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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Is your data team constantly feeling the pressure to deliver? Do members of your team say they feel like they’re doing work meant for two people? If the answer to either or both of these questions is a resounding yes, you may feel tempted to think, “We just need more hands on deck.” However, hiring…
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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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Over the past three years our teams have noticed a pattern. Many companies looking to migrate to the cloud go from SQL Server to Snowflake. There are many reasons this makes sense. One of the reasons and common benefits was that teams found it far easier to manage that SQL Server and in almost every…
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If you work in data, then you’ve likely used BigQuery and you’ve likely used it without really thinking about how it operates under the hood. On the surface BigQuery is Google Cloud’s fully-managed, serverless data warehouse. It’s the Redshift of GCP except we like it a little more. The question becomes, how does it work?…
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