“Data is the new oil!” This line is believed to have been said in 2006 by Clive Humby, UK mathematician, and the creator of retail giant Tesco’s Clubcard program. Later, in 2017, The Economist published a story titled “The world’s most valuable resource is no longer oil, but data.” For years, oil had been considered a resource with the highest value of all, but it seems that position has now been claimed by data.

Why is data so valuable?

In organizations big and small and from all verticals, massive amounts of data flow in on a daily basis. These comprise production numbers, employee punch-in records, inventory, waste material, and numerous other data points. Their purposes differ, but what is common is the fact that when mined carefully, they could reveal many useful insights that an organization could leverage to guide its strategic decisions. It thus becomes critical to manage and sort these huge quantities of data, a job that falls to big data professionals.

What are the important tools for big data?

Careers in big data are very popular, for reasons explained above. For someone with an interest in technology, a fondness for numbers, and skills in organizing and analysis, these could be a great option.

In the course of day-to-day work, a big data professional needs to work with a variety of big data software and tools. The top ones are enumerated below:


This is a search engine with JSON rest API using Lucene, similar to engines deployed for complex searches in document databases. Examples include searches accounting for language morphology or by geocoordinates. It has official clients in Groovy, Java, JavaScript, NET (C #), Perl, PHP, Python, and Ruby.

ElasticsSearch uses a key-value store for objects, lending it much more flexibility than traditional relational databases where data is stored in tabular format. This also allows it to process queries much higher in complexity than those handled by traditional databases, and that too at a scale of petabytes.

For projects of a scale smaller than that requiring Hadoop or similar large platforms, ElasticSearch is a good option. It is based on standard NoSQL-solutions, good for handling average volumes of data accumulation and processing. It is great for 2–10 terabytes of data per year and 20–30 billion documents in indices, and it works well with the Spark cluster.


This is sometimes touted as the next-generation leader in cloud and big data integration software. It is essentially an open-source software integration platform/vendor that includes solutions for data management and integration. It has a graphical wizard that generates native code, and it also allows the integration of big data, masters data management, and checks the quality of data. Some of its features are as below:

  • Accelerated time to value
  • Simplified extract-transform-load (ETL) and extract-load-transform (ELT) processes
  • Native code generation for simpler usage of MapReduce and Spark
  • Machine learning and natural language processing for higher-quality data
  • Speedy completion of big data projects through Agile DevOps


It is hard to imagine a career in big data without knowledge of Hadoop. This is an open-source framework from Apache, written in Java and running on commodity hardware. It was based on the Google concept of working with large amounts of data, and it comprises several closely intertwined subprojects.

Some of the main modules in Hadoop are the following:

  • MapReduce: The data processing layer
  • YARN: A task scheduler that manages resources of the computing cluster, the MapReduce module, and the module for managing Hadoop internal libraries
  • HDFS: The storage layer – a special file system that works with large files

Hadoop has a number of use-cases. These include data searching, analysis, and reporting; large-scale indexing of files; and other tasks in the data processing.


RapidMiner supports visualization, validation, and optimization of data, among other stages of in-depth data analysis. It is a free open-source environment that helps to conduct predictive analytics with access to all the necessary functions. What helps its usage by big data professionals is the fact that it does not require programming knowledge, given that it uses visual programming. Also, it does not require complex mathematical calculations.

Working with RapidMiner is quite simple. All that is needed to form the data processing is to:

  • Drop the data on to the working field
  • Drag the operators into the graphical user interface (GUI)

How does one get the skills required?

For stronger prospects in the big data industry, it is a good idea to opt for one of the best big data certifications. Certification shows the candidate is willing and desirous of spending time and effort in developing skills and knowledge, and ready to do this on a continuing basis. It is a testament to possessing the latest knowhow in big data and is a great way to begin a career or to grow to a position of higher responsibility.