# parquet Published articles for parquet. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Persistent Databases in the Browser with DuckDB-Wasm and OPFS DevFeed: [Persistent Databases in the Browser with DuckDB-Wasm and OPFS]() Original publisher: [Read original article]() Author: Carlo Piovesan, Geertjan Wielenga Published: 2026-09-18T00:00:00Z Content type: tutorial Language: en Sources: [DuckDB]() Topics: [DuckDB](), [wasm](), [Filesystems](), [browser](), [Databases]() Tags: [browser](), [cli](), [databases](), [duckdb](), [file-system](), [npm](), [parquet](), [python](), [using-duckdb](), [wasm]() ### AI overview This tutorial explains how DuckDB-Wasm can persist browser databases in the Origin Private File System (OPFS), including version requirements, reload behavior, database files, and checkpointing. ### Source excerpt DuckDB-Wasm can open a persistent database file in the browser's Origin Private File System (OPFS). This post shows how, and when data reaches disk. ## DuckDB Skills for Claude Code DevFeed: [DuckDB Skills for Claude Code]() Original publisher: [Read original article]() Author: The DuckDB team Published: 2026-09-16T00:00:00Z Content type: tutorial Language: en Sources: [DuckDB]() Topics: [DuckDB](), [Claude Code](), [Command-line interface](), [data](), [CSV](), [parquet]() Tags: [claude-code](), [cli](), [csv](), [data](), [duckdb](), [parquet](), [using-duckdb]() ### AI overview This post introduces the duckdb-skills plugin for Claude Code. The plugin uses the DuckDB CLI to inspect data files, run SQL queries, convert formats, explore object storage and spatial data, search documentation, and recall earlier sessions. It explains installation, how the skills select appropriate commands, and how Claude uses DuckDB for exact query results. ### Source excerpt The duckdb-skills plugin gives Claude Code a growing number of skills that use the DuckDB CLI to read data files, run queries, convert formats, explore object storage, work with spatial data, search the documentation and recall earlier sessions. ## ClickHouse release 26.8 DevFeed: [ClickHouse release 26.8]() Original publisher: [Read original article]() Author: ClickHouse Published: 2026-09-10T00:00:00Z Content type: release Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [parquet](), [data]() Tags: [bug](), [clickhouse](), [data](), [feature](), [parquet](), [performance](), [release](), [sql](), [tokenizers]() ### AI overview ClickHouse 26.8 is an LTS release that adds background queries, pipelined SQL, text tokenizers, and expanded data lake integrations, alongside performance improvements for Parquet, aggregations, and joins. ### Source excerpt ClickHouse 26.8 LTS introduces background queries, pipelined SQL, new text tokenizers, expanded data lake integrations, and faster Parquet, aggregation, and join queries. ## Loading Parquet data into MySQL with ClickHouse DevFeed: [Loading Parquet data into MySQL with ClickHouse]() Original publisher: [Read original article]() Author: Mark Needham Published: 2026-09-10T00:00:00Z Content type: tutorial Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [MySQL](), [parquet](), [Databases](), [Amazon S3]() Tags: [clickhouse](), [mysql](), [parquet](), [s3](), [sql]() ### AI overview A tutorial on using ClickHouse to load Parquet data into MySQL and query MySQL through ClickHouse table functions. ### Source excerpt Use ClickHouse to load Parquet files into MySQL, explore remote data, and run MySQL queries with table functions and named collections. ## Introducing chdb Postgres extension: High-performance imports from cloud storage DevFeed: [Introducing chdb Postgres extension: High-performance imports from cloud storage]() Original publisher: [Read original article]() Author: David Wheeler Published: 2026-09-08T15:42:52Z Content type: release Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [cloud-infrastructure](), [Amazon S3](), [parquet](), [CSV]() Tags: [benchmark](), [clickhouse](), [cloud](), [duckdb](), [extension](), [json](), [parquet](), [performance](), [postgres](), [s3](), [storage]() ### AI overview chdb is a new Postgres extension that uses the in-process ClickHouse engine to import and export data across cloud storage systems and formats. The article presents import benchmarks, format support, and usage through a query function and a COPY hook module. ### Source excerpt The chdb Postgres extension brings fast imports and exports across cloud storage platforms and data formats, powered by the