Scraping data from PDFs is a right of passage if you work in data. Someone somewhere always needs help getting invoices parsed, contracts read through, or dozens of other use cases. Most of us will turn to Python and our trusty list of Python libraries and start plugging away. Of course, there are many challenges…
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A key responsibility for any data team is to understand the core metrics driving their business. Starting from the top, these metrics often include figures like gross revenue and expenses. However, these high-level metrics can feel too far removed and abstract from the actual business. Many companies, therefore, break down these top-line metrics into more…
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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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What would you do if you became the head or director of data for a 1,000-person company? Yesterday, you were plugging along as an analyst, and now, suddenly, you have all these new responsibilities. Figuring out where to start is part of the job. You’d probably feel a strong temptation to freak out. Who wouldn’t?…
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Recently, I wrote an article diving into what Druid is and which companies are using it. Now I wanted to do a deeper dive into Apache Druid’s architecture. Apache Druid has several unique features that allow it to be used as a real-time OLAP. Everything from its various nodes and processes that each have unique…
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SQL Server Integration Services (SSIS) comes with a lot of functionality useful for extracting, transforming, and loading data. It can also play important roles in application development and other projects. But SSIS is far from the only platform that can provide these services. You might seek alternatives to SSIS because you want a more agile…
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Companies that range from start-ups to enterprises are looking to implement AI and ML into their data strategy. With that it’s important not to forget about data quality. Regardless of how fancy or sophisticated a company’s AI model might be, poor data quality will break it. It will make the outputs of these models useless…
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I once had an engineer tell me that they essentially didn’t want to consider cost as they were building a solution. I was baffled. Don’t get me wrong, yes, when you’re building, you iterate and aim to improve your solutions cost. But from my perspective, I don’t think completely ignoring costs from day one is…
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Photo by Shubham Dhage on Unsplash Learning about how to data models from basic star schemas on the internet is like learning data science using the IRIS data set. It works great as a toy example. But it doesn’t match real life at all. Data modeling in real life requires you fully understand the data…
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Photo by Leif Christoph Gottwald on Unsplash A few months ago, I uploaded a video where I discussed data warehouses, data lakes, and transactional databases. However, the world of data management is evolving rapidly, especially with the resurgence of AI and machine learning. There are numerous other methods that technical teams are utilizing to handle…
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