10 Big Data tools for enterprise developers every one should Know Posted on : Nov 25 - 2016

The term ‘Big Data‘ can no longer be considered a buzzword. As more and more organizations make the move to leverage data to make better business decisions, a number of tools are being used work with big data.

There are thousands of Big Data tools out there. All of them promising to save you time, money and help you uncover never-before-seen business insights. And while all that may be true, navigating this world of possible tools can be tricky when there are so many options.

1. MongoDB – This is an open-source documental database that is ideal for developers who want to have precise control over the final results.

This comes with full index support and the flexibility to index any attribute and scale horizontally without affecting functionality. The document-based queries and GridFS for storing files mean that you shouldn’t have issues with compromising your stack

2. Cassandra – Is an open-source, distributed database management system. It was originally developed by Facebook and can handle large amounts of data across servers. This improves data availability and reduces the possibility of failure.

3. Spark – One of the most active projects in the Apache Software Foundation, Spark is an open-source cluster computing platform.

4. Hadoop – Written in Java, Hadoop is an open-source framework for distributed storage and processing of large amounts of data. This data is based on computer clusters that are built on commodity hardware.

5. Elasticsearch – Is a distributed RESTful search engine that is built mainly for the cloud.

6. NoSQL – A flexible graph model is used which, again, can scale across multiple machines. NoSQL databases do not provide a high-level declarative query language like SQL to avoid overtime in processing. Rather, querying these databases is data-model specific.

7. CouchDB – a NoSQL, open-source, document-oriented database using JSON to store data.

8. Apache Hive – The Apache Hive data warehouse software facilitates querying and managing large datasets residing in distributed storage. Hive is a powerful tool for ETL, data warehousing for Hadoop, and a database for Hadoop.

9. Data Mining – Not to be confused with data extraction (covered later), data mining is the process of discovering insights within a database as opposed to extracting data from web pages into databases. The aim of data mining is to make predictions and decisions on the data you have at hand.

10.Data Visualization – Data visualization companies will make your data come to life. Part of the challenge for any data scientist is conveying the insights from that data to the rest of your company. For most of your colleagues, MySQL databases and spreadsheets aren’t going to cut it. Visualizations are a bright and easy way to convey complex data insights. And the best part is that most of them require no coding whatsoever!