embedded ClickHouse engine. ## Data Engineering Weekly #286 DevFeed: [Data Engineering Weekly #286]() Original publisher: [Read original article]() Author: Ananth Packkildurai Published: 2026-09-07T00:18:06Z Content type: article Language: en Sources: [Data Engineering Weekly]() Topics: [data-engineering](), [AI, ML & Data Engineering](), [agent observability](), [Large Language Model](), [data-modeling](), [Monitoring](), [Scalability](), [semantic-layer](), [parquet]() Tags: [ai](), [ai-agents](), [data-engineering](), [data-modeling](), [llm](), [monitoring](), [newsletter](), [observability](), [parquet](), [scalability](), [semantic-layer](), [weekly]() ### AI overview Data Engineering Weekly #286 is a curated newsletter covering data platform fundamentals, mathematics for machine learning, agentic machine learning at Instacart, Netflix's lifecycle for LLM-as-a-Judge systems, semantic layers and data modeling for AI analytics, and Apache Pinot scalability. ### Source excerpt The Weekly Data Engineering Newsletter ## Parquet File Write Support, Bloom Filters, Improved Performance: Hardwood 1.1.0.Beta1 Is Out DevFeed: [Parquet File Write Support, Bloom Filters, Improved Performance: Hardwood 1.1.0.Beta1 Is Out]() Original publisher: [Read original article]() Published: 2026-08-31T19:36:00Z Content type: release Language: en Sources: [Gunnar Morling]() Topics: [parquet](), [Library](), [Parser](), [Command-line interface](), [Compression]() Tags: [apache-parquet](), [api](), [cli](), [compression](), [library](), [parquet](), [performance](), [release]() ### AI overview The first beta of Hardwood 1.1 introduces initial Parquet file-writing support through record-based and batch-oriented APIs. The release also adds Bloom filters, dictionary-based row-group pruning, performance improvements, and CLI enhancements. ### Source excerpt Table of Contents Write Support Query Evaluation: Bloom Filters and Dictionary-Based Row-Group Pruning Performance Improvements Hardwood CLI Closing Thoughts "When is write support gonna land in Hardwood?" That's probably the most common question I got over the last few months. As of today, I am very happy to share that the answer has changed from "It's coming soon" to "A first cut is there, give it a try" -- the first Beta of Hardwood 1.1 is out! This is a major milestone for the project, marking the first step in evolving Hardwood from being solely a Parquet parser to a complete library for this widely used columnar file format. But there's more. This release also comes with significant enhancements to the query layer (Bloom filters, dictionary-based row-group pruning), many performance improvements such as a fast path for effectively fixed-length list columns, an even snappier CLI, and much more. Let's dig into some of the new features and changes! ## AWS Weekly Roundup: Welcome DuckLabs to the team, Agentic Resource Discovery (ARD), and more (August 31, 2026) DevFeed: [AWS Weekly Roundup: Welcome DuckLabs to the team, Agentic Resource Discovery (ARD), and more (August 31, 2026)]() Original publisher: [Read original article]() Author: Daniel Abib Published: 2026-08-31T14:45:25Z Content type: news Language: en Sources: [AWS News Blog]() Topics: [data](), [Amazon S3](), [Amazon Redshift](), [Node.js]() Tags: [acquisition](), [amazon-ec2](), [amazon-elastic-container-service](), [amazon-gamelift](), [amazon-redshift](), [amazon-s3](), [amazon-sagemaker](), [amazon-sagemaker-ai](), [analytics](), [announcements](), [aws](), [aws-fargate](), [aws-glue](), [aws-iot-core](), [aws-lambda](), [database](), [duckdb](), [json](), [news](), [node-js](), [open-source](), [parquet](), [python](), [sql](), [week-in-review]() ### AI overview AWS weekly roundup covering the planned acquisition of DuckLabs, the company behind DuckDB, alongside Amazon ECS recovery updates and AWS Lambda preview runtimes for Node.js 26 and Python 3.15. ### Source excerpt The news that interested me the most last week was the DuckLabs acquisition. AWS has signed a definitive agreement to acquire DuckLabs, the Amsterdam-based company behind DuckDB, the popular open source analytical database that runs in-process and executes SQL directly against files like Parquet, CSV, and JSON. DuckDB stays open source under its independent foundation [...] ## Asynchronous I/O in DuckDB: Work, Thread, Work DevFeed: [Asynchronous I/O in DuckDB: Work, Thread, Work]() Original publisher: [Read original article]() Author: {"picture" =\> "/images/blog/authors/pedro\_holanda.jpg"} Published: 