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Diving into The Ocean of Big Data & Analytics

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Today, the universe of data is continuously expanding at a speed that is much more than expected. At the start of 2020, the amount of data generated was estimated to be a little over 44 zettabytes! I’m sure it has well surpassed these numbers today.

This data is immensely complex and exists in the form of structured and unstructured data sets transmitted from a variety of sources and is known as big data

With this blog, let’s explore the what’s and why’s of big data, its importance, trending platforms, and its key results- diving into the depths of the big data ocean.

What is big data?

Big data refers to a large, diverse set of information that is growing exponentially. It often comes from data mining and is present in multiple formats. Big data attributes encompass the size, speed, and diversity at which the data is created and collected. These key attributes collectively form the three Vs of big data.

What-is-big-data

  • Volume: Massive amounts of data being stored
  • Velocity: Speed at which data must be processed and analyzed
  • Variety: Different data sources and forms, such as numbers, text, video, images, audio, text, etc. from which data is gathered

We know that a massive amount of data is being generated constantly, but it’s equally important for companies and organizations to manage, store, visualize, and analyze it.

Traditional data tools aren’t equipped to handle this kind of complexity and volume, so, here are some modern-age big data platforms that can help businesses manage their data and thrive using it.

What are the 5 trending big data platforms?

In this competitive environment, each major big data platform offers slightly different functionalities to users according to their varied needs. Here are some of the exceptional big data platforms which are popular in the market:

  • Amazon EMR
  • Cloudera Data Platform
  • Google Cloud Dataproc
  • HPE Ezmeral Data Fabric
  • Microsoft Azure HDInsight

How does big data analytics work?

To get valid and relevant results from big data analytics applications, data scientists have built practices for data preparation, data profiling, data cleansing, data validation, and data transformation.

Using customer data as an example, the various branches of analytics that can be explored with different sets of big data include the following:

  • Data distribution and parallel processing: Breaking files into multiple small blocks (racks) stored in different units which are later processed and results are merged back together
  • Failure tolerance: Enabling big data solutions to scale up even on cheap commodity hardware rather than expensive SAN disks
  • Scalability & flexibility: Adding or removing racks from the cluster without worrying about the size for which this solution was designed
  • Cost-effectiveness: Lowering the cost of expensive servers with failure-resistant disks (e.g. SAN)

How to build an effective big data strategy?

In an organization, developing a big data strategy requires a fair understanding of business goals, analysis of organizational data, and assessment of the need for additional data to help meet the business objectives. Here’s how an effective big data strategy can be built along with experienced service providers:

  • Prioritizing planned use cases and applications: Identity, structure, and prioritize the most promising cases and tasks using big data conceptual or data-driven approach.
  • Identifying new systems and tools needed: Recognize big data tools that can help you in handling huge business data sets and identifying patterns/ trends
  • Creating a deployment roadmap: Build a big data implementation roadmap as per your business goals, the maturity of your data management environment, and the amount of risk your organization can absorb
  • Evaluating internal skills: See if retraining/ hiring/ internal training is required to implement big data throughout your system

Want to find your future with big data?

Big data is gaining importance in organizations to manage their datasets no matter what size they are. So, I would say, it’s the best time for you to get started with an effective big data strategy by focusing on the above points to mitigate future risks and achieve business goals.

At last, wrapping up my blog. I hope it helped you understand big data, its importance, and the benefits you can achieve. If you wish to explore the big data ocean more, reach out to our dedicated big data specialists.

About Parija Kopergaonkar

Sr. Marcom Executive

  • Automation
  • Business Strategy
Parija, a Senior Marcom Executive at Nitor Infotech is a writing enthusiast, doodler, and loves to bring creativity in every kind of work she does. She trusts the power of manifestation and believes in karma- “what goes around comes around”. Never restricted by language, songs are her mood changer. She wants to explore the world by keeping one memory from each corner- a long bucket list to go!