Big Data Processing Tools.
Big Data processing technologies provide ways to work with large sets of structured, semi-structured, and unstructured data so that value can be derived from big data.
Let's talk about three open source technologies and the role they play in big data analytics—Apache Hadoop, Apache Hive, and Apache Spark.
Hadoop is a collection of tools that provides distributed storage and processing of big data.
Hive is a data warehouse for data query and analysis built on top of Hadoop.
Spark is a distributed data analytics framework designed to perform complex data analytics in real-time.
Hadoop, a java-based open-source framework, allows distributed storage and processing of large datasets across clusters of computers. In Hadoop distributed system, a node is a single computer, and a collection of nodes forms a cluster.
Hadoop provides a reliable, scalable, and cost-effective solution for storing data with no format requirements.
Hive is an open-source data warehouse software for reading, writing, and managing large data set files that are stored directly in either HDFS or other data storage systems such as Apache HBase.
Hive is not suitable for transaction processing that typically involves a high percentage of write operations. Hive is better suited for data warehousing tasks such as ETL, reporting, and data analysis and includes tools that enable easy access to data via SQL.
This brings us to Spark, a general-purpose data processing engine designed to extract and process large volumes of data for a wide range of applications, including Interactive Analytics, Streams Processing, Machine Learning, Data Integration, and ETL.
It takes advantage of in-memory processing to significantly increase the speed of computations and spilling to disk only when memory is constrained. Spark has interfaces for major programming languages, including Java, Scala, Python, R, and SQL.
The ability to process streaming data fast and perform complex analytics in real-time is the key use case for Apache Spark.
