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Big Data

Big data is a term that describes the large volume of data – both structured and unstructured – that inundates a business on a day-to-day basis. But it’s not the amount of data that’s important. It’s what organizations do with the data that matters. Big data can be analyzed for insights that lead to better decisions and strategic business moves.

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Apache Cassandra

Apache Cassandra is a free and open-source, distributed, wide column store, NoSQL database management system designed to handle large amounts of data

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Apache Spark

Apache Spark is a unified analytics engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.

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Apache Hadoop

Apache Hadoop is a collection of open-source software utilities that facilitates using a network of many computers to solve problems involving massive amounts of data and computation

Big Data Implementation

  1. Analyze Business Requirements. …
  2. Agile Implementation Is Key. …
  3. Business Decision Making. …
  4. Make Use of the Data You Have. …
  5. Don’t Abandon Legacy Data Systems. …
  6. Evaluate the Data Requirements Carefully. …
  7. Approach It From the Ground Up. …
  8. Set Up Centers of Excellence.

Best Practices

  • Gather business requirements before gathering data.
  • Implementing big data is a business decision not IT
  • Use Agile and Iterative Approach to Implementation
  • Evaluate data requirements
  • Ease skills shortage with standards and governance
  • Align with the cloud operating model.

Data Implementation

Begin big data implementations by first gathering, analyzing and understanding the business requirements; this is the first and most essential step in the big data analytics process. If you take away nothing else, remember this: Align big data projects with specific business goals.

Big data projects start with a specific use-case and data set. Over the course of implementations, we have observed that organization needs evolve as they understand the data – once they touch and feel and start harnessing its potential value. Use agile and iterative implementation techniques that deliver quick solutions based on current needs instead of a big bang application development. When it comes to the practicalities of big data analytics, the best practice is to start small by identifying specific, high-value opportunities, while not losing site of the big picture. We achieve these objectives with our big data framework.