the performance gap. Both products can handle very large amounts of data with multiple kinds of processes. The choice between Spark and Flint may depend upon the system's intended use, subtle differences between these two major stream processing platforms, or even a preference from using a more time-tested product.  Ultimately, it depends on client requirements, and which product helps our customers meet those requirements most efficiently and effectively.

Onyx and Streaming: The Neverending Story

Our customers have challenging analytical problems that span a variety of problems sets, each of which have benefitted greatly with the introduction of stream processing technologies.  Whether it be analyzing security systems and other platforms to detect real time insider threats, evaluating financial transactions in the interests of determining potential fraud, or inspecting transportation logistics-oriented data frames to understand the most efficient path between two points, Spark’s streaming analytics capabilities have reaped great rewards.

At a very high level, we leverage a specific technology stack which includes, but is not limited to, the combined use of Apache NiFi, Kafka, and Spark to ingest and perform transformation, processing, and analytics on data in real time.  The flexibility of the technologies chosen allow us to also perform a variety of other ML oriented analytical workloads as well.

Have questions about the above for your project? Trust the experts at Onyx Government Services to evaluate and recommend the best solution for your business needs. Contact us today to start the conversation.



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