Why Conventional Data Exchange Methods Solve Less Than Half Of The Problem

Enterprise technologies are going through a period of rapid change and they are facing problems in navigating through digital economies. The most common problem is aligning technologies that are primarily data intensive. Digital technologies like sensors, Internet of Things (IoT) are generating huge piles of data. Studies indicate that enterprises in digital economies are generating 2.5 quintillion bytes of data every day.

This growth will heavily impact enterprises with conventional technologies to process data. They will struggle in piping this data and preparing it for analytics or monetizing B2B networks. Enterprises will require near-zero latency or large file data ingestion capability to process data between different dimensions.

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Large Data Sets And Industry Challenges

Previously only employees in an enterprise were generating data. However, in the present scenario, enterprise systems & smart machines like sensors, gadgets, machines, etc. are also generating data. Due to this, enterprises across the globe are dealing with an exponential growth in data.

In the current scenario, enterprises are generating several Petabytes and Exabytes of data while executing business operations. Where one Exabyte is equal to 36,000 HDTV video years or 3000 Netflix catalog times. Data generated in some months is greater than the data generated in 20 years. Gartner believes that global data will increase by 40 Zettabytes: with structured data growth by 40% and unstructured data by 80%. Sandboxes, pilot environment, and siloed IT will prevent organizations from moving this structured or unstructured data across different systems. Here are some problems that companies face while processing the data:

Large Data Processing Can be Engineering Intensive: Processing high velocity, high volume, and high variety of data can be increasingly challenging. The data in JSON, NoSQL or unstructured formats need to be parsed and again combined after post-processing. This method is engineering intensive and it is error-prone. Enterprises are restrained by hardware and network bandwidth to process data. In many cases, incomplete data is processed in the source systems.

Errors and Outages deliver Performance Lags: Large databases include an array of hardware & software stacks for running different operating systems and packages. It needs different plugins, containers to run properly. These components need to be updated from time to time. Because of these updations, databases gain volume and become difficult to manage. IT and business teams encounter several memory crashes and server errors.

Lack of Monitoring and Governance: IT teams don’t have proper triggers for low latency processing. They face problems in curating, visualizing, and storing data. Teams cannot manage larger data sets and scale them for monitoring information. SQL based datasets solve only half of this problems as they cannot scale naturally for bigger or faster data sets. Moreover, binary elements provide limited flow options.

As a result, enterprises don’t get a clear picture of events and they cannot diagnose overlapping issues. Many process related issues go unnoticed. And enterprises fail to meet the service level agreements. Large file data processing requires near-zero latency for executing queries. They should have the ability to do execute multidimensional queries on large datasets in few milliseconds.

All these problems can be avoided with a software-based large file data integration solution instead of a conventional point-to-point integration solution. This advantage helps teams in moving files without costly appliances and maintenance. Teams can exchange files smoothly over a network, transact faster and productize complex B2B environments.

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Conventional methods of large file data ingestion cannot address the problems of modern-day B2B networks. Know the common pitfalls of using them for processing large data sets.

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