1.How HomeToGo improved our superset monitoring framework
How HomeToGo improved our superset monitoring framework?
In this article, HomeToGo share with you how they have iterated and improved our previous Superset monitoring from both technical and the actionability standpoints AtHomeToGo, the authors revamped their Superset Monitoring Framework by ingesting metadata into Snowflake, redefining usage and adoption metrics, and adding cost tracking. This allowed us to automate governance, decommission 25% of underutilised dashboards, and increase our 30-day utilization rate to 90%. Our new monitoring dashboard provides actionable insights, optimizing both performance and costs.
2.Leveraging dwell time to improve member experiences on the linkedIn feed
How Can leveraging dwell time help improve member experiences on the linkedIn feed?
The LinkedIn Feed is a key part of the member experience, offering a space to share and engage with content. While some members interact explicitly by commenting or sharing, others engage more passively by dwelling on content they find interesting. Understanding dwell time helps Linkedin better tailor the feed to enhance engagement and member satisfaction.
3.Making Uber’s experiment evaluation engine 100x faster
How Did Uber make their experiment evaluation engine 100x faster?
This article explores how Uber redesigned its experimentation platform by moving from remote to local evaluations. By eliminating the need for network calls, Uber achieved remarkable speed improvements, reducing latency and enhancing the reliability of its services across all business units.
https://www.uber.com/en-IN/blog/making-ubers-experiment-evaluation-engine-100x-faster/
4.Boosting ML pipeline efficiency: direct cassandra ingestion from spark
How Can direct Cassandra Ingestion from spark boost the efficiency of your ML pipelines?
At Yelp, the authors optimized ML pipeline by enabling direct ingestion from Spark to Cassandra, cutting out the intermediary Data Pipeline. This led to a 30% reduction in infrastructure costs and a faster, more streamlined feature publishing process, improving developer efficiency by 25% and simplifying system maintenance.
5.SQL has problems. we can fix them: pipe syntax in SQL
How can pipe syntax in SQL help solve some of Its long-standing problems?
In this article, Google engineers introduce a new pipe syntax to SQL, addressing long-standing challenges while maintaining compatibility with the existing SQL ecosystem. By incorporating a pipe-structured data flow, SQL becomes more intuitive, flexible, and easier to extend, all without requiring a migration to a new language. This enhancement simplifies query construction, boosting productivity and making SQL more accessible.
https://research.google/pubs/sql-has-problems-we-can-fix-them-pipe-syntax-in-sql/
6.Query optimization with historical-based optimization framework in presto
How does presto's historical-based optimization framework Improve query performance?
in this article Presto's Historical-Based Optimization (HBO) framework leverages past query execution data to improve query performance. By using historical statistics to inform query planning, HBO enables more efficient resource usage, reducing execution times and optimizing complex query workloads in distributed systems. This advancement offers significant performance improvements, especially for queries involving complex joins.
7.How we built Ngrok's data platform
How Christian Hollinger built Ngrok's data platform?
At Ngrok's, managing a data lake with a team of one has led to unique insights and innovations in data engineering. This article shares how Christian Hollinger built their data platform, the challenges faced, and the solutions we've implemented, including using open-source tools like Airbyte and Apache Flink.
https://ngrok.com/blog-post/how-we-built-ngroks-data-platform
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