How to deal with Big Data (Data is everything)

Big data is prevalent in research, and data sets are only getting more extensive and challenging to work with. In just five years, the digital universe will be ten times its current size.

Big Data is making it difficult for organisations to keep track of their data. As a result, they must implement more intelligent data management techniques that help them organise and optimise their data.


What is Big Data?

Big data is used to describe massive, more complicated datasets, especially those gathered from novel sources. Traditional data processing software cannot handle such vast quantities of data. However, because the enormous data may be utilised to address business issues that would otherwise have been impossible, we’re going to discuss it in detail in this blog.

The three primary issues that arise as a result of Big Data are storage, processing, and managing it effectively. Data management tools have evolved to store vast information, and purpose-built hardware has improved the computational capacity. The next stage in this development is learning how to handle Big Data throughout its entire life cycle.


How Should Big Data Be Managed?

The first step is to identify the data’s ‘unique set’ and reduce the amount of data to be managed.


Leverage The Power of Virtualisation Technology

Data-centric organisations must virtualise this unique data set so that multiple applications can reuse the same data footprint and store the smaller data footprint on any vendor-independent storage device.

Virtualisation is a key weapon that businesses may use to tackle the Big Data management difficulty.

Big data is reduced to small data and managed like virtual data when you remove the digital footprint, virtualise the reuse and storage of the data, and centralise the management of the data set. Now that the data footprint is reduced, businesses can make significant improvements in three critical areas of data management:

  • Applications take less time to process data.
  • Even though access is dispersed, data may be more securely guarded since control has been centralised.
  • The more complete the data, the more reliable your conclusions will be since all versions of the data are visible.
  • When it comes to managing Big Data, virtualisation is the “hero,” and it offers several advantages for businesses: end-users profit from lower costs and greater flexibility.


Visualise The Data

As data sets increase, new wrinkles appear. Each step will bring you face to face with fascinating, strange behaviour. Create many graphs and analyse them for outliers.


Show The Progress

Showing your progress is just as essential as finishing the job. This implies keeping track of everything you did with the data, including how you obtained it and what version of it you used. It’s essential to record and repeat your procedures, especially if you’re working in a team or needing to demonstrate that something was completed. This kind of information is critical for documenting and reproducing your methods.


Utilise Version Control

The ability to track changes over time is one of the most important advantages of version control. It allows researchers to see exactly how a file has evolved and who made the modifications. On the other hand, some systems impose size restrictions on the files you may utilise.


Record The Metadata

Metadata is only useful if one understands what it is. This is the function of metadata, which describes how data was gathered, formatted, and organised. Before collecting data, think about which metadata to track and save with the information – either in the programming tool used to collect the information or in a README file. Whatever approach you choose, bear in mind the long term. You might decide to link your data with those of other labs at some point. If you plan and manage your metadata effectively, this integration will be simpler for you.



Traditional data sets are too enormous to go over manually. Therefore, automation is required. When it’s time to combine data from a different source into a more extensive database or collection, Apache Spark and Hbase, two open-source software packages, can be used to check and repair data in real-time.


Focus On High-performance Computing


Large data sets necessitate high-performance computing (HPC), and many research institutions have HPC systems. Time is money when it comes to computing. Before migrating its analyses to the HPC network, one must conduct little-scale tests to make the most of computer time.


Capture The Environment

To replicate an analysis, you’ll need the same version of the tool and the same operating system and all of the required software libraries. Because of this, work in a self-contained computing environment- a Docker container that can be put together anywhere.


Do Not Download Data

It is not feasible to download and retain colossal data sets. Researchers should perform computations remotely, near the data they are working with. Jupyter Notebook, designed to analyse big data, creates documents that include software code, text, and figures. Researchers may “spin up” such papers on or near the data servers to conduct remote studies, analyse the data, and more. Jupyter Notebook isn’t particularly user-friendly for researchers who are new to the command line, but there are alternative platforms that can help them bridge the gap, such as Terra and Seven Bridges Genomics.


Be Proactive

Data management is critical for young professionals, so begin learning as early as possible. Start with the command line fundamentals and a programming language such As Python or R, whichever is more important to your area. Get comfortable using the command line to access data.



A more intelligent data management technique not only helps back up Big Data efficiently but also makes it more readily recoverable and accessible with enormous cost savings – freeing IT staff to focus on more strategic technology initiatives that drive corporate growth rather than fighting a battle against an out-of-control Big Data monster.