Spark natively has machine learning and graph libraries.Īpache Spark works in a master-slave architecture where the master is called “Driver” and slaves are called “Workers”.Using Spark Streaming you can also stream files from the file system and also stream from the socket.Spark also is used to process real-time data using Streaming and Kafka.Using Spark we can process data from Hadoop HDFS, AWS S3, Databricks DBFS, Azure Blob Storage, and many file systems.You will get great benefits using Spark for data ingestion pipelines.Applications running on Spark are 100x faster than traditional systems.Spark is a general-purpose, in-memory, fault-tolerant, distributed processing engine that allows you to process data efficiently in a distributed fashion.Inbuild-optimization when using DataFrames.

Can be used with many cluster managers (Spark, Yarn, Mesos e.t.c).

Distributed processing using parallelize.

Spark – Default interface for Scala and Java.

Below are different implementations of Spark. Apache Spark is a framework that is supported in Scala, Python, R Programming, and Java.

Design spark tutorials