5 Big Data Technologies to Watch Out For

5 Big Data Technologies to Watch Out For
5 Big Data Technologies to Watch Out For

The volume of data generated by companies today has reached record levels. Both structured and unstructured data are collected from multiple sources, but cannot be processed by traditional techniques. However, such big data can offer valuable information and insights for companies to grow their business and attract customers. In fact, worldwide revenue from Big Data is expected to touch $203 billion by 2020, according to IDC. And by 2022, this number is predicted rise to $274.3 billion.

Corporate training courses that include Big Data training can help companies develop effective business strategies, says iFuture Technologies, a leading provider of corporate training programs. Employee training and development also play a huge role in companies being able to retain talent in this intensely competitive world.

Here’s a look at the most popular big data analytics tools that should be part of corporate IT training.

  • Apache Hadoop

This is a Java-based open source software, capable of storing and analyzing massive volumes of data. It offers distributed computing and storage facilities to its users. It works by dividing one file into numerous blocks and stores the data across many nodes in a cluster. It can also offer high availability by replicating the data in a cluster. You can learn how to apply this tool through corporate IT training courses.

  • Apache Sqoop

Sqoop is an effective solution to handle data in a traditional database. It is capable of tackling huge amounts of data and can be used to transfer this data to Hive or Hadoop. Through export and import commands, it transfers datasets to Hadoop Distributed File System (HDFS). Along with data transfers, Sqoop also provides fault tolerance and parallel processing.

  • Presto

Presto is an SQL query engine, capable of handling data of sizes from gigabytes to petabytes. However, it is not designed for Online Transaction Processing. It offers fast response time and can be ready to use within a few minutes. Presto operates on clusters of machines. Queries from clients are submitted to the coordinator. This in turn plans how to execute the query and sends the processing information to the workers. Presto can deal with both traditional as well as other data sources, like Hive and Cassandra.

  • Hive

Hive is a distributed data management tool that makes operations like data encapsulation, datasets analysis and ad hoc queries easy. Since Hadoop programming deals with flat files, Hive improves performance on queries by using directory structure to partition data. A few functions that cannot be done in Relational Databases can be performed using Hive. There are massive chunks of data that might be difficult to search and produce queries. Hive does this quite efficiently. It is capable of processing queries and delivering results rapidly.

  • Apache Spark

This is a computing engine that processes and analyzes data of all sizes. Spark uses a central coordinator, called the driver, and many distributed workers, called executors. Unlike batch processing, Spark can analyze live data and historical data to help make fast and real time decisions.

Corporate training courses in big data analytics can help you gain more knowledge on how the process works. Enroll yourself in a reputed IT training institute to gain a deeper understanding of these tools.

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