Open File format in data analytics and AI - changing the international rules game
What do open file formats in data analytics, Aussie Rules football, and Gaelic Football have to do with each other? Sort of nothing – it's just an analogy that'll make sense after a few more paragraphs. Hang in there!
If you work in data and AI, you're likely familiar with data lakes, lake houses, and powerful analytical engines like MSFT Fabric, Snowflake, and Databricks. But it's important to recognise that the backbone of all this lies in data storage using open file formats. These formats support efficient data management and play a critical role in ensuring data interoperability, future-proofing, and optimal performance and scale.
In my opinion, their biggest advantage is their open nature – they allow for greater flexibility and reduce dependency on a single technology. I've worked on too many programs where a customer signed up for a data ecosystem using Oracle, SQL Server, or another, only to later switch platforms for various reasons. This often leads to significant and costly migration efforts to rework and remediate the data infrastructure, the logic and the data itself. Open file formats can help mitigate such challenges, saving time and money in the long run and provide huge performance and cost benefits, flexibility and scale.
So, let’s look at open file formats, and why you should care if you are working through a data strategy, data transformation, or looking to get going with something modern.
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