1. DataFrames at Scale Comparison: TPC-H
How do DataFrame tools stack up with TPC-H benchmarks at scale?
The authors aim to analyze the performance of popular DataFrame tools like Spark, Dask, DuckDB, and Polars under TPC-H benchmarks across various scales and architectures. The results reveal trade-offs in scalability, ease of use, and local vs. cloud performance. This comprehensive breakdown is a must-read for data engineers navigating the evolving landscape of high-performance analytics.
https://docs.coiled.io/blog/tpch.html
2. Apache Iceberg: The Hadoop of the Modern Data Stack?
Is Apache Iceberg the Hadoop of the Modern Data Stack?
The author says that Apache Iceberg could transform data lakes as Hadoop did for big data, but it’s not without challenges. From adoption complexities to metadata overhead and small file issues. Iceberg mirrors many lessons from the Hadoop era. This post explores whether Iceberg will thrive or falter—essential reading for anyone navigating the modern data stack.
https://blog.det.life/apache-iceberg-the-hadoop-of-the-modern-data-stack-c83f63a4ebb9
3. Evaluating Quality in Large Language Models: A Comprehensive Approach using the legal industry as a use case
How do you ensure quality and accuracy in Large Language Models for critical applications like the legal industry?
This article explores innovative methodologies, such as using LLMs as evaluators, multi-agent debate, and frameworks like Scorecard and DeepEval. It’s a must-read for understanding how to ensure accuracy, relevance, and trustworthiness in AI-driven decisions, especially in regulated industries.
4. Build Write-Audit-Publish pattern with Apache Iceberg branching and AWS Glue Data Quality
"How can Apache Iceberg branching and AWS Glue Data Quality enhance data pipeline reliability?”
This article explores the Write-Audit-Publish (WAP) pattern using Apache Iceberg branches and AWS Glue Data Quality. The authors showcase how to validate data efficiently while maintaining flexibility and accuracy, crucial for analytics and decision-making. Read on to see how modern data lakes can uphold quality and reliability.
5. A First Look at S3 (Iceberg) Tables
What’s New with S3 (Iceberg) Tables?
The author explores AWS's new S3 Tables, highlighting how this feature integrates Iceberg natively into S3, automates compaction, and simplifies catalog management. This post dives into the cost, ease of use, and transformative potential of S3 Tables for OLAP tools and data analytics pipelines.
https://meltware.com/2024/12/04/s3-tables
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