Apache Programming Tutorials & Engineering Articles

To understand where agentic AI stands today, consider the last seismic shift in technology: the rise of the internet in the 90s. It was new and full of…

Using NVIDIA Cluster Readiness Engine, teams can bring reliable GPU clusters to production with workload-driven validation.

Kubernetes manages what runs on your nodes. Managing the nodes themselves is the challenge: kernel settings, system packages, storage layouts, security agents…

We’re open-sourcing Rebalancer, the assignment-problem solver that has been used to solve resource allocation problems throughout Meta for over nine years. Rebalancer separates several relate…

Notes from three days in the Netherlands, featuring a lightning talk on pg_clickhouse and pg_stat_ch at PGDay Lowlands and a session on PostgreSQL 19 monitoring at Percona Live Amsterdam.

The official ClickHouse provider for Apache Airflow simplifies data workflows with standard SQL operators, bulk inserts, and shared setup across self-managed Airflow and Astronomer.

The ClickHouse adapter for dbt v2 is in public beta, powered by dbt’s Rust engine. ClickHouse also joins the dbt platform in private beta, supporting open-source ClickHouse and ClickHouse Cloud.

Google has partnered with Speakeasy to open-source their OpenAPI code generation suite under the AGPLv3 license, a strategic move prompted by the sudden shutdown of Google's previous proprietary SDK provider. The newly open-sourced suite equips developers with deterministic, multi-language SDK generators that natively support strict typing and SSE streaming, alongside tools for compiling agent-native CLIs and documentation MCP servers. Engineering teams can now safely integrate this robust tooling directly into their CI pipelines to automatically generate reliable client libraries for their own APIs, all while retaining complete licensing control over the output code.

ClickHouse 26.8 LTS introduces background queries, pipelined SQL, new text tokenizers, expanded data lake integrations, and faster Parquet, aggregation, and join queries.

After claims that ClickHouse is “winning the observability wars” sparked debate, we reflect on why it has become a leading storage and query engine, where it still falls short, and why winning the database layer isn’t the same as winning observability.

For years, a Python developer who needed a GPU had two realistic choices: Learn NVIDIA CUDA C++ well enough to write an extension, set up a build toolchain…

ClickHouse is joining the Open Secure AI Alliance alongside NVIDIA and other industry leaders to help build open tools that keep AI agents secure.

To prevent context window bloat and reduce token consumption, Genkit Go introduces Agent Skills based on a progressive disclosure architecture. Developers can package specialized instructions, scripts, and references into modular SKILL.md bundles where only the frontmatter metadata is initially exposed to the agent's system prompt. When a task matches the skill's description, Genkit's middleware dynamically loads the full instruction body and associated assets, ensuring the model accesses precise workflows exactly when needed.

Bypassing row-by-row Snowflake result materialization cut fetch-memory growth and doubled one ML workflow’s training-data window on the same cluster size.

Retention limits, sampling, and metric roll-ups aren't observability best practices - they're workarounds for storage systems that can't handle full-fidelity data, and they're becoming a hard blocker for AI-driven workflows.

Query Insights is now in preview for ClickHouse Cloud Managed Postgres: every query pattern your database runs, ranked by impact, with the diagnostic picture of why each one is slow.

ClickHouse was released in open source on Jun 15 2016, ten years ago. Since then, it became the most popular open source analytical database with more than 2000 contributors.

On June 15, 2016, ClickHouse went open source under the Apache 2.0 license. Ten years later, it's one of the most widely deployed analytical databases in the world.

ClickHouse now has an official ADBC driver, giving Ruby, R, C, and every other ADBC-aware tool zero-conversion, Arrow-native access to ClickHouse without a dedicated client for each language.

Useful quantum computers will require fault tolerant logical operations. Researchers are actively exploring many different quantum error correction (QEC) codes…

As more teams move from humanoid robot bring-up to task-specific skill development, the need for repeatable development workflows is growing.

Industrial machinery generates more alarms than technicians can triage. For each important alarm requiring follow-up, the technician pulls historical context…

GPU-accelerated query engines are often constrained by memory and I/O bandwidth. NVIDIA hardware advances—including high bandwidth memory (HBM)…

An open specification for finding and verifying tools, skills, and agents across the web.Agents are ...

DiffusionGemma is an experimental text-generation model built on the Gemma 4 architecture that uses diffusion-based parallel generation instead of token-by-token autoregression, enabling much faster inference, bidirectional context awareness, and real-time self-correction while remaining deployable on consumer GPUs. Its architecture generates and refines 256-token blocks in parallel through iterative denoising, allowing it to handle complex constraint-based tasks such as Sudoku more effectively than traditional language models and demonstrating strong gains from fine-tuning. The model integrates with vLLM and other popular inference frameworks, giving developers access to a new non-autoregressive approach that combines high performance, efficient long-context scaling, and straightforward customization and deployment.

Some microservices are difficult to test because their behavior depends on a long tail of inputs that are hard to model by hand. This post focuses on how to build a repeatable Apache Spark replay harness for testing without creating a second implementation of the service.

Some microservices are difficult to test because their behavior depends on a long tail of inputs that are hard to model by hand. For that class of problem, Apache Spark's massive parallelism and linear scalability can be leveraged to build a regression test harness. That makes it possible to compare old and new implementations, model upcoming rule changes, and quantify impact before a change reaches production.

Maximizing the value of AI infrastructure demands deep visibility into GPU utilization. Yet many platform teams running AI workloads on Kubernetes operate with…

How Airbnb shifts from PaaS to an internal knowledge graph infrastructure at scale.

NVIDIA Ising is the world’s first family of open AI models for building quantum processors, launching with two model domains: Ising Calibration and Ising…