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Must Know Big Data Tools And Technology For Your Successful Career | 2018


Must Know Big Data Tools And Technology For Your Successful Career | 2018
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With businesses around the world generating an enormous amount of data each day, the global revenues of big data and business analytics software will increase from $122 billion in 2015 to over $187 billion in 2019. This large amount of data when merged with industries such as e-commerce, IoT, consultancy, and financial services, presents immense job opportunities.

Presently, companies rely on predictive, real-time, and integrated insights to transform their business, and a majority share of IT investment goes into managing and maintaining big data. This has resulted in an increase in the number of people interested in the data analytics field. Thus, if you want a career in big data, it is recommended to get a big data certification to help you understand the modern technologies and have a definite edge.


Learning the big data tools and technologies will give you the right exposure to the industry. Let’s have a look at the top five big data tools and technologies to give your career a valuable boost.

1. Spark

Apache Spark is one of the main open-source ventures for data processing. Thanks to its speed and scalability, organisations are increasingly utilising Apache Storm for processes related to real-time results. Spark is also one of the most frequently used big data tools for streaming information, where precise and hi-quality results are expected. It has cohesive units for graph processing and SQL sustenance, which makes it one of the fastest engines for big data processing. Spark supports all chief big data processing languages like Java, R, Python, and Scala.

2. Hadoop

Hadoop, or high-availability distributed object-oriented platform, is a software that assesses unstructured and structured data. With the help of Hadoop, data scaling is conceivable without the problem of hardware malfunctions. It allows huge storage for a variety of data, and it can practically handle infinite coexisting responsibilities.

Today, Hadoop has evolved tremendously, and now encompasses an entire arrangement of interlinked software. This is the main reason why significant big data solutions are dependent on Hadoop. According to Zion Market Research, the market for Hadoop-based services and products will increase by 50% CAGR by 2022, and the worth of these services will be nearly $87.14 billion in 2022, as opposed to $7.69 billion in 2016.

3. MongoDB

MongoDB is an effective, principal NoSQL, and open-source document software that is compatible across different platforms. It is recognised for its storage capacity and its performance in the MEAN software stack (software stack for developing dynamic sites and web applications). It collects the document information in the binary structure of the JSON document and is mostly used for its powerful obtainability, scalability, and presentation.

MongoDB has some striking inbuilt framework which makes the database appropriate for companies to make prompt decisions and develop a custom data-based relationship with its users. Apart from MEAN stack, MongoDB is compatible with Java platform and .NET applications.

4. R

An interesting aspect of R is that it is not only a software but a programming language as well. The software feature of R takes responsibility for data mining and extraction, and the programming language feature is accountable for the sophisticated analytics of the excerpted data. Overall, it is an open source tool that is formulated in its own programming language. R is famous amongst data miners and developers all over the world who are into quantitative and statistical data interpretation. Apart from assisting as a data mining platform, R also provides features like linear and nonlinear modelling, clustering, time series analysis, classical statistical tests, graphical and statistical techniques. R is definitely one of the most interconnected big data platforms available.

5. Elasticsearch

Elasticsearch is a versatile, flexible, and seamless big data tool that enables you to obtain data from unspecified sources and utilise it for visualisation and analytics. It supports all forms of information and is developed for horizontal scalability, ease of management, and security. Elasticsearch supports other data formats and supplies a myriad of methods to communicate with information and obtain data. You can explore diverse data standards from unstructured and structured, geo-metrics, acquire insights on your specifications, and discern the important elements of your company or project.

6. Hive

Hive is a data warehouse platform that converts query language into MapReduce commands. Apache Hive is a part of Hortonworks Data Platform (HDP) and offers a similar interface as SQL (to save information in HDP). HiveQL is the query language that is exclusive for Hive, and it translates SQL-like queries and extends it to the Hadoop platform. HiveQL entirely supports MapReduce scripts which acts as a plugin for the queries. Hive increases the design flexibility and provides for data deserialisation and serialisation.

The big data ecosystem is constantly progressing and advanced tools and technologies are getting more sophisticated in applying analytics for business operations. Professionals require the above listed big data tools and technologies to complete and filter information for distinct sets of large data volumes for additional usage. One important thing to remember is that it’s not only the exposure to these tools and technologies that will create an impression on your career. You must be informed of how the industry works and a big data certification will help bridge the gap between industry requirements and what you learn.

Abhinav Rai is the Data Analyst at UpGrad, an online education platform providing industry oriented programs in collaboration with world-class institutes, some of which are MICA, IIIT Bangalore, BITS and various industry leaders which include MakeMyTrip, Ola, Flipkart etc.