Here is something that remains true in the era of AI. Data is still siloed and hard to access. Are there plenty of tools that can help make data ingestion and ETL easier? Yes, but they aren’t all created equal. One established solution is CData. According to CData, its products provide live connectivity and data…
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Every morning, your team wakes up to over one hundred data quality alerts. I know, I recall this issue at Facebook. Some of them would be fixable issues others should have just been warnings. It’s so easy to build data pipelines and add data quality checks these days that I am sure for some people…
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When you demo a new data tool, everything works perfectly. Account executives are great at making it sound like their product is the perfect product. They make sure their data pipelines run smoothly. The demo dashboards that load in seconds. And of course, the graphs and charts they put together look pretty impressive. So, you…
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Data teams continue to debate which is better: ETL (Extract, Transform, Load) or ELT (Extract, Load, Transform). Meanwhile, they are overspending, pipelines are breaking, or no one is utilizing the data. So, does this debate even matter anymore? Ok perhaps that’s harsh but I do always find it a little funny when people still talk…
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We are several years into the AI Revolution, so to speak, and with that has come an increased demand for data. The increased demand for data comes an increased demand for data infrastructure. Some companies already have reliable data stacks; others are looking to migrate to Snowflake, Databricks, or some other solution(I am sure some…
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The demand for data has grown substantially in this AI-driven world. Meaning, there are more and more data pipelines being created. The funny thing is, when I first started in the data world, no one around me used the term data pipeline. I am sure plenty of data teams used the term data pipeline. But…
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Whether you’re working at a large enterprise or a small business, there’s almost always a need to extract data from source systems, process it, and use it for operational or analytical purposes. That process, moving data from point A to point B, transforming it along the way, and making it usable, is what we typically…
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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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Maybe you’re luckier than me. Maybe you’ve never opened a .sql file or an Airflow DAG only to be greeted by a 5,000+ line query…a true monster of a script that leaves you wondering where to begin. I’ve seen plenty of these, and every time, I ask myself: Why in the world do these exist? And, more…
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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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