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Why You Should Use Event Stream Processing for Big Data

Story Highlights
  • Big Data Solutions
  • Hadoop Challenges
  • Event Stream Processing, the Alternative
  • Stream and Batch Processing

Big data was the most popular term in the past decade. So much so that managers, IT experts, and academicians overused it. Several ideas emerged as to its use but failed until people came upon event stream processing. In this article at Information Age, Roberto Bentivoglio talks about how event processing can be the answer to leveraging big data well.

Big Data Solutions

Hadoop is a distributed processing framework to control data processing and store big data. Yahoo! developed the open-source technology which Apache Software Foundation currently owns. Companies heavily invested and promoted Hadoop from the notion that it would maximize profits from big data.

Similar misconceptions were doing the rounds. Business leaders were optimistic about the efficiency of Hadoop in processing a large amount of data. Also, they thought that replacing legacy technologies would no longer be a pain. However, in recent years, experts pointed out the following issues:

Hadoop Challenges

  • While deployment models are maturing from standalone solutions to cloud-based services, Hadoop is not compatible with the cloud.
  • The framework cannot support machine learning.
  • Hadoop cannot match up to the requirements of advanced and real-time analytics.
  • Vendors relied on data lakes to nurture big data. With organizations depending on several repositories, the data extraction process was becoming expensive and time-consuming.

Event Stream Processing, the Alternative

Right off the bat, event stream processing provides several ways to optimize your big data usage:

  • Ability to manage multi-cloud architecture
  • Efficiency in deploying and controlling ML models
  • Capacity to process real-time data while working on historical data

To make Hadoop match up to its competitor, the vendors added streaming frameworks like Apache Stream and Apache Spark Streaming. It was counterproductive because architects and developers now had to handle more computational Hadoop engines. Other suppliers were using bound and unbound data sources by executing stream engines for batch processing.

Stream and Batch Processing

While you cannot run an event processing framework on batch processing, the latter can. Stream processing can go through each row, create an event, and process your big data. Whereas, batch processing can work only if you provide it with several events. Here are the benefits event stream processing provides for big data optimization:

  • You can work on bound and unbound data.
  • Processing data with high throughput even at low latency is possible.
  • Event stream processing comes with various processing semantics.
  • It allows you to handle heterogeneous data in a decentralized way to enable upgrading the systems horizontally.

While the event stream processing framework might be more user-friendly, it is not easy to leverage. To get the best out of it, equip yourself with self-service applications and best practices.

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