What is Big Data Analytics and Where to Start First Here?

In this myriad world of Big Data, scientists and data engineers have to deal with magnanimous volume and varieties of data sources. Not every data could lead to the results, and that’s why data engineers have to rely on a team of Big Data analysts to study Structured and Unstructured data. With the ever-expanding scope of Big Data analytics, it’s a cardinal rule to start with the right skills and knowledge about the industry and the ongoing trends.

What is Big Data Analytics?

In Big Data analytics courses in Bangalore, the definition could help understand the history and evolution of this concept, starting from the 1950s during the Cold War era, right until the ongoing Industrial Revolution 4.0. It could involve learning about Machine Learning/AI and data intelligence, in addition to Programming and coding.

Big Data analytics courses in Bangalore provide training into high-performance in-memory analytics for quick, agile and competitive decision-making.

 

According to a leading Big Data journal and research company, Big Data Analytics is defined as the systematic examination of a large volume of data – Structured and Unstructured, to unravel the hidden patterns, correlations and other data-based insights to arrive at a desired conclusion. In the recent times, apart from Structured and Unstructured data, engineers have also started working with Dark Data whose origin and analytical output can’t be gauged first-hand.

Where to Start with Big Data Analytics?

Big data analytics are driven by a specialized set of software tools and systems that run on quantum computing, cloud programming and machine data storytelling. There are at least three layers of data mining for Big Data applications, starting with predictive modeling, statistical intelligence and RDBS.

In the current state, you could start with the open source Big Data analytics tools. Here are the top five you could master –

  • Apache Haadoop

The benefits of learning Apache Hadoop include orienting skills to design and run on-premise Cloud architecture using HDFS, YARN, MapReduce and Hadoop Libraries. The Apache Hadoop software library allows Big Data engineers to work with distributed computing architecture at multiple levels of applications, including Hive, HCatalog, and ZooKeeper.

  • Apache Spark

Another skill level for Big Data analytics is the Hadoop data engine using Apache Spark. Spark is a fast computing engine that supports all the on-going projects in Machine Learning, ETL, NLP, Stream Processing and batch computation. Apache Spark also works with ZooKeeper, Avro and Tez—all these can be used to assist and/or replace MapReduce as data-flow programming framework.

Apart from these skills, ambitious data engineers could also explore Neo4j, R andMongoDB for studying inter-relationship of data in Amazon Web Services (AWS) and cipher query platforms.

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