I like to think of data quality management within the context of physical fitness. You can get in shape with hard work, but staying in shape requires good habits. And above all else it's a mindset and a lifestyle. You also have to sweat. At least a little bit. No technology, including data observability, can act as a slimming belt toning your data quality while you lay back and … [Read more...] about Data Quality Management: 6 Stages For Scaling Data Reliability
big data engineer
What Good Data Product Managers Do – And Why You Probably Need One
Atul Gupte co-authored this post. The companies we talk to are diligently building their data product or platform. This includes migrating to Snowflake, integrating with Databricks, moving towards a data mesh, or generally investing in their data stack. Increasingly, we are seeing data departments modernize their team structure with data product managers at the helm of such … [Read more...] about What Good Data Product Managers Do – And Why You Probably Need One
You Have More Data Quality Issues Than You Think: Here’s Why.
Say it with me: your data will never be perfect. Any team striving for completely accurate data will be sorely disappointed. Data testing, anomaly detection, and cataloging are important steps, but technology alone will not solve your data quality problem. Like any entropic system, data breaks. And as we've learned building solutions to curb the causes and downstream impact of … [Read more...] about You Have More Data Quality Issues Than You Think: Here’s Why.
What’s Next for Data Engineering in 2023? 13 Predictions
What's next for the future of data engineering? Each year, we chat with one of our industry's pioneering leaders about their predictions for the modern data stack - and share a few of our own. A few weeks ago, I had the opportunity to chat with famed venture capitalist, prolific blogger, and friend Tomasz Tunguz about his top 9 data engineering predictions for 2023. It looked … [Read more...] about What’s Next for Data Engineering in 2023? 13 Predictions
5 Strategies For Stopping Bad Data In It’s Tracks
For data teams, bad data, broken data pipelines, stale dashboards, and 5 a.m. fire drills are par for the course, particularly as data workflows ingest more and more data from disparate sources. Drawing inspiration from software development, we call this phenomenon data downtime- but how can data teams proactively prevent bad data from striking in the first place? In this … [Read more...] about 5 Strategies For Stopping Bad Data In It’s Tracks
