Apache Hadoop is an open source software like no other. It is scalable, trustworthy, consistent and supports distributed computing. It is a software library rather than a separate software program. The Hadoop framework allows distributed processing of extensive datasets, which is why we get to see a lot of mention of Hadoop in the big data scenario. It is not just another software that takes care of data and database management, it is a software of the future, which can pave the way for machine learning and big data into your company.
Who uses Apache Hadoop?
As per RemoteDBA.com, a huge number of companies across the USA and the world use Hadoop for scientific analytics, business data storage, research and production purposes. This Java-based programming framework is rather flexible and reliable. It can easily manage thousands of terabytes of data and handle thousands of application hardware nodes.
Most of the big data projects now use short-term computing resources like Hadoop in substantial amounts. Now, this is well suited for highly scalable public clouds including Google Cloud Platform, Amazon Web Services, and Microsoft Azure. Most of the similar public clouds support Hadoop tasks directly, or they have specifically tailored services for Hadoop-type tasks.
How is Apache Hadoop going so many places without any direct publicity?
Hadoop can extend its own capabilities and features by supporting many added projects including –
- Apache Flume: this is a tool for collecting and aggregating massive amounts of data into the HDFS.
- Apache Hive: this is a data warehouse from Apache, which can summarize data and process queries for analysis.
- Apache HBase: it is an open source, non-relational database
- Apache Phoenix: it is another open source engine for Hadoop. It still uses Apache HBase for functioning.
- Apache Oozie: it can manage Hadoop jobs by scheduling server-based workflow.
- Apache Pig: it is a high-level platform. It can support and create programs for Apache HBase.
- Apache Sqoop: it transfers bulk amounts of data between Hadoop and relational database systems.
- Apache Spark: this is a fast engine for big data processing. It can stream and support SQL, graph processing, and machine learning.
- Apache ZooKeeper: another open source configuration that can support synchronization of large distributed data systems.
- Apache Storm: it is an open data processing system for the open source relational database systems.
Maintaining and working on Apache Hadoop is not easy. Unlike MySQL databases, it is not as easy to master. If you want your company to step into the realm of big data holding the hands of Hadoop, you will need the service of a responsible database administrator. We know, getting an in-house senior DBA is next to impossible for most startups and small businesses. However, you can surely opt for reliable but affordable remote DBA services.
Author Bio: David Wicks is an author, blogger and RDBMS researchers. His work with RemoteDBA.com. has been outstanding. It has shed light on cost cutting while data management, efficient big data management, minimization of data corruption and maintaining multiple data tables by using modern RDBMS options.
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