2026-07-31T00:00:00Z Content type: article Language: en Sources: [DuckDB]() Topics: [aio](), [CSV](), [Amazon S3](), [Amazon EC2]() Tags: [deep-dive](), [duckdb](), [latency](), [parquet](), [performance](), [s3]() ### AI overview DuckDB v2.0 will add asynchronous reads for Parquet and CSV files to improve query performance when remote I/O cannot saturate available bandwidth. The article contrasts local SSD workloads with remote data-lake setups using S3 and EC2, where concurrent reads can keep worker threads from waiting on network requests. ### Source excerpt Starting with v2.0, scheduled for fall 2026, DuckDB will support asynchronous reads of Parquet and CSV files. This can significantly speed up queries when synchronous I/O does not saturate the available bandwidth, as is typical in EC2/S3 compute-storage setups. ## Indexing the Data Lake for Online Point Queries DevFeed: [Indexing the Data Lake for Online Point Queries]() Original publisher: [Read original article]() Author: Spotify Engineering Published: 2026-07-27T20:34:28Z Content type: article Language: en Sources: [Spotify Engineering](), [Spotify Engineering Blog]() Topics: [AI, ML & Data Engineering](), [Amazon S3]() Tags: [ai-agent](), [analytics](), [bigquery](), [data](), [infrastructure](), [low-latency](), [parquet](), [sql](), [storage]() ### AI overview Spotify describes Random Access Parquet (RAP), which uses an external index and ranged reads to support low-latency key-based lookups in data-lake Parquet files without maintaining separate serving copies. ### Source excerpt Companies like Spotify need vast quantities of data accessible at low latency for online services and,... The post Indexing the Data Lake for Online Point Queries appeared first on Spotify Engineering. ## A Fast Path for Fixed-Length Lists in Parquet DevFeed: [A Fast Path for Fixed-Length Lists in Parquet]() Original publisher: [Read original article]() Published: 2026-07-22T05:30:00Z Content type: article Language: en Sources: [Gunnar Morling]() Topics: [parquet](), [Embeddings](), [Optimization](), [data]() Tags: [embedding](), [embeddings](), [encoding](), [optimization](), [parquet](), [performance](), [retrieval](), [vector]() ### AI overview The article explains why Apache Parquet handles fixed-length lists inefficiently through Dremel encoding and describes a Hardwood optimization that detects effectively fixed-length data pages and bypasses regular record reconstruction. Benchmarks show speed-ups of up to 3.7x for 768-element lists, reaching performance comparable to a flat column. ### Source excerpt Table of Contents Parquet's Dremel Encoding Reading Effectively-Fixed-Length Lists Faster Performance Gains Summary In its current form Apache Parquet isn't a great fit for storing fixed-length lists, such as coordinates, RGB(A) colors, or--an increasingly common case--vector embeddings driving search and retrieval workloads. A 768-dimensional embedding is just a list of floats that always has the same length, yet Parquet's Dremel machinery encodes it as if that length could vary from row to row, spelling out and reconstructing each vector's structure on read. That costs roughly 3x more than a purely flat columnar representation of the same data (see apache/arrow#34510). ## How We Refresh Razorpay's Data Warehouse 10x Faster with Graphs and Indexes DevFeed: [How We Refresh Razorpay's Data Warehouse 10x Faster with Graphs and Indexes]() Original publisher: [Read original article]() Author: Amit Prabhu Published: 2026-07-14T14:06:16Z Content type: article Language: en Sources: [Razorpay Engineering - Medium]() Topics: [data](), [airflow](), [Apache Spark](), [Amazon S3](), [Apache Iceberg](), [microservices architecture](), [parquet](), [Architecture & Design]() Tags: [airflow](), [analytics](), [apache-iceberg](), [architecture](), [batch](), [data](), [graphs](), [infrastructure](), [parquet](), [pipelines](), [razorpay](), [spark](), [trino](), [warehouse]() ### AI overview Razorpay describes its data warehouse refresh pipeline, which builds wide denormalized Facts by joining data from multiple microservices. The article covers the original Airflow- and Spark-based full-refresh process, the underlying lake formats and query layer, and the scaling challenges that led the team to reconsider refresh strategy, data layout, and high-cardinality dimensions. ### Source excerpt Contributors: Utkarsh Koppikar Rohan Background Razorpay provides the payment infrastructure for millions