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infoq.com /1 week ago

Hardwood Promises High-Speed JVM Apache Parquet Processing with Zero Mandatory Dependencies

Hardwood, the project Gunnar Morling kick-started handling of Parquet files in Java, reached version 1. Its multi-threaded approach and zero mandatory external dependencies promise...

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dataengineeringcentral.substack.com /1 month ago

Benchmarking Vortex File Format ... vs Parquet, CSV ... vs DuckDB, Polars, Datafusion.

just because we want to make em' mad

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dev.to /1 month ago

PostgreSQL change data capture governed Apache Iceberg / Parquet on AWS S3 вЂ” built for AI agents.

Core Features pg-cdc is not just replication. pg-cdc streams Postgres Write Ahead Logs(WAL) out of production Postgres into typed, immutable, time-travelable Iceberg tables on S3...

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dev.to /1 month ago

Performance and Apache Iceberg's Metadata

This is Part 3 of a 15-part Apache Iceberg Masterclass. Part 2 covered the metadata structures of all five table formats. This article focuses on exactly how query engines use Iceb...

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dataengineeringcentral.substack.com /3 weeks ago

Apache Datafusion Comet (Spark Accelerator)

any better than last time?

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dzone.com /1 week ago

Parquet vs Lance: How Storage Layout Changes the Read Path

Apache Parquet became the default format for analytical data because it matched the read path of analytical engines. Queries scanned large parts of a dataset, often across a small...

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aws.amazon.com /1 month ago

Beyond JSON blobs: Implementing the VARIANT data type in Apache Iceberg V3

This post is part 1 of a two-part series. We walk through the basics: creating an Iceberg V3 table with a VARIANT column, inserting semi-structured data, and querying it with varia...

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feeds.feedblitz.com /2 weeks ago

A Guide to Apache Paimon Java API

Explore the Java API for Apache Paimon and learn how to perform CRUD operations on a Paimon database. The post A Guide to Apache Paimon Java API first appeared on Baeldung.

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dzone.com /1 week ago

Building Production-Grade Delta Lake Pipelines With Apache Spark on Databricks

Why Delta Lake? Apache Parquet on cloud storage was a great first step for data lakes — but it left engineers dealing with a painful set of problems in production: No ACID transa...

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cloud.google.com /1 month ago

Accelerating data lakes: Optimizing Apache Iceberg and Spark with gcs-analytics-core

Many data engineers spend significant time managing compatibility and getting best performance across multiple analytics engines. To help solve this pain point, we are excited to a...

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medium.com /3 weeks ago

Apache Spark | Structured Data

The data (r)evolution started with structured data, most strategic decisions and dashboards rely on it. Long before databases and…Continue reading on Medium »

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blogs.vmware.com /4 weeks ago

Embracing Schema Drift: Seamless Parquet Querying in Tanzu Greenplum, No Matter How Your Data Changes

<div><img width="300" height="157" src="https://blogs.vmware.com/wp-content/uploads/2026/06/vmw-blogtile-tanzu-greenplum-v2_fzmxrz.png" class...

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aws.amazon.com /1 month ago

Real-time CDC from Aurora PostgreSQL to Amazon S3 Tables using Debezium and Firehose

In this post, we show you how to build a CDC pipeline that delivers query-ready Iceberg tables directly. The pipeline captures inserts, updates, and deletes from Aurora PostgreSQL...

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dzone.com /1 week ago

Apache Spark Query Optimization on Databricks: Catalyst, AQE, and Photon Engine

Why Query Optimization Matters A Spark query written by a human and a Spark query executed by the engine are often very different things. The gap between them — the optimization —...

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dzone.com /1 month ago

Rust-Native Alternatives to Spark SQL and DataFrame Workloads

Apache Spark is one of the most powerful tools in the data and AI engineering world. It helps process massive datasets and is widely used across industries, irrespective of cloud p...

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infoq.com /1 month ago

Article: The Schema Proliferation Problem in Kafka and Flink Pipelines: How to Solve It

Schema proliferation builds slowly and gets expensive fast. One schema per event type feels right until there are ten tables, union queries spanning all of them, and a single field...

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dzone.com /1 month ago

Why We Chose Iceberg Over Delta After Evaluating Both at Scale

When people compare Delta Lake and Apache Iceberg, the discussion often stays too abstract. Most articles describe features at a high level, but platform decisions are usually made...

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databricks.com /1 month ago

Advancing Apache Iceberg on Databricks: Iceberg v3 GA, Open Sharing, and Unified Governance

The next phase of the open lakehouse will be defined by the catalog. Open table formats...

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dzone.com /1 month ago

Scaling Cloud Data Automation: A Practical Guide to Open Table Formats

When we talk about data analytics the way we set up our tables is really important. This is because it can make a difference, in how well our systems work and how fast they can gro...

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dzone.com /1 month ago

Architecting Petabyte-Scale Hyperspectral Pipelines on AWS

The Data Challenge Every industry has its version of the same data engineering problem: massive, complex payloads generated at the edge — far from the cloud, often on unreliable ne...

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aws.amazon.com /1 week ago

Accelerating log analytics at scale with AWS Glue and Apache Iceberg materialized views

In this post, you learn how to build an application log pipeline for production use with Amazon CloudWatch Logs, AWS Lambda, Amazon Data Firehose, AWS Glue, and Apache Iceberg mate...

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snowflake.com /1 week ago

Apache Ossie (Incubating): The New Name for Open Semantic Interchange

Open Semantic Interchange is now Apache Ossie (Incubating) — a vendor-neutral semantic standard for AI and data tools, entering the Apache Incubator with 50+ contributors.

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snowflake.com /4 weeks ago

Real-Time Data Pipelines via Snowpipe Streaming | Snowflake

Discover how data engineers ship real-time data pipelines affordably using Snowpipe Streaming and Snowflake CoCo. Start streaming in minutes.

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dzone.com /4 weeks ago

Cutting Data Pipeline Costs and Data Freshness Issues With Netflix Maestro and Apache Iceberg: A Practical Tutorial

Analytics pipelines tend to scale in both cost and the age of their data sources: costs increase with data volume growth, while data freshness decreases due to longer batch jobs. T...

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