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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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Latest from Lakehouse Research Base

27 recent articles

Lakehouse Research Base

Jan 27, 2026 · Big Data

Big Data Expert's Failure in Traditional Enterprise: Heavy Tech Stack Wastes Resources on Small Data

A big data expert from a large tech company builds a full Hadoop/Spark/CDH stack for a traditional enterprise with only hundreds of thousands of daily records, causing high maintenance costs, half-hour query delays, and eventual departure; the case underscores the importance of matching technology to business scale, with StarRocks proposed as a lightweight alternative.

CDHHadoopSpark

0 likes · 11 min read

Big Data Expert's Failure in Traditional Enterprise: Heavy Tech Stack Wastes Resources on Small Data

Lakehouse Research Base

Jan 20, 2026 · Databases

Why Is Your Elasticsearch Cluster Yellow? 5 Causes & Fixes for Unassigned Replicas

This guide walks through a systematic troubleshooting process for Elasticsearch yellow cluster status, covering how to diagnose unassigned replica shards using cluster health, shard allocation, and allocation explain APIs, then resolves the five most common causes: insufficient nodes, disk watermarks, disabled allocation, node failures, and oversized shards, with commands for both curl and Kibana Dev Tools.

Elasticsearchcluster healthcluster operations

0 likes · 13 min read

Why Is Your Elasticsearch Cluster Yellow? 5 Causes & Fixes for Unassigned Replicas

Lakehouse Research Base

Jan 19, 2026 · Operations

Building an Automated Root Cause Analysis System for Alibaba Cloud Big Data Operations

This article details a comprehensive root cause analysis system for Alibaba Cloud big data platforms, covering a five-layer architecture, three-dimensional analysis (cluster health, task execution, data pipelines), knowledge graph-driven automation, and AI-enhanced future directions to shift from reactive firefighting to proactive defense.

AI OperationsAlibaba CloudAutomated Remediation

0 likes · 26 min read

Building an Automated Root Cause Analysis System for Alibaba Cloud Big Data Operations

Lakehouse Research Base

Jan 15, 2026 · Databases

StarRocks Temporary Partitions: Complete Guide to Viewing & Safe Deletion

This guide explains why StarRocks temporary partitions block DDL operations due to metadata consistency, locking, and atomicity constraints, and provides step-by-step commands for viewing partitions via SHOW TEMPORARY PARTITIONS and information_schema, plus safe deletion using ALTER TABLE DROP TEMPORARY PARTITION with critical precautions.

DDL operationsSQL commandsStarRocks

0 likes · 10 min read

StarRocks Temporary Partitions: Complete Guide to Viewing & Safe Deletion

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.

FlinkPaimonSME

0 likes · 21 min read

SME Data Architecture Selection: Lightweight Lakehouse Implementation Guide

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

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.

IcebergJoin PerformanceOLAP

0 likes · 17 min read

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