Data Observability: How to Prevent Broken Data Pipelines

Data Observability: How to Prevent Broken Data Pipelines

Data Observability: How to Prevent Broken Data Pipelines. The relationship between data downtime, observability, and reliable insights

While the technologies and techniques for analyzing, aggregating, and modeling data have largely kept pace with the demands of the modern data organization, our ability to tackle broken data pipelines has lagged behind. So, how can we identify, remediate, and even prevent this all-too-common problem before it becomes a massive headache? __The answer lies in the data industry’s next frontier: _[**_data observability](https://towardsdatascience.com/data-observability-the-next-frontier-of-data-engineering-f780feb874b).**

When you were growing up, did you ever read a Choose Your Own Adventure novel? You, the protagonist, are responsible for making choices that will determine the outcome of your epic journey, whether that’s slaying a fire-breathing dragon or embarking on a voyage into the depths of Antarctica. If you’re in data, these “adventures” might look a little different:

The Data Analyst Quest

It’s 3 a.m. You’ve spent the last four hours troubleshooting a data fire drill, and you’re exhausted. You need to figure out why your team’s Tableau dashboard isn’t pulling the freshest data from Snowflake so that Jane in Finance can generate that report… yesterday.

The Data Engineering Escape

You’re migrating to a new data warehouse and there’s no way to know where important data lives. Redshift? Azure? A spreadsheet in Google Drive? It’s like a game of telephone trying to figure out where to look, what the data should look like, and who owns it.

data database data-engineering data-science data-quality big-data

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