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Lakehouse Research Base
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Lakehouse Research Base

Focused on technical sharing in the data field, covering a tech stack that includes Hadoop, Spark, Flink, Kafka, Fluss, Paimon, Iceberg, StarRocks, ClickHouse, ES, Milvus, and more. Welcome to follow.

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Recent Articles

Latest from Lakehouse Research Base

90 recent articles
Lakehouse Research Base
Lakehouse Research Base
Jan 8, 2026 · Big Data

SME Data Architecture Selection: Lightweight Lakehouse Implementation Guide

This article provides a practical framework for SMEs to select and implement data platform architectures, comparing traditional warehouses, lakehouse, and multimodal data lakes across cost, ROI, and operational efficiency, with scenario-based recommendations and a lightweight lakehouse implementation guide using open-source stack Flink, Paimon, StarRocks, and MinIO.

Data ArchitectureData LakeFlink
0 likes · 21 min read
SME Data Architecture Selection: Lightweight Lakehouse Implementation Guide
Lakehouse Research Base
Lakehouse Research Base
Jan 4, 2026 · Big Data

How We Fixed Flink CDC GC Crashes and Cut Latency from 30min to 5s

This article details a full-chain optimization of a Flink CDC real-time data warehouse pipeline syncing 20 MySQL instances to StarRocks via Kafka, resolving GC crashes, checkpoint failures, and 30-minute latency through resource scaling, parallelism redesign, memory tuning, checkpoint configuration, and startup strategy changes, achieving 5-second end-to-end latency and 100% checkpoint success.

Checkpoint TuningFlink CDCGC optimization
0 likes · 18 min read
How We Fixed Flink CDC GC Crashes and Cut Latency from 30min to 5s
Lakehouse Research Base
Lakehouse Research Base
Jan 2, 2026 · Databases

StarRocks Production Incident: Continuous Report Refresh Triggers Cluster Crash & Recovery

This article details a StarRocks cluster crash caused by users continuously refreshing reconciliation dashboards, the emergency response including query timeout reduction and compute node scaling, root cause analysis highlighting storage-compute separation benefits, and long-term preventive measures like business reporting processes, Multi-Warehouse isolation, and query governance.

Multi-WarehouseSQL optimizationStarRocks
0 likes · 17 min read
StarRocks Production Incident: Continuous Report Refresh Triggers Cluster Crash & Recovery
Lakehouse Research Base
Lakehouse Research Base
Dec 21, 2025 · Databases

StarRocks Join Performance Gap: Paimon/Iceberg vs Internal Tables - Root Causes & Fixes

This article analyzes why StarRocks multi-table joins on Paimon/Iceberg tables remain significantly slower than internal tables even after cache warming, detailing six root causes across storage format, data layout, caching, execution engine, metadata, and version management, plus four optimization strategies to narrow the gap.

Join PerformanceLakehouseOLAP
0 likes · 17 min read
StarRocks Join Performance Gap: Paimon/Iceberg vs Internal Tables - Root Causes & Fixes
Lakehouse Research Base
Lakehouse Research Base
Nov 30, 2025 · Big Data

Apache Paimon 1.3: 150x Faster Queries, Pure Python SDK, AI-Ready Lakehouse

Apache Paimon 1.3 introduces incremental clustering for 150x faster append-table queries, a pure Python SDK 95x faster than its JVM-based predecessor, data evolution cutting wide-table storage by 99.4%, a virtual file system for unified cross-engine access, and upcoming vector storage for AI workloads.

AI Data ManagementApache PaimonData Evolution
0 likes · 8 min read
Apache Paimon 1.3: 150x Faster Queries, Pure Python SDK, AI-Ready Lakehouse
Lakehouse Research Base
Lakehouse Research Base
Nov 15, 2025 · Big Data

StarRocks Lakehouse: Internal Tables vs. Paimon/Iceberg – 5-10x Performance Gains

The article benchmarks StarRocks internal tables against Paimon and Iceberg lake tables using TPCH 100G, shows internal tables excel at real-time analytics while lake tables optimize cold storage cost and multi-engine sharing, and details optimization strategies (metadata, data cache, predicate pushdown) achieving 5-10x speedup, illustrated by a retail case study cutting query latency from seconds to milliseconds and storage cost by 65%.

Data CacheHot-Cold TieringLakehouse
0 likes · 9 min read
StarRocks Lakehouse: Internal Tables vs. Paimon/Iceberg – 5-10x Performance Gains
Lakehouse Research Base
Lakehouse Research Base
Oct 3, 2025 · Operations

100 Advanced Linux Interview Questions for Big Data Operations

This comprehensive guide presents 100 advanced Linux interview questions tailored for big data operations, covering command-line mastery, system performance tuning, cluster configuration for Hadoop, Kafka, Spark, and Flink, complex troubleshooting scenarios, automation with Ansible and Prometheus, security hardening, and containerized deployments on Kubernetes.

Big DataHadoopKafka
0 likes · 45 min read
100 Advanced Linux Interview Questions for Big Data Operations
Lakehouse Research Base
Lakehouse Research Base
Oct 2, 2025 · Operations

100 Linux Interview Questions for Big Data Operations

This comprehensive guide presents 100 Linux interview questions tailored for big data operations, covering basic commands, permissions, process management, networking, log processing, shell scripting, and cluster-specific operations with detailed answers and practical explanations.

Big DataHadoopKafka
0 likes · 41 min read
100 Linux Interview Questions for Big Data Operations