July 2022 Summaries

The cloud data platform, Snowflake, has gained prominence due to its easy startup costs, low ops, and instant scalability. However, users have realized that monitoring their database is crucial for preventing issues such as degraded data reliability, performance bottlenecks, and cost overruns. While traditional data governance and reporting tools can spot-check inconsistencies and test for data quality at single points in time, they are not suitable for continuous data validation and testing. A better solution is a unified, continuous data observability platform like Acceldata that automates the continuous data validation and testing needed to create organization-wide trust in data, as well as hiccup-free throughput and optimized price-performance.

The blog ranks 12 common failure metrics for data engineers, starting from the least relevant ones like Mean Time To Failure (MTTF) to the most important ones such as Mean Down Time (MDT). MTBF and MTTR are also considered key metrics that all data teams should employ. A continuous data observability platform like Acceldata can help optimize these failure metrics by automating anomaly detection, workflows, RCA, fixes application, and more.

In the recent CDO Conversation between Sharath Gokula, Director of Data Sciences and Analytics at Walmart eCommerce, and Tristan Spaulding, Head of Product at Acceldata, they discussed how data is crucial for understanding customer behavior and driving business decisions. They highlighted that having more data isn't enough; the quality and reliability of the data are key to gaining a competitive advantage. Furthermore, modern CDOs need skills across various disciplines such as analytics, data engineering, and machine learning to foster a data-driven culture within their organizations. The discussion also touched upon how Walmart subsidiary PhonePe is leveraging data observability to scale its data infrastructure by 2000% and save $5 million annually.