Apache Spark is described as 'Multi-language engine for executing data engineering, data science, and machine learning on single-node machines or clusters' and is a Cloud Computing service in the business & commerce category. There are more than 10 alternatives to Apache Spark for a variety of platforms, including Linux, Mac, Windows, SaaS and Web-based apps. The best Apache Spark alternative is Apache Hadoop, which is both free and Open Source. Other great apps like Apache Spark are Amazon Kinesis, ILUM, Apache Flink and Disco MapReduce.
Apache Hadoop is a open source software framework that supports data-intensive distributed applications licensed under the Apache v2 license. It enables applications to work with thousands of computational independent computers and petabytes of data.
Amazon Kinesis services make it easy to work with real-time streaming data in the AWS cloud.
Flink’s core is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations over data streams.
Disco is a lightweight, open-source framework for distributed computing based on the MapReduce paradigm and written in Python.
Apache Heron (Incubating) is a realtime, distributed, fault-tolerant stream processing engine from Twitter.
Apache Storm is a free and open source distributed realtime computation system. Storm makes it easy to reliably process unbounded streams of data, doing for realtime processing what Hadoop did for batch processing.
Object storage has been nothing short of revolutionary. S3 broke ground in 2006 with simple storage operations on named objects – and 18 years later, S3 Express One Zone even allows appends. But ultimately, object storage is all about blobs and byte ranges.
Proton is a unified streaming and historical data processing engine in a single binary. It helps data engineers and platform engineers solve complex real-time analytics use cases, and powers the Timeplus streaming analytics platform.
Upsolver is an In-Memory Data Preparation Platform. It removes the complexity from Big Data and Real-Time projects and shortens their implementation time from weeks/months to several hours, literally.
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