One of the questions most data engineers need to answer is whether this data pipeline should be real-time or batch. Sometimes posed as streaming vs batch. The tools you might use to do that have changed over the past few years, but that was always the question. The business, of course, would always ask for…
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After working in data for over a decade, one thing that remains the same is the need to create data pipelines. Whether you call them ETLs/ELTs or something else, companies need to move and process data for analytics. The question becomes how companies are actually building their data pipelines. What ETL tools are they actually…
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Leading data teams can be challenging. You’ve got management and non-technical teams constantly reaching out with ad-hoc data requests; you’re likely trying to figure out what tools will work best and not blow the bank. Not to mention, you’ve got to bridge the gap between business and technology. All while trying to grow your data…
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One of the holy grails that many data teams seem to chase is real-time data analytics. After all, if you can have real-time analytics, you can make better decisions faster. However, there often is a conflation between real-time data analytics and stream processing. These are two different concepts that are crucial to understanding how to…
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Running a successful data team is hard. Data teams are expected to juggle a combination of ad-hoc requests, big bet projects, migrations, etc. All while keeping up with the latest changes in technology. In the past few years I have gotten to work with dozens of teams and see how various directors and managers deal…
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Getting data out of source systems and into a data warehouse or data lake is one of the first steps in making it usable by analysts and data scientists. The question is how will your team do that? Will they write custom data connectors, pay for a data connector out of the box or perhaps…
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If you’ve worked in data long enough, then you’ve likely come across the term change data capture. Often called CDC, change data capture involves tracking and recording changes in a database as they happen, and then transmitting these changes to designated targets. This can be crucial because some pipelines, in particular batch pipelines, don’t capture…
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As data increased in volume, velocity, and variety, so, in turn, did the need for tools that could help process and manage those larger data sets coming at us at ever faster speeds. As a result, frameworks such as Apache Spark and Apache Flink became popular due to their abilities to handle big data processing…
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Have you ever been part of a data or software project that seems stuck in a loop? Three weeks have passed, and although you arrive at work daily, exhausted, having tackled numerous issues, the project remains stagnant. Why? Then, suddenly, a new engineer or project manager steps in, reorganizes and prioritizes tasks, and just like…
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No matter your industry, you’ll often need to make split-second business decisions in the digital age. Real-time data can help you do just that. It’s information that’s made available as soon as it’s created, meaning you don’t need to wait around for the insights you need. Real-time data processing can satisfy the ever-increasing demand for…
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