of merchants globally. Behind every payment, settlement, and refund is a microservices architecture where each service owns its own database. While this keeps services independent and scalable, it creates a challenge for stakeholders who need to see across those boundaries. The Data Platform team manages the infrastructure that bridges this gap. Transactional data flows into the lake via CDC pipelines, ingested onto S3 in Delta Lake, Apache Iceberg, or plain Parquet formats. On top of the lake, we build domain-specific warehouse tables -- wide, pre-joined tables that co-locate all the data a consumer needs, queryable via Trino. These power two use cases: Analytics (internal dashboards on Tableau and Superset) and Reporting (merchants and regulated entities who download structured data exports; Razorpay generates nearly a million such reports per month). The warehouse tables that power both use cases are called Facts. A Fact is a flat denormalised table on S3, produced by joining 10 to 30 microservice tables and materialising the result once. A settlement Fact, for example, merges payments, refunds, adjustments, and card details into a single wide row so that a dashboard or report reads from a single table instead of joining across services in real time. It is closer to a domain-specific materialised view than a classical data warehouse fact table. We maintain over 50 such Facts, and approximately 40% of all merchant reports are served directly from them. As data volumes and the number of entities per fact grew, the batch generation pipeline began to show its limits, prompting us to rethink the refresh strategy, the data layout, and how to handle high-cardinality dimensions. The rest of this post covers that journey. The Full Refresh Pipeline: Our Baseline and the Pain The original full-refresh pipeline was straightforward. Schedule: Airflow schedules Spark jobs o ## Tableflow: Turn Kafka Topics into Iceberg Tables DevFeed: [Tableflow: Turn Kafka Topics into Iceberg Tables]() Original publisher: [Read original article]() Author: Mohtasham Sayeed Mohiuddin Published: 2026-07-10T15:36:14Z Content type: tutorial Language: en Sources: [Confluent: Data in motion]() Topics: [Apache Iceberg](), [Kafka](), [schema-evolution](), [Confluent Cloud](), [parquet](), [Amazon S3](), [Streaming](), [AWS Glue](), [Amazon Redshift](), [BigQuery]() Tags: [amazon-redshift](), [amazon-s3](), [apache-iceberg](), [aws-glue](), [bigquery](), [confluent-cloud](), [kafka](), [parquet](), [schema-evolution](), [streaming](), [technologies](), [tutorial]() ### AI overview This tutorial explains how Confluent Cloud Tableflow continuously materializes Apache Kafka topics as Apache Iceberg or Delta Lake tables. It covers automatic schema handling, type conversion, schema evolution, Parquet conversion, catalog publishing, and table maintenance for querying streaming data with analytics engines and warehouses. ### Source excerpt Learn how Confluent Tableflow turns Kafka topics into Iceberg tables for zero-ETL analytics with automatic schema evolution and open catalog access. ## Scaling Grab's Data Lake: Our journey to Apache Iceberg adoption DevFeed: [Scaling Grab's Data Lake: Our journey to Apache Iceberg adoption]() Original publisher: [Read original article]() Author: Rahul Penti Published: 2026-07-10T00:00:00Z Content type: article Language: en Sources: [Grab Tech]() Topics: [Apache Iceberg](), [data](), [Amazon S3](), [parquet](), [Apache Spark](), [Latency](), [Concurrency]() Tags: [apache-iceberg](), [compute](), [concurrency](), [data](), [database](), [engineering](), [latency](), [parquet](), [performance](), [s3](), [scale](), [spark]() ### AI overview Grab describes its transition from Hive Parquet and a directory-based Hive Metastore architecture toward Apache Iceberg and a table-centric lakehouse. The article outlines scalability problems involving metadata latency, small files, S3 request overhead, and operational management, and mentions a unified Spark catalog built to hide table-format differences. ### Source excerpt Introduction: The evolution of Grab's Data Lake At Grab's scale, managing petabytes of data across billions of S3 objects demands more than a storage layer. It demands a robust architectural primitive that supports the high-concurrency needs of a modern "Lakehouse." Our goal is full storage-compute separation, leveraging S3 as an elastic foundation for both near-real-time metrics and large-scale batch transformations. For years, the vast