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Internet of Things (IoT) and streaming data features.

If you are looking for an open source platform, Apache Hadoop is a good place to start for distributed storage and processing of large datasets. In addition, it offers services for data access, governance, security, and operations. It is a collection of utilities that facilitates a network of multiple computers and data sets on computer clusters built from commodity hardware to solve problems.

This solution is fundamentally resilient to support large computing clusters. Failure of individual nodes in the cluster is rarely an issue and if it does, the system automatically re-replicates the data and redirects the remaining ones in the cluster. It is a highly scalable platform that stores, handles, and analyzes data at a petabyte scale.

Why choose Apache Hadoop?

  1. Low cost. Since it is an open source platform, it runs on low-cost commodity hardware making it a more affordable solution compared to proprietary software.
  2. Flexible platform. Data can be stored in any format, parsed, and applied the schema to it when read. Since structured schemas are not required before storing data, you may even store data in semi-structured and unstructured formats.
  3. Data access and analysis. Data analysts have the option to choose their preferred tools as they can interact with data in the platform seamlessly using batch or interactive SQL or low-latency access with NoSQL.

15. Apache Spark

Apache Spark dashboard example

Apache Spark is a developer-friendly big data analytics platform that supports large SQL, stream processing, and batch processing. Like Apache Hadoop, it is an open source platform in data processing that supports a unified analytics engine for machine learning and big data.

To maximize this solution, you can run it on Hadoop to create applications that will leverage its power, derive deeper insights, and improve data science workloads in a single and shared database.

Consistent levels of response and service are expected with its Hadoop YARN-based architecture which makes the tool one of data access engines that work in YARN in HDP. This means the solution, along with other applications, can share a common dataset and cluster with ease.