Traditional computer systems and software applications don’t have what it takes to support big data. If you want to collect, store, refine, or analyze big data, you have to have the right tools. Check out the following ten tools that are specifically designed with big data in mind.
1. Hadoop
If you know, or are willing to learn Java, Hadoop might be a great solution for you. This open source framework gives you the ability to store large amounts of data over several computers. Hadoop has enough storage to handle tons of data, and the power to stand up to multiple and ongoing processing requests.
2. Neo4J
Neo4J touts itself as being the first and the largest graph database in the world. It provides solutions to businesses that want to exploit data relationships in order to drive smart
applications.
3. Plot.ly
Big data is only as useful as your ability to communicate it to people who may not have significant technical know how. This is where Plot.ly comes in handy. This tool gives you the ability to create easy to understand charts and graphs based upon your data.
4. Bokeh
This is another tool that you can use to create visual representations of large collections of data. This product is more advanced than Plot.ly. Bokeh has been used to great effect in this Ted Talk that addresses the concept of first and third world nations. The Bokeh website provides a gallery of similar examples.
5. Cloudera
This solution allows businesses to create an enterprise data repository. This can then be accessed by team members from all business areas for the purposes of analysis and planning. Cloudera uses Hadoop and then adds on a few new features. This makes it an ideal solution for those who don’t have the technical skills required to use Hadoop directly, but still want to take advantage of its functionality.
6. Cassandra
With Cassandra, Apache has created a tool that can efficiently handle very large amounts of data. It leverages a NoSQL database in order to keep track of data that is stored not just on different machines but in multiple data centers. Reddit, Facebook, and Twitter are just three companies that have chosen Cassandra as one of their big data solutions.
7. OpenRefine
Unfortunately, not all data is formatted in a way that makes accessing or analyzing it very easy at all. Because of this, the ability to clean up messy data, and to navigate sets of data that aren’t very well structured is important. OpenRefine (formerly known as GoogleRefine) provides users with a way of accomplishing these tasks.
8. Wolfram Alpha
Wolfram Alpha is a search engine that treats the internet itself as a large repository of data. For example, if you enter a keyword into a standard search engine, it will return websites with content matching that keyword. Do the same in Wolfram Alpha, and you will see a variety of data relating to your search that has been compiled from a variety of sources. Wolfram Alpha can also be used to perform complex calculations.
9. MongoDB
If you have a data repository that is frequently changing MongoDB might be a solution that will work for you. It is perfect for large project catalogs, content management systems, content creation websites, data from mobile apps, and for situations where the ability to view data from multiple systems is important. There is definitely a learning curve when it comes to mastering this product. Fortunately, the MongoDB website offers up plenty of educational material.
10. DataCleaner
Just like the name implies, this big data solution helps users to ensure that they have the cleanest data sets possible. DataCleaner helps to identify missing information, detects data redundancies, and provides data cleaning as well as standardization. It can also be used as a data monitoring tool. This helps to ensure the ongoing quality of your organization's data. In industries where a high level of accuracy is needed, this solution can prove to be well worth the cost.
Conclusion
Your organization’s ability to effectively store and handle large amounts of information could be key to your success. These ten tools provide you with a means to get a handle on big data and improve your competitive advantage, or increases your ability to use that data to effect meaningful change.
Some opinions expressed in this article may be those of a guest author and not necessarily Analytikus. Staff authors are listed in https://www.analyticbridge.datasciencecentral.com/profiles/blogs/10-tools-for-working-with-big-data-for-successful-analytics
