How to Modernize Data Integration

Content By Devops .com

In times of uncertainty, accurate real-time data becomes essential to informed decision-making. When the pandemic ends and businesses begin to reopen, this will be truer than ever. While there is no precedent for COVID-19’s impact on the economy, what is clear is that the enterprise is clamoring for more analytics than ever. Data engineering is at the heart of operationalizing the delivery of that data, wherever it originates and wherever it’s needed.

To operationalize data flows, engineers must be able to collaborate with different personas, reuse peer assets, support data pipelines in production and, as platforms and business requirements change, evolve quickly and with confidence. The focus of data engineering can no longer be limited to ease-of-use and developer productivity. In today’s fast-changing world, we need to enable modern engineers to embrace DataOps. In doing so, they will be able to guarantee continuous data for fast, confident decisions by and for the business.

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Current Options Are Too Simple or Too Complex

Current approaches follow either a “grab-and-go” mentality that focuses on simplifying ad-hoc access for first-time analysis, or lurches towards specialized coding for anything complex. Both of these approaches lead to ongoing and recurring headaches. Significant effort is required to debug data pipelines, rerun them and rework them every time a change happens. Today’s data engineers end up spending 80% of their time just keeping the lights on, leaving very little time for new value-added work.

Using Intelligent and Responsive Data Pipelines

Data engineers face mounting pressures and challenges, from an ever-increasing project backlog to an accelerating pace of upstream changes to the emergence of cloud data platforms driving re-platforming projects. To tackle these issues, engineers need a solution that allows them to operationalize their work by abstracting the “what” of the data—the business meaning and the logic—away from the “how” of the data—the technical implementation details that the business doesn’t care about. To achieve this, businesses need to think about data pipelines as dynamic, intelligent sources that can (and should) be capable of real-time adjustments.

There are three ways that data pipelines can be considered smart or dynamic: they are easy to start and easy to extend; they are resilient to changes that occur over time and they are portable, supporting hybrid and multi-cloud environments.

Companies that have focused on removing the “how” by developing intelligent and responsive systems free up their engineers to stay focused on the “what” – what are the outputs? Are the analytics providing the right ROI or information? They also make it easy to get started building pipelines through intent-driven design, removing tedious tasks for engineers and empowering ETL developers to self-service. This includes the repetitive task of managing data drift, whether it’s a schema change, a database version upgrade or a file format change. Smart pipelines handle such changes with minimal to no intervention required.

Demand Continuous Data for the Business, Now

COVID-19 has segmented businesses into have-lots, haves and have-nots. The have-lots have experienced unimaginable growth and, consequently, are generating incredible amounts of data. They have had to deliver better engagement with their constituents, optimize their product offerings and stop bad actors, fraudsters and cybercriminals from exploiting the unexpected surge in demand for their business. If they have risen to the challenge, you can bet that they have modernized their data practice and can deliver continuous data to all consumers.

The haves and have-nots have generally been in survival mode, operating under short-term mindsets to reduce cost, consolidate as much as possible and wring out any waste. Depending on each individual situation, that may have meant staying on legacy platforms and postponing modernization efforts. Some have enjoyed cost savings by reducing consumption and negotiating with their vendors. Others have made a tactical move to the cloud, but simply as a path to lower costs of their data platforms. Only a select few have done the right thing and made the complementary investments in data engineering to reap the full benefits of their shift to cloud.

As this pandemic comes to an end, the prospects for the haves and the have-nots will start to look more promising. If you have been focused solely on keeping the lights on and managing costs, you may have been mortgaging your future. This needs to end. Don’t wait. By modernizing your ETL to create more dynamic, real-time data pipelines now, your engineers can operationalize their work, deliver a new level of innovation and self-service to the business and demonstrate their true worth as data heroes.


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