Data Integration Best Practices

How to Avoid Painful Data Problems by Following these Best Practices

Many companies struggle to get visibility to their data but it doesn't have to be that hard. By following a few best practices, data integration can be easy to set up and manage, making it easy to get insights across all your systems.


Most people think that you just get data from an API, stuff it into a database and then you are done. Voilà! Instant insights!
The reality is that data integration and aggregation is a little more complicated than that.
When we import data, we do several things in order to ensure that we can trace back the history of the data over time:


We build import processes that can be re-run when necessary (either at the database level or at the day-level.) To do this we follow a few best-practices when importing data:


We built standardized logging at the database and application level in order to trace back history of every job. Some of the logs that we create are:

Quality Checking

We create checks as part of our processes that ensure the quality of the data across calculated tables including:


We create alerts via channels like SMS, Slack and email when errors occur, making sure that problems can be quickly identified and resolved.

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