Photo by Nana Smirnova on Unsplash There is a lot of discussion on how you should implement your data stacks today. Should you bundle or unbundle, should you use a traditional data warehouse, lakes, lake houses, or mesh? Or a hybrid of all of them? With all these different ways you can put together your…
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Photo by Pawel Czerwinski on Unsplash Big data is touted as the solution to many problems. However, the truth is, big data has caused a lot of big problems. Yes, big data can provide more context and provide insights we have never had before. It also makes queries slow, data expensive to manage, requires a lot of expensive…
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It’s becoming difficult to keep track of all the new data warehouse solutions that are trying to challenge the current incumbents. Choosing the best data warehouse to meet the needs and objectives of your operation is a crucial component of your business strategy. Unfortunately, many organizations are still struggling with this decision. To add to…
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Companies of all sizes are looking for faster and easier ways to take advantage of data through analytics and BI. In particular, knowing who your customers are, how they interact with your business, products and services is a modern necessity. In a customer-centric world where data can help improve the overall customer experience and relationships,…
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Photo by Myriam Jessier on Unsplash The modern data stack (MDS) is a new approach to data integration capable of saving your engineers time while allowing both engineers and analysts to focus on high-value pursuits. With a suite of tools to support data integration, the modern data stack will free your teams of monotony while empowering…
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Maybe it’s the 6 figure salaries, the opportunity to work with cool technology or people are finally learning that data engineering is where everything starts in the data field. Whatever the reason, people are noticing. VCs are investing in data storage and ingestion platforms and companies are interviewing more data engineers compared to previous years. But how…
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Scaling data talent is hard. It’s hard to hire. Hard to grow and just overall hard in terms of managing processes and output. But there are brilliant leaders and managers doing it everyday. I recently interviewed Veronica Zhai, the principal product manager at Fivetran. She has led Fivetran’s analytical team for nearly the last year…
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Photo by Christina @ wocintechchat.com on Unsplash Why does it feel like so many more articles are discussing the data engineering profession? Perhaps it’s because Dice’s 2020 tech jobs report cites data engineering as the fastest-growing field in 2020, increasing by a staggering 50%, while data science roles only increased by 10%? Or maybe it’s just because the Medium…
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Photo by ThisisEngineering RAEng on Unsplash 2021 is almost halfway over and it seems like hundreds of millions of dollars has gone into investing in data, data start-ups and machine learning. In particular funding has also shifted heavily from just focusing on the data science and machine learning space to the data engineering and data management…
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Software engineer vs. data engineer. People outside these professions are often confused about the differences between these experts. They may think these are two different names for the same thing: a programming expert. Of course, the duties of each engineer type are different, but their roles are similar enough to confuse people who are in…
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