Apache Spark is an open-source framework used for large-scale data processing. The framework is made up of many components, including four programming APIs and four major libraries. Since Spark’s release in 2014, it has become one of Apache’s fastest growing and most widely used projects of all time.
Spark uses an in-memory processing paradigm to speed up computation and run programs 10 to 100 times faster than other big data technologies like Hadoop MapReduce. According to the 2016 Apache Spark Survey, more than 900 companies, including IBM, Google, Netflix, Amazon, Microsoft, Intel, and Yahoo, use Spark in production for data processing and querying.
Apache Spark is important to the big data field because it represents the next generation of big data processing engines and is a natural successor to MapReduce. One of Spark’s advantages is that its use of four programming APIs — Scala, Python, R, and Java 8 — allows the user flexibility to work in the language of their choice. This makes the tool much more accessible to a wide range of programmers with different capabilities. Spark also has great flexibility in its ability to read all types of data from various locations such as Hadoop Distributed File Storage (HDFS), Amazon’s web-based Simple Storage Service (S3), or even the local filesystem.