majority of our tables were Hive Parquet, managed through the Hive Metastore with a directory-based layout. This model served us well, but as data volume grew, the directory-and-metastore approach became the limiting factor. We are now transitioning to a table-centric architecture built on modern table formats, treating data as a first-class primitive to ensure consistency and performance across our internal data transformation platforms: Slide, which powers batch transformations, and Hugo, which handles online-to-data-lake ingestion. Along the way, we also built the UnifiedSparkCatalog, a unified Spark catalog that hides table-format differences from users entirely, which we are open-sourcing alongside this post. The catalyst for change: Challenges with Hive Parquet For years, Hive Parquet was the backbone of our Data Lake, representing the vast majority of our tables. However, as data volume scaled, the architectural limitations of directory-based storage became apparent. We identified four primary bottlenecks: Catalog latency: The Hive Metastore (HMS) became a centralized failure point. High concurrency during metadata access led to O(n) listing overhead, where query planning time scaled linearly with partition count, crippling throughput. The small file problem: The directory layout left us with severe file fragmentation. Certain Machine Learning (ML) datasets had an average file size under 1 MB, with thousands of files in each partition. At this scale, the overhead of S3 object listing and metadata request latency drove up Appl ## The Creator of Pandas on AI, Apache Arrow, and the Future of Software Engineering DevFeed: [The Creator of Pandas on AI, Apache Arrow, and the Future of Software Engineering]() Original publisher: [Read original article]() Author: Daniel Beach Published: 2026-07-08T12:16:09Z Content type: article Language: en Sources: [Data Engineering Central]() Topics: [pandas](), [data-engineering](), [Artificial Intelligence](), [DuckDB](), [data-platforms](), [software-development](), [future of software](), [Apache Spark](), [Hadoop](), [Open Source](), [parquet]() Tags: [ai](), [apache-arrow](), [arrow](), [data](), [data-engineering](), [data-platforms](), [duckdb](), [hadoop](), [large-language-models](), [open-source](), [pandas](), [parquet](), [software-development]() ### AI overview An interview with Wes McKinney covers the origins of pandas and Apache Arrow, the evolution of modern data engineering from Hadoop to lakehouse architectures, and the roles of tools such as Parquet, DuckDB, DataFusion, and Spark. McKinney also discusses how AI affects software development, arguing that it can improve experienced engineers' productivity but does not replace software engineering, architecture, or judgment. ### Source excerpt interview with Wes McKinney ## Hardwood 1.0: A Fast, Lightweight Apache Parquet Reader for the JVM DevFeed: [Hardwood 1.0: A Fast, Lightweight Apache Parquet Reader for the JVM]() Original publisher: [Read original article]() Published: 2026-06-25T05:30:00Z Content type: release Language: en Sources: [Gunnar Morling]() Topics: [parquet](), [releases](), [Java](), [Library](), [Open Source](), [Maven Central]() Tags: [apache-parquet](), [compatibility](), [compression](), [cpu](), [java](), [jvm](), [library](), [maven-central](), [open-source](), [parquet](), [performance](), [releases]() ### AI overview The article announces Hardwood 1.0, an open-source Apache Parquet library for Java 21 or newer and the JVM. It focuses on fast, multi-threaded reading with no mandatory dependencies, supports local and remote files, and is available from Maven Central. ### Source excerpt Table of Contents Why Hardwood What's in Hardwood 1.0 Performance The Hardwood CLI Building Open-Source With AI A Big Thank You What's Ahead Hardwood is a new Parquet library for the JVM, written from scratch to do one thing well: read (and soon, write) Apache Parquet files fast, with no mandatory dependencies. It is performance-focused and multi-threaded at its core, fanning page decoding out across all your CPU cores by default. Today, Hardwood reaches 1.0. After five preview releases since the start of the year (Alpha1, Beta1, Beta2, CR1, CR2), we now consider Hardwood ready for production, and its public API will evolve with a strong focus on backwards compatibility going forward. Hardwood targets Java 21 or newer, is open-source (Apache License 2.0), and is available from Maven Central. ## Bridge Queries in Redpanda SQL DevFeed: [Bridge Queries in Redpanda SQL]() Original publisher: [Read original article]() Author: Paul Wilkinson Published: 2026-06-23T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [Apache Iceberg](), [SQL](), [Streaming](), [parquet](), [Compression](), [real-time](), [data](), [dashboards]() Tags: [analytics](), [compression](), [data](), [engineering](), [parquet](), [real-time](), [sql](), [storage](), [streaming]() ### AI overview This article introduces bridge queries in Redpanda SQL, which combine historical data from Iceberg tables with recent messages from Redpanda topics through a single virtual SQL table. By reading the freshness gap directly from the topic, teams can flush data to Iceberg less frequently, producing better-sized Parquet files while retaining near-real-time query results and reducing compaction overhead. ### Source excerpt Stop choosing between fresh data and robust Parquet files. Redpanda SQL bridge queries let you query live streaming topics and historical Iceberg tables together, without the compaction overhead. ## Cisco Talos empowers threat researchers while reducing TCO by 75% with ClickHouse Cloud DevFeed: [Cisco Talos empowers threat researchers while reducing TCO by 75% with ClickHouse Cloud]() Original publisher: [Read original article]() Author: ClickHouse Published: 2026-06-15T00:00:00Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Cisco Talos](), [Threat Research](), [Cybersecurity](), [data](), [Cloud](), [databricks](), [Amazon S3](), [parquet](), [Amazon Web Services](), [Kubernetes](), [Serverless]() Tags: [api](), [aws](), [cisco-talos](), [clickhouse](), [cybersecurity](), [data](), [databricks](), [event-driven](), [kubernetes](), [parquet](), [s3](), [serverless]() ### AI overview Cisco Talos migrated a nearly 2-trillion-row, 2 PB threat-intelligence dataset from self-managed ClickHouse to ClickHouse Cloud on AWS with zero downtime and a complete data match. The move reduced storage costs by about 90% and total cost of ownership by about 75%. ### Source excerpt Cisco Talos runs a threat intelligence reputation service on ClickHouse Cloud, classifying file hashes so researchers can make fast, accurate security decisions. ## Improved Column Reader API, First Cut of Geospatial Support: Hardwood 1.0.0.CR1 Is Available DevFeed: [Improved Column Reader API, First Cut of Geospatial Support: Hardwood 1.0.0.CR1 Is Available]() Original publisher: [Read original article]() Published: 2026-05-31T20:36:00Z Content type: release Language: en Sources: [Gunnar Morling]() Topics: [parquet](), [Command-line interface](), [API]() Tags: [apache-parquet](), [cli](), [documentation](), [geospatial](), [improvements](), [parquet](), [release]() ### AI overview Hardwood 1.0.0.CR1 introduces a substantially improved column reader API for Apache Parquet files, initial support for GEOMETRY and GEOGRAPHY column types, and other improvements to the core library and CLI. The columnar API remains experimental. ### Source excerpt Table of Contents Reworked ColumnReader API Geospatial Support Documentation Overhaul Further Fixes and Improvements I am happy to announce the release of Hardwood 1.0.0.CR1! This first candidate release of Hardwood 1.0 brings a substantially improved API for columnar access to Apache Parquet files, initial support for Parquet's GEOMETRY/GEOGRAPHY column types, and many other improvements to the core library as well as the Hardwood CLI. ## VARIANT Support, Interactive Parquet File TUI: Hardwood 1.0.0.Beta2 Is Out DevFeed: [VARIANT Support, Interactive Parquet File TUI: Hardwood 1.0.0.Beta2 Is Out]() Original publisher: [Read original article]() Published: 2026-04-29T17:45:00Z Content type: release Language: en Sources: [Gunnar Morling]() Topics: [parquet](), [Text-based user interface](), [Command-line interface](), [Parser](), [API](), [Amazon S3]() Tags: [apache-parquet](), [cli](), [compression](), [parquet](), [performance](), [release](), [s3](), [screen](), [storage]() ### AI overview Hardwood 1.0.0.Beta2 adds support for VARIANT columns, an interactive text-based UI for examining Parquet files, improved performance, and more efficient object-storage reads. ### Source excerpt Table of Contents VARIANT Support Hardwood CLI TUI Unified Reader API Performance Improvements Wrapping Up I am happy to announce the release of Hardwood 1.0.0.Beta2! The latest version of this new parser for Apache Parquet comes with support for VARIANT columns, an interactive text-based UI (TUI) for examining and