How We Used Big Data Techniques to Query Millions of Transactions
With millions of transactions a day on our banking app, we extracted and transformed the data so we could query the transactions we needed.
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One of the challenges of working on a live banking application that handles millions of transactions a day is that the larger the amount of data, the harder it is to query it. Here’s how my development team extracted and transformed the data so we could query the transactions we needed.
Because we’re in the financial sector, which the government regulates, delegated government auditors need unrestricted read access to our transaction data. They need integrity and consistency of the data, so they can read and query it when required.
We used Extract, Transform and Load (ETL), a big data technique, to store and transform the data so the auditors could easily query and analyse it. ETL is the process that transforms raw data into structured, ready-to-query data on a schedule or on-demand basis.