Spark is considered one of the best among ETL tools. Spark is a pretty quick and convenient way to conduct the ETL process, built especially for processing big data via clustered computing. Here are the main reasons for the importance of Spark for ETL compared to other tools.
Processing enormous amounts of data
It’s a perfect solution for large-scale data analytics because Spark can sort 100 terabytes of “raw” data in less than half an hour. Furthermore, it’s very convenient to integrate Spark with any file system. For example, there is the support of HDFS, S3, or MongoDB.
Reducing security risks
The multi-language engine supports different deployment types. The security level depends on the custom configuration. There are possibilities to turn on authentication and authorization on the web UI, configure SSL, and event log-in. At the same time, Spark is a private tool that isn’t deployed on the public Internet.
Effective teamwork in big data projects
Spark is also convenient for big enterprise projects, as it’s an easy-to-configure, fast, and versatile ETL tool. The engine demonstrates good speed and high performance, even processing a large amount of data for a big data science team.