The Most Important Tools for Big Data Professionals

Across verticals and in companies of all sizes, massive amounts of data are constantly flowing in. Be it customer interactions, employee records, sales figures, or anything else, companies have access to a wealth of information, which could be a great source of insights for future action.

This is precisely where the big data industry comes in. It helps to properly segment and store the huge amounts of data, in a suitable format and with the requisite access rights as per authorization. This helps to prepare the data for further analysis, which could reveal a wealth of insights for top management.

To be successful big data professionals, it is important to be conversant with a variety of tools commonly used in this field. Below are the top big data tools:

Hadoop

Most professionals begin their journey in the big data industry with Hadoop. Written in Java, it is an open-source framework from Apache and runs on commodity hardware. Its origins are rather interesting, as it was based on the Google concept of working with large amounts of data. It comprises several subprojects closely intertwined with each other.

Its main modules include:

  • HDFS: A special file system for working with large files, it is the storage layer of Hadoop.
  • YARN: A task scheduler for managing the resources of the computing cluster, the MapReduce module, and the module for managing Hadoop internal libraries
  • MapReduce: The data processing layer

Specific use cases include data searching, analysis, and reporting; large-scale file indexing of files; and other data processing tasks.

Presto

A distributed open-source mechanism of the SQL query engine, this is used to perform interactive analytical queries to data sources of various sizes – from gigabytes to petabytes. Developed by Facebook, it helps big data professionals to conduct interactive analytics and is known for its quick pace when working with commercial data warehouses.

Some of its features include:

  • One query can aggregate data from multiple sources, facilitating big data analysis across the organization
  • It allows the use of standard SQL data types, window interface functionality, statistical and approximate aggregate functions. This is because it supports ANSI SQL, implying the use of JSON, ARRAY, MAP, and ROW.

RapidMiner

This is a free open-source environment for conducting predictive analytics with access to all the necessary functions. It offers support across stages of in-depth data analysis, such as visualization, validation, and optimization.

Two big factors in favor of its use in the big data industry are that it does not require knowledge of:

  • Programming: as it uses visual programming
  • Writing code: as no complex mathematical calculations are required

Its working is very simple, too:

  • The user drops the data on to the working field
  • The user drags the operators into the graphical user interface (GUI)
  • The data processing process is formed

It is possible, though not essential, to understand the generated code. It can also work with Hadoop by adding the paid RapidMiner Radoop extension, which requires the Hadoop cluster to be accessible from the client running RapidMiner Studio.

R

The last but most certainly not the least in the list, R is massively popular among statisticians and data miners for developing statistical software and data analysis. Supported by the R Foundation for Statistical Computing, R is a programming language and free software environment.

To enable wide-scale statistical analysis and data visualization, big data professionals commonly use R with the JuPyteR (Jupiter, Python, R) stack. JupyteR Notebook is one of the most popular tools for big data visualization, allowing the user to:

  • Compose any analytical model from more than 9,000 Comprehensive R Archive Network (CRAN) algorithms and modules
  • Run it in a convenient environment
  • Adjust it on the go
  • Immediately inspect the analysis results

R has the following advantages:

  • Allows compilation and running on a wide variety of UNIX platforms, Windows and MacOS, making for comfortable usage
  • Can run inside the SQL server
  • Supports Apache Hadoop and Spark
  • Easily scales from a single test machine to vast Hadoop data lakes

Apart from familiarity with the top tools, it is also wise to add to the technical knowledge by opting for one of the popular big data certification programs. A certification testifies to the knowledge of the latest tools and techniques, and also shows the employer that the candidate is serious about a career as a big data professional.

The more complex the analytics planned, the more tools one should pick up for a great career in big data!

Leave a Reply

Your email address will not be published.