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Top 10 Tools for Working With Big Data for Successful Analytics

DZone's Guide to

Top 10 Tools for Working With Big Data for Successful Analytics

We have gathered a list of the current tool for working with big data for analysts that want to perform their tasks successfully. Check it out below.

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Data is everything is a modern business. No decisions, strategies, or methods would have been developed or implemented without this analysis. Earlier, the data industry called it “business intelligence,” which made perfect sense because the information was critical to developing effective solutions that gave competitive advantage on the market and so on. Today, however, this term has been replaced with a new buzzword, “big data,” because the information possessed in the databases in the organization has long started to pile up and reached huge volumes. Indeed, big data is a much more complicated world than business intelligence because of the larger scale.

Collection and analysis of big data can be a challenge for businesses because it requires a number of complex technical solutions. However, there are some powerful analytical tools for working with big data that can be utilized both by analytics professionals and businesses to tackle the job. We have gathered a list of the current tool for working with big data for analysts that want to perform their tasks successfully. Check it out below.

1. OpenRefine

It is a popular data analytics tool among the professionals because it enables the user to prepare everything for the analysis. This means that even though you have a messy database with different types of data and names that won’t be so easily processed by a computer, the tool will group the entries through its powerful clustering algorithms. As soon as the clustering is done, the analysis can begin.

2. Hadoop

Big data and Hadoop have been going hand-to-hand for quite a while now. It is a software library and framework that enables distributed processing of large data sets across clusters of computers utilizing simple programming models. It is known for extremely good processing of large volumes of data and making it available on local machines. The developer organization Apache is constantly enhancing the tool to make it even more effective.

3. Storm

Another great product from Apache, Storm, is a real-time computation system that enhances the processing of unbounded streams of data. It is also used to perform a variety of tasks related to big data, including distributed RPC, continuous processing, online machine learning, and analytics in real time. Another advantage of using Storm is integration with the technologies already in use, which makes processing the big data much easier.

4. Plotly

This is a data visualization tool compatible with Javascript, MATLAB, Python, and R. Plotly allows creating really dynamic visualization even when you do not have skills or time writing the codes, which contributed to its wider use in today’s big data industry. The tool has been used by the new generation of data scientists because it is a business development platform that enables quick comprehending and analysis of huge amounts of data. Data scientists at Aussiessay, for example, use it to make investment decisions by spending less time for data computation.

5. Rapidminer

Another tool for big data needs is Rapidminer, which is the open source data science platform that operates through visual programming. Its functions include manipulation, analysis, modeling, creation of models, and fast integration in business processes. Rapidminer has won the recent data science software poll from one of the leading websites in the industry, KDnuggets. This demonstrates its popularity among data scientists and reliability of the product.

6. Cassandra

Apache Cassandra is the next tool worth attention because of its ability to manage massive amounts of data in an effective and efficient fashion. It is a scalable NoSQL database that monitors the data across different centers and is used by a number of well-known enterprises, including Netflix and eBay.

7. Hadoop MapReduce

This term is used to refer to a software framework that allows writing apps which process massive volumes of data in-parallel in a reliable way. There are two main jobs performed by the MapReduce apps, including mapping and reduce job, which deliver a variety of data processing results. The tool was originally developed by Google.

8. Bokeh

The main goal of this visualization framework is a provision of elegant and concise construction of novel graphics in order to enhance this function with interactivity over massive streaming datasets. It is available exclusively to Python.

9. Wolfram Alpha

It is a search engine that invites the user to search for something they want to calculate or know about. For example, if you type “Facebook,” you will get tons of data, such as HTML element hierarchy, input interpretation, web hosting information, web statistics, subdomains, Alexa estimates, web page information and many more other.

10. Neo4j

The official site of this tool names it the next major evolution in graph database technology. Partly, it is true because this database uses the relationship between data to operate and boost the performance improvements. Neo4j is used by many companies today to obtain sustainable competitive advantage through data relationships that drive intelligent applications.

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Topics:
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