DataOps: A Pathway To Accelerate Data Acquisition
Whether your company is dealing with the sudden onset of a market downturn (as we currently experience), or you are dealing with a fast-growing.
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Updated November 2026. This was first published on 17 May 2020. The idea is the same. Any number below is what the original piece said then. It is not a new count.
Key takeaways
- Whether your company is dealing with the sudden onset of a market downturn (as we currently experience), or you are dealing with a fast-growing, hyper-competitive market- getting timely and accurate insights in the hands of the right people can always help.
- It improves the impact of business decisions.
- We live in the information age with seemingly endless sources of data, which we could access to help drive insights.
Whether your company is dealing with the sudden onset of a market downturn (as we currently experience), or you are dealing with a fast-growing, hyper-competitive market- getting timely and accurate insights in the hands of the right people can always help. It improves the impact of business decisions. We live in the information age with seemingly endless sources of data, which we could access to help drive insights. But, this abundance is proving to be one of the biggest challenges to getting value from data fast enough and at the enterprise-scale that we need it. Every data-intensive process considers data as a commodity. But, it is a very crucial ingredient to business insights or analytics. Around 70-75% effort in almost every analytics project spent towards acquiring and curating the required datasets. Creating a strong DataOps strategy can provide a powerful approach to bring this world under control.
The modern data or information ecosystem is changing**.
Before we get into the DataOps specific conversation, it is worthwhile understanding why traditional approaches are no longer working as intended.
Data is not linear anymore:** Previously, we used to deal with linear patterns where data origination was mainly in on-premise operational systems. Today, we see an explosion in the data sources, and a comprehensive data set, from every possible source of data, is needed to drive accurate and data-oriented decisions. Multi-dimensional data architecture:** Besides, multi-cloud and hybrid architectures are becoming quite popular. It is quite clear that there is just no one size fits all anymore. More ways to process your data:** We always had great choices of databases. recently the emergence of cloud data processing technologies has opened new and more agile data lakes or data warehouses that require zero setups. The challenge is that each brings unique process characteristics or processing languages (e.g.-SQL) that we need to maximize.
DataOps emerging to operationalize this modern data to insight value chain #
The traditional way for us to get the right data into a place for the analysts or data scientists was heavily dependent on manual development efforts performed by IT resources. DataOps has emerged as a methodology and technology framework to break this dependency and bring acceleration and scale to your data to the insights value chain.
From my experience over the past number of years working with enterprises big and small, I found the following principles to reflect a practical approach to what you should target in your DataOps journey.
Automation of creating data pipelines:** The key is to minimize the human intervention in developing the pipelines that bring data from your application into your data lake. If you already move one table or file from an on-premise database to your Snowflake data lake, adding 10 or 100 more tables should not require massive human intervention. Also, adding standard routines like data cleansing operations or audit columns should be part of the automatic pipeline generation. Intelligent optimization:** With the range of data processing technologies, each with their characteristics and unique SQL dialects, it is necessary to intelligently and automatically be able to optimize the data pipeline executions. It is essential to pick the right processing patterns - whether parallel performance across multiple nodes based on your data set or whether push down into the source database for faster execution. Auto healing:** Moving bad data into data lakes impacts every downstream decision process. It is necessary then for intelligent rules to be defined on the data to ensure that bad data such as wrong date formats or missing data elements. There should be flags that determine whether to stop the pipeline or ignore the errors and continue loading the rest of the data. Intelligent monitoring:** Instrumenting your automated data pipelines need to augment with end to end tracking at a very granular level. Whether it is to understand why a job that usually takes 2 minutes to complete, ran for 15 minutes tonight, or whether a job failed to complete at all - we need the insights into our data pipelines to intervene and bring corrections rapidly.
Diyotta can easily augment the DataOps for the enterprise. It can establish a firm foundation with the above principles so that the data team can focus on solving real business problems instead of spending time on something which can be commoditized, i.e.-data.
Accelerating data pipelines with DiyottaMatic #
Diyotta was founded with the vision to build the next-gen data integration platform designed from the ground up for this multi-cloud hybrid world. What makes Diyotta unique is that it has been architected to scale to today’s data volumes, reduce the complexity of managing data pipelines across our complex data landscape, is enterprise-ready, and offers the lowest cost of ownership in the market.
DiyottaMatic** includes a set of utilities that are built around our proven platform to automate the creation of your data pipelines intelligently.
Reduced development effort by approx 80% Reduced manual errors as the pipelines are auto-generated Reduced maintenance as there are no manual scripts or code to maintain Apply best practices using pre-configuration for various extraction and load properties & data cleansing routines. Addition of control & audit metadata along with the data lineage for better audibility and governance.
Questions and answers #
What is the main point? #
Whether your company is dealing with the sudden onset of a market downturn (as we currently experience), or you are dealing with a fast-growing, hyper-competitive market- getting timely and accurate insights in the hands of the right people can always help.
What else did the original piece say? #
It improves the impact of business decisions.