Why Companies are moving away from Batch Streaming Data?

Data processing is one of the most challenging tasks for an organization. And, due to the massive explosion in the way organizations actually want to mine and analyze data for a variety of operations, processing data has become all the messier and to a point ugly. The messy approach to processing data could be attributed to many reasons, such as poor data management techniques, poor data management records, and so on. However, a majority of problems can be solved by adopting a stored batch streaming data processing approach. Batch streaming creates more problems than it solves.

Let’s understand how to solve the critical problems in data processing by moving away from contemporary batch streaming data techniques.

Establishing Correlation between Different Streams of Data

Analyzing the problems linked with different types of data can’t be addressed in real time. If we talk about data management processes from the early 2000s, we would find that a large group of data streams would suffer from multiple problems, and these could be only identified after weeks of scrutiny. Correcting these data streams would take much longer.

Things changed with the arrival of better tools and techniques, and batch streaming data is the reason why data ops teams are mostly unable to detect missing data and identify problems.

Data Ops teams need something that works in real time and that’s why moving away from batch streaming has happened so quickly now in 2021. Data discovery has become easier with streaming data, even as companies are increasingly looking at agile ways for data integration to present greater opportunities to themselves that cut through the noise, mess, and risks involved in data management and analytical processes.

Simplifying Real time analogies

Modern business applications require a modern approach to solve critical problems linked with networking, remote workplace collaboration, and data storage. The whole paradigm shift with remote workplace culture has forced companies to switch to hybrid clouds. In hybrid clouds, data op can’t work effectively with batch streams. Instead, they prefer to work with something that delivers real-time data streams, something that aligns with the advancing demands from digital transformation and cloud modernization.  In order to communicate effectively in real time scenarios, treating data in real-time is the most productive approach.

Solving Data Storage issues

An infinite volume of data is generated from so many different sources such as websites, social media, IoT devices, servers, applications, and connections. It is impossible to keep a track of where these data come from and where they will lead to; until these are regulated, monitored, and processed in a meaningful manner. Switching to real time streaming helps in providing a “timestamp” to every attribute linked to data processing.

A part of the challenge linked with what we call Big Data management is solved by going away from batch streaming. As such big data applications and platforms are available for Data Ops teams to ingest, process, and structure an infinite volume of data, yet it all boils down to the storage aspect of data management.

Working in non-batch streaming allows data ops teams to seamlessly gather and mine real-time intelligence from a unified source of data, which could be further refined and re-engineered for building advanced applications. In short, a large chunk of data would be trashed and that’s how organizations get rid of useless data by junking batches in a large percentage.

So, moving away from batch streaming solves the perennial problem involved with the storage of data, especially when Big Data projects are getting stacked in a time sensitive manner that requires all the analysts and IT engineers to work cohesively and competently in a modernized Cloud environment.

Moving to Open Source Projects

We are hearing a lot of talk happening on topics such as Auto Machine Learning, Advanced AI, and Supercomputing. Programmers are comfortably regrouping in open source ecosystems to develop No Code, Low Code, and Open code applications that require little or no expertise with working with “behind the scene” data processing tools. This happens when open source developers decide to move completely beyond the perennial issues linked with batches.

Organizations gain more from data in motion than what is coming out of batch processing.

Record by record processing also opens up new opportunities for Data Ops as far as working with open source platforms is concerned. These are Apache and Hadoop’s family of streaming frameworks that are already making a huge wave with time series analytics with data arriving with a timestamp.