This blog explores how Meta optimized tail utilization to enhance the performance and reliability of its ads inference service. Through innovative load-balancing techniques and infrastructure improvements, Meta achieved a 35% increase in work output, a two-thirds reduction in timeout errors, and a 50% decrease in p99 latency.
This blog post explores Paimon's innovative approach to integrating with Flink, offering real-time data streaming, efficient changelog handling, and unified storage for batch, OLAP, and streaming data. Learn how Paimon can enhance your data processing capabilities and streamline your analytical solutions with Flink.
This article explores the role of data observability tools in ensuring data quality, managing transformations, and delivering reliable analytics, drawing insights from our experience with 1,000 data teams.
In this article, Martin discusses how he would choose between SQL and No-SQL databases for a solution. He explores the roles of structured and unstructured data in this decision and other factors. This decision-making process can be complex.
AWS introduces OpenLineage-compatible data lineage visualization in Amazon DataZone, enhancing data movement and transformation tracking.
This blog post explores race conditions and changelogs in Flink SQL, highlighting potential pitfalls and solutions for ensuring data consistency and reliability. We'll cover changelogs' mechanics, race conditions' impact, and practical mitigation strategies, helping you maximize Flink SQL's potential in streaming applications.
Unit and integration testing in Databricks has been challenging, but recent advancements like Databricks Serverless Spark make continuous integration more efficient. This guide provides practical steps to streamline your testing processes and reduce costs.
Listen to a talk with three Kubernetes leaders about its evolution and current efforts to support AI/ML workloads in open-source Kubernetes.
Learn how to create a production-grade MLOps platform using Kedro, MLflow, and Terraform, enhancing your team's productivity.
Join a hands-on lab to explore Snowflake's end-to-end ML capabilities, from feature engineering to model deployment.