apache spark what does it mean

What does apache spark mean?

Apache Spark is a powerful open source processing engine originally developed by Matei Zaharia Developed as part of his doctoral thesis at the University of California, Berkeley. The first version of Spark was released in 2012.

Apache Spark is a fast, easy-to-use framework that allows you to solve a variety of complex data problems, whether semi-structured, structured, streaming, or machine learning or data science. It has also become one of the largest open source communities in big data, with more than 1,000 contributors from more than 250 organizations, and more than 300,000 Spark Meetup community members in more than 570 locations around the world.

What is Apache Spark?

Apache Spark is an open source, powerful distributed query and processing engine. It offers the flexibility and scalability of MapReduce but at significantly higher speeds: it is 100 times faster than Apache Hadoop when data is stored in memory and up to 10 times faster when accessing disk.

Apache Spark allows users to read, transform, aggregate data, and easily train and deploy complex statistical models. Java, Scala, Python, R, and SQL all have access to the Spark API.

Apache Spark can be used to build applications, either packaged as libraries to be deployed on a cluster, or executed interactively via notebooks such as Jupyter, Spark-Notebook, Databricks notebooks, and Apache Zeppelin Quick analysis.

Apache Spark provides many libraries that will be familiar to data analysts, data scientists or researchers who have used Python's pandas or R language's data.frame or data.tables. It's very important to note that although Spark DataFrame will feel familiar to users of pandas or data.frame, data.tables, there are still some differences, so don't expect too much. Users with more background in SQL can also use the language to shape their data.

In addition, Apache Spark also provides several implemented and tuned algorithms, statistical models and frameworks: MLlib and ML for machine learning, GraphX ​​and GraphFrames for graph processing, and Spark Streaming (DStream and Structured). Spark allows users to freely combine these libraries in the same application.

Apache Spark runs conveniently on a local laptop and can be easily deployed in standalone mode on a local cluster or in the cloud via YARN or Apache Mesos. It can read and write from different data sources, including (but not limited to) HDFS, Apache Cassandra, Apache HBase and S3:

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