Data Science on JSON using SlamData REFORM and RStudio or Jupyter Notebooks

Obstacles in doing/learning Data Science Whether you’re a professional data scientist or studying to become a data scientist, you’ll likely need to work with a JSON dataset. JSON isn’t easy to work with. It’s not tabular, and you can’t just push it to a SQL database — at least not without a “bit” of work. […]

Turn any API into an analytic ready data source

There are over 200,000 API’s on the web today, and more being added all the time. And the vast majority of these API’s provide JSON data via a REST interface. Unless you are a developer you generally can’t do much with this data or get any value from it. API’s are by design built for […]

Reinventing ETL on a New Mathematical Foundation

What is MRA? The Multidimensional Relational Algebra, or MRA, is at the core of everything we do at SlamData. It’s what enables our product to provide such a smooth and intuitive user experience. It’s what drives the ahead-of-time optimizations and static analyses which ensure that transformations are evaluated with extremely high performance as close to […]

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Why Organizations Want To Solve The JSON Problem

JSON stands for JavaScript Object Notation, and it’s a way to format data. It was developed in the early 2000s, but it’s only in the last few years that it’s really caught on. JavaScript spec now includes a JSON object, and many developers are incorporating JSON as a sort of subset of the language itself. […]

What To Do When You Don’t Have A Data Integration Engineer

You’ve probably been hearing about “big data” for a while now — the term has been around for years, and in the meantime, data has only gotten bigger. A whole industry has sprung up around it, from collection to storage to analytics, and data integration engineers are part of that puzzle. But what if your […]

The Final Frontier — The Missing Link In ETL/ELT

The ETL (extract, transform, load) industry has been around for decades. Its primary purpose is to move data from source locations to data warehouses so analytics and data science teams can access the data in one place and perform analysis across a range of critical data sources. More recently, the rise of low cost cloud […]