The best Big Data tools
The use of Big Data tools by companies is symptomatic. The collection of huge amounts of information with which to reach the perfect customer is of vital importance.
In recent times, so much Big Data software has appeared that it can be difficult to know which one to choose. Therefore, it is essential to know which tools to use to transform data into useful knowledge.
Knowledge that allows, for example, to create strategies focused on attracting new customers and increasing sales. However, the enormous amount of data obtained in these processes is really difficult to analyze if the right tools are not used.
Be that as it may, among the most widely used tools in this field there are some open source and some paid ones, which is good proof of the success of this development model that helps to analyze, process and store the data collected.
10 must-have Big Data tools for data analysis
Collecting vast amounts of data and finding trends in it allows organizations to move much faster, smoother and more efficiently. Here are some of the most widely used.
1. Apache Cassandra
Apache Cassandra is a NoSQL database originally developed by Facebook. It is one of the best options if you need scalability and high availability without compromising performance. Among the companies that use it are Reddit or Netflix.
2. Apache Drill
Apache Drill is an open source framework that enables interactive analysis of large-scale data sets. It was designed to achieve great scalability in servers and process large amounts of data and millions of records instantly. It is compatible with many file systems and databases.
3. Apache Hadoop
Apache Hadoop is probably the most widely used Big Data software. In fact, it is used by large companies such as Facebook or The New York Times. This framework allows the processing of large volumes of data in batch using simple programming models. In addition, it is scalable, so it is possible to go from operating on a single server to operating on many servers.
4. Apache Oozie
Apache Oozie is another Big Data tool that could not be missing in this list. In essence, it is a workflow system that allows a wide range of jobs written or scheduled in different languages to be set up. It also allows jobs to be linked and users to define dependency relationships between them.
5. Apache Spark
Apache Spark is synonymous with speed; in fact, it is up to a hundred times faster than Apache Hadoop. This software enables real-time batch data analysis, as well as the creation of applications in various languages such as Java, Python, R or Scala, among others.
6. Apache Storm
Apache Storm is an open source tool that can be used with any programming language and easily processes endless data in real time. In addition, the system creates topologies of the big data to convert and analyze them continuously as information flows into the system constantly.
Elasticsearch makes it possible to process a huge amount of data and visualize its evolution in real time. It also displays graphs to help you better understand the information provided. A point in its favor is that it can be expanded with Elastic Stack, a product package that multiplies its features. Some of the big companies that use this software for Big Data are Etsy or Mozilla.
MongoDB is a NoSQL database designed to work with data sets that vary frequently, or that are semi-structured or unstructured. It is one of the Big Data tools used, among others, for storing data from mobile applications and content management systems. Large companies such as Telefónica or Bosch are some of its users.
R is an environment and programming language for statistical analysis very similar to mathematical language. However, it is also used for the analysis of large amounts of data. Since there is a large community of users, there are numerous libraries. Many statisticians and data miners use it.
Python has the great advantage that it can be used with minimal computer skills, so it is not surprising that it has a large number of users who can create their own libraries. However, one of its drawbacks is its speed, as it is considerably slower than its rivals.
Big Data software, essential for companies
In recent years, the amount of data produced by new technologies has increased exponentially. Whereas in the past we used to talk about megabytes and gigabytes of data, today it is not uncommon to talk about petabytes.
Thus, companies need solutions that help them to store, process and analyze information in order to make the best decisions. This is why Big Data tools are so vital for making the best use of this data.
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