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Data Loss

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Efficient Ops
Efficient Ops
Jun 16, 2024 · Information Security

How a Former NCS Engineer’s Revenge Hack Caused $670K Loss and Prison

A Singapore court sentenced former NCS employee Kandula Nagaraju to over two years in prison after he illegally accessed his ex‑employer’s QA system, deleted 180 virtual servers, and caused more than $670,000 in damages, highlighting serious cybersecurity and legal repercussions.

Data LossUnauthorized Accesscybercrime
0 likes · 6 min read
How a Former NCS Engineer’s Revenge Hack Caused $670K Loss and Prison
DevOps Operations Practice
DevOps Operations Practice
May 20, 2024 · Cloud Computing

Google Cloud Data Deletion Incident at UniSuper: Causes, Impact, and Lessons Learned

Google Cloud mistakenly deleted data and backups for Australian pension fund UniSuper, causing over 600,000 members to lose access for more than a week, and the incident highlights the risks of single‑provider reliance, the importance of robust backup strategies, and the growing relevance of hybrid and multi‑cloud architectures.

BackupData LossIncident Analysis
0 likes · 5 min read
Google Cloud Data Deletion Incident at UniSuper: Causes, Impact, and Lessons Learned
HelloTech
HelloTech
Oct 31, 2023 · Big Data

Investigation of Data Loss in a Flink Kafka Consumer Caused by Mixed Kafka Cluster Configuration

The data loss in a Flink‑Kafka job was caused by a mis‑configured bootstrap.servers list that mixed production and pre‑release Kafka clusters, leading different subtasks to connect to different clusters, resulting in inconsistent partition discovery and offset fetching, which omitted several partitions until the list was corrected.

Cluster ConfigurationData LossElasticsearch
0 likes · 8 min read
Investigation of Data Loss in a Flink Kafka Consumer Caused by Mixed Kafka Cluster Configuration
Wukong Talks Architecture
Wukong Talks Architecture
Sep 2, 2022 · Big Data

Preventing Data Loss in Kafka: Message Semantics, Failure Scenarios, and Reliability Solutions

This article explains Kafka's message delivery semantics, analyzes potential data‑loss scenarios across producer, broker, and consumer components, and provides concrete configuration and coding practices—such as idempotent producers, proper ACK settings, replication factors, and manual offset commits—to maximize message durability and reliability.

BrokerConsumerData Loss
0 likes · 18 min read
Preventing Data Loss in Kafka: Message Semantics, Failure Scenarios, and Reliability Solutions
Xiaolei Talks DB
Xiaolei Talks DB
Mar 16, 2022 · Operations

How to Recover a TiