Data drives everything we do. For this reason, data analysis has become one of the most important elements of programming, engineering and testing across organizations. But traditional data management techniques are failing businesses by being unable to cope with hugely complex data sets.
It’s important to be able to process these sets because of their use in building large technical systems like an IBM mainframe, on which many widely-used systems are built.
Complexity in data sets is caused by its size and diversity, but also in the size, geographical and experiential diversity of data processing teams. The growth of data in industry is, paradoxically, causing chaos that is resulting in data project failure.
This is where DataOps come in, as a potential solution to data chaos and project failure.
DataOps is a set of defined practices and processes that aims to place data at the center of optimization by promoting speed, quality and collaboration in data analytics.
You can think of it as a culture or way of working, focusing on communication between different data professionals and integrating various tools and development principles into a cohesive way of processing data.
DataOps is more than just a single tool or method. It’s an approach to data processing that aims to reduce error and allow systems to manage large data sets without loss.
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For example, think of an API. What is an API? A piece of software that facilitates and defines interactions between pieces of software. When developing these, developers collect huge data sets because an API works between many different applications.
Traditional data processing methods may not be capable of storing or effectively processing such data.There are a few key benefits to DataOps that makes it an effective approach to data management.
These are the benefits of DataOps, but it’s also important to understand the factors affecting traditional data processing methods. The three major components of traditional processing each have their own issue that is solved by well-implemented DataOps.
There are four principles that underpin DataOps, each of which must be properly implemented for this process to work well and enable your team to store, process and manage large data sets.
These are the four principles of DataOps, all of which take a different viewpoint on development and the ways in which information can be managed.
Pohan Lin is the Senior Web Marketing and Localizations Manager at Databricks, a global Data and AI provider connecting the features of data warehouses and data lakes to create lakehouse architecture. With over 18 years of experience in web marketing, online SaaS business, and ecommerce growth. Pohan is passionate about innovation and is dedicated to communicating the significant impact data has in marketing. Pohan Lin also published articles for domains such as PingPlotter.
Disclaimer: The author is completely responsible for the content of this article. The opinions expressed are their own and do not represent IEEE's position nor that of the Computer Society nor its Leadership.
