5 Data Processing Frameworks For Businesses In The Information Age

The evolution of big data impacts every business and customer. By 2020, we are expected to have over 44 trillion gigabytes of information in the digital universe. Information is ballooning to incredible volumes, and to be useful to business owners, it must be transformed into something meaningful. Storage is not enough. Business leaders who use data must be able to harness it in innovative ways to create unique insights.

In order to utilize this data, automation and artificial intelligence have thus become irreplaceable parts of every business. They must have the power to process, mine, and create data in a way that informs business leaders and researchers. Data processing frameworks are a necessity, with Hadoop, Spark, and other solutions providing much-needed personalization. Many are open source and in a constant state of evolution. These are five of the best data frameworks for business:


Hadoop is so widespread that it’s become the default framework for enterprise businesses. It has a massive range of tools that make processing remarkably easy. It’s known as one of the best data processing frameworks in the world today, partially thanks to the power of its distributed file system, which stores and streams information in clusters, even if your business uses thousands of servers. This computation allows growth on demand. Data crunching can occur on physical nodes, improving speed and simplicity. It uses Apache Hive to work with large datasets and indexes that quicken the processing pace. The Pig platform is responsible for analytics and optimization.


Spark can work alongside Hadoop if your company uses it instead of MapReduce. This way, you gain the benefits of two groups of tools. Spark transforms data and computes results on resilient distributed datasets. Its APIs are primarily written in Scala, but it also supports getting started with Java and Python, so if your information technology training includes traditional languages, it will serve you well. Spark Streaming lets you process raw data without split second latency, while its machine learning technology can learn algorithms. It also maps relationships in graphs.


Flink can manage batch and real-time processing through its streaming data flow engine. It has a wide range of tools including APIs for Java, Scala, and Python for companies. It might seem to compete directly with Hadoop, but it primarily goes head to head against Spark. Flink offers streaming algorithms, just as Spark does, but the latter only approximates stream processing. Flink can do its job without the help of Apache Storm and similar tools. It manages analytics in clusters and handles iterative processing on the same nodes, improving speed. Its batch processes equally reliable, so it’s a necessity if you’re processing business data in real-time.


Apache Storm focuses on the real-time processing of unbounded streams in any programming language. It handles machine learning with ease and integrates into Hadoop. The deployment process is labor-heavy, but once that’s managed, it’s incredibly easy to use. Trident provides an abstraction layer that lets you support batches. Storm offers real-time analytics and machine learning. Unlike Hadoop, business data is streamed and dynamic, but the framework is comparatively slow and is designed to operate online. It must also be implemented in Clojure, so it requires a specialized in-house team.


Apache Samza has an intuitive API that’s similar to MapReduce. It can handle huge amounts of big data analysis and migrates information when it encounters a fault. As a stream-only framework for companies, it’s highly flexible and designed to extract meaning from complex interactions via Apache Kafka. Streaming is accomplished using related data, which is partitioned during distribution. Kafka replicates data storage via an affordable multi-subscriber model, but its recovery of an aggregated state is inaccurate. Samza is thus ideal for streaming if you already have Hadoop and Kafka to support it.

Hybrid processing for business makes batch and stream processors possible, enhancing benefits and minimizing deficits. There will never be a single data processing framework for all businesses because they’re designed to be used together. Data management is a science as much as it is an art. The best combination of frameworks is one that gives you speed and scalability that exploits the skills of your existing IT team.

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