Which Three Open Source Projects Are Transforming Big Data Hadoop?

Hadoop, an open source software development framework, is a game-changer for businesses. Big data hadoop enables companies to store, manage, and analyze excessive data for competitive advantage and actionable insights. However, it is not as easy as it seems to be. Implementing Hadoop is a serious job that requires expert qualified team of engineers and data scientists. This is slightly costly and cumbersome for several businesses and especially the small scale ones. But, with the help of open source projects, companies can now easily afford big data analytics and Hadoop development services.

hadoop programing

Here we will share three major open source projects that help transforming hadoop:


Earlier it was a difficult job to analyze stored data for insight reporting with Hadoop. There were skilled data scientists who were trained in writing complex “java map-reduce” jobs that could unleash analytics capabilities of Hadoop. To resolve this issue, Hive (2008) was discovered.

Hive uses HiveQL language to translate SQL like queries automatically into MapReduce jobs that are executed on Hadoop. SQL is an easy and widely recognized programming language among developers and a preferred data language used in the industry. Hive puts SQL on top of Hadoop and transformed Hadoop by providing its analytics power to companies and people, not only developers. Hive is suitable for querying, summarizing, and analyzing large structured data sets.


When Hive resolved the issue of summarization, querying and analyzing of large data sets, developers encountered trouble with computations on MapReduce as it was limited and slow in speed; Spark comes in to limelight. Apache Spark, an open source powerful Hadoop data processor, is designed and intended for handling both batch and streaming workloads in record period. Apache Spark runs program faster in memory (100 times) and on disk (10 times) as compared to MapReduce.


Presto, a query machine runs programs in memory only and not in memory and on disk. This functionality enables Presto to run not-so-complex queries on Hadoop in just a few hundred milliseconds (complex and intricate queries take few minutes to run). It is efficient in combining data from multiple sources into a single query.

Hadoop Programming is getting larger every day. Due to several opportunities and open source tools (as mentioned above), companies are now started looking latest and innovative ways to leverage valuable data and make best practices to cater big data demands of the future.

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