Query-Adaptive RAG: Routing Complex Questions to Multi-Hop Retrieval While Keeping Simple Queries Fast (8 minute read)
Query-adaptive RAG enables enterprise retrieval-augmented generation systems to dynamically classify and route queries based on complexity, shifting between fast single-hop retrieval for factual questions and multi-hop synthesis for reasoning-heavy tasks. Leading implementations report 30-40% latency reductions and 8% accuracy gains, with precision on factual queries reaching 92-96% and LLM call costs dropping by up to 50%. Success hinges on robust complexity detection, tailored routing orchestration, and continuous feedback loops. |
Your Agent's Reasoning Is Fine - Its Memory Isn't (17 minute read)
Production incident response is slowed not by lack of fixes but by scattered context and undocumented dependencies. Integrating an AI agent powered by GraphRAG enables rapid, structured retrieval of service relationships, ownership, past incidents, and documentation. This system, triggered by monitoring tools like Prometheus and interfacing via FastAPI and Slack, transforms incident alerts into actionable, context-rich reports within seconds. |
A Deep Dive Into SeaTunnel Metadata Caching (5 minute read)
SeaTunnel Zeta addresses the major data integration bottlenecks of classloader conflicts, checkpoint overhead, and metadata request surges via intelligent metadata caching, distributed Hazelcast's IMap storage, and automated lifecycle management. Its shared classloader model reduces Metaspace use, while millisecond-level state access and asynchronous persistence decouple memory and backup, ensuring reliability in large-scale jobs. |
Apache Spark Performance Tuning on Amazon EMR (21 minute read)
Optimal Spark performance on EMR demands strategic tuning, rather than additional compute, to prevent wasted costs and degradation from shuffle overhead, GC pressure, and network contention. Key optimizations include executor sizing, memory management, partition and shuffle tuning, caching, column/prune elimination, predicate pushdown, handling small files, and leveraging Z-ordering for "haystack queries". Benchmarks show a 10x difference in 1TB S3 read times based solely on data organization and configuration. |
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Building Data Pipelines Like Assembly Lines (19 minute read)
A small data engineering team stopped building one-off Airflow pipelines and instead created a declarative factory where every dataset follows the same write, test, and publish pattern. By encoding testing, documentation, dependency management, and safety into reusable components, they can ship new pipelines in hours instead of days and avoid bad data reaching production. |
The MCP Setup Every Data Engineer Needs (9 minute video)
A tutorial on how to connect an AI tool to MotherDuck via an MCP server so you can ask questions in plain English and have the AI run read-only SQL to explore data, fix query errors automatically, and return insights fast. This is demonstrated on a huge table, private S3 Parquet files (including vector similarity search), and even public APIs that can be queried and parsed like tables. |
You Probably Don't Need a Vector Database for Your RAG — Yet (6 minute read)
For small-to-medium RAG pipelines, NumPy and scikit-learn can deliver millisecond-level in-memory vector search on millions of records, eliminating the overhead and complexity of dedicated vector databases like Pinecone or Weaviate. Using pure matrix multiplication and tree-based search (KD/Ball-Tree), typical workloads up to ~1.5GB of embeddings (e.g., 1M vectors × 384-dim) perform efficiently without network serialization or CRUD demands. Transition to vector databases only when persistence, high-frequency updates, metadata filtering, or RAM limits are required. |
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mviz (GitHub Repo)
mviz is a lightweight tool that lets you turn small JSON specs into polished HTML or PDF reports through Claude, so you can explore data, iterate quickly, and share results without building dashboards or infrastructure. It focuses on fast, AI-native analysis workflows with minimal boilerplate and high-quality static outputs that are easy to export and reuse. |
Unconventional PostgreSQL Optimizations (11 minute read)
Unconventional PostgreSQL techniques can deliver big performance gains, such as skipping impossible scans using constraints, indexing only the expressions you actually query to shrink indexes, and enforcing uniqueness on large values with hash-backed constraints. Thoughtful schema and index design often beats the default habit of adding large B-tree indexes everywhere. |
Prisma 7: Rust-Free Architecture and Performance Gains (3 minute read)
Prisma, the TypeScript-first ORM, shifts away from Rust in its release 7.0, delivering a 90% reduction in bundle size and 3x faster query execution, while cutting CPU and memory loads. Generated code now resides outside node_modules, enabling faster, restart-free development workflows, and type generation is 70% quicker, evaluating 98% fewer types. |
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How ClickHouse became one of the fastest-growing databases in the world (4 minute read)
ClickHouse grew by solving a problem most databases could not: running complex analytics at massive scale with real-time, millisecond latency. Its strong fit for AI workloads, simple developer experience, and obsessive engineering focus from its creator have made it a default choice for fast-growing companies that need performance without operational complexity. |
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