analysing the structure of Parquet files, significantly improved performance, more efficient reading of files from object storage, and much more. ## Comparing ClickHouse versions with clickhousectl DevFeed: [Comparing ClickHouse versions with clickhousectl]() Original publisher: [Read original article]() Author: Mark Needham Published: 2026-04-29T00:00:00Z Content type: tutorial Language: en Sources: [ClickHouse Blog]() Topics: [releases]() Tags: [benchmark](), [cli](), [clickhouse](), [http](), [local](), [parquet](), [performance](), [server]() ### AI overview A tutorial on using clickhousectl to run ClickHouse 25.12 and 26.3 locally, load data, and compare query performance across versions. ### Source excerpt We use clickhousectl to spin up multiple ClickHouse versions side by side and benchmark two recent performance improvements. ## Do you still need Elasticsearch for log analytics? ClickHouse says no. DevFeed: [Do you still need Elasticsearch for log analytics? ClickHouse says no.]() Original publisher: [Read original article]() Author: Tom Schreiber; Lionel Palacin Published: 2026-04-23T00:00:00Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [benchmarking](), [elasticsearch](), [log management](), [observability](), [OpenTelemetry](), [telemetry](), [parquet](), [GitHub]() Tags: [analytics](), [benchmark](), [clickhouse](), [data](), [elasticsearch](), [github](), [logs](), [observability](), [opentelemetry](), [parquet]() ### AI overview ClickHouse presents redesigned full-text search integrated with its analytical engine and benchmarks it against Elasticsearch for log analytics. Across OpenTelemetry workloads of up to 50 billion rows, the article reports 2-6x faster full-text analytical workloads and more compact storage for ClickHouse. ### Source excerpt ClickHouse now brings full-text search and large-scale analytics together in one engine, making it a powerful alternative to Elasticsearch for log analytics. This benchmark shows why. ## Hardwood Reaches Beta: S3, Predicate Push-Down, CLI, and More DevFeed: [Hardwood Reaches Beta: S3, Predicate Push-Down, CLI, and More]() Original publisher: [Read original article]() Published: 2026-04-02T18:20:00Z Content type: release Language: en Sources: [Gunnar Morling]() Topics: [parquet](), [Amazon S3](), [Command-line interface]() Tags: [apache-parquet](), [cli](), [parquet](), [rest-api](), [s3]() ### AI overview Hardwood 1.0.0.Beta1 is a new Apache Parquet parser with an S3 backend, predicate push-down for local and remote files, Avro bindings, and a CLI for inspecting Parquet files. Its S3 support uses Java's built-in HTTP client and adds no mandatory dependencies. ### Source excerpt Table of Contents S3 Backend Predicate Push-Down Avro Bindings hardwood-cli Wrapping Up I am pleased to announce the release of Hardwood 1.0.0.Beta1! Hardwood is a new parser for Apache Parquet, optimized for minimal dependencies and great performance. Since the project's initial release just a few weeks back, a small yet very active community has come together and evolved Hardwood significantly. Today, we are shipping an S3 backend, allowing to parse files directly from object storage, predicate pushdown for both local and remote files, Avro bindings, a CLI for inspecting Parquet files, and much more. We're also excited to launch a website for the project, hardwood.dev, which contains the documentation and API reference. Let's dig in. ## ClickHouse is data lake ready DevFeed: [ClickHouse is data lake ready]() Original publisher: [Read original article]() Author: Karolina Ruiz Rogelj; Melvyn Peignon Published: 2026-03-25T00:00:00Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Apache Iceberg](), [parquet](), [Data Space](), [real-time](), [Cloud](), [Amazon S3](), [Azure]() Tags: [analytics](), [apache-iceberg](), [azure](), [caching](), [clickhouse](), [cloud](), [concurrency](), [data](), [interoperability](), [open-source](), [parquet](), [performance](), [real-time](), [s3](), [scale](), [storage]() ### AI overview ClickHouse is presented as data lake ready, with direct querying of Iceberg and Delta Lake data, support for cloud catalogs and Parquet optimizations, and the option to load data into native storage for faster high-concurrency analytics while writing results back to open formats. ### Source excerpt ClickHouse now supports direct querying of Iceberg and Delta Lake formats across major cloud catalogs without requiring data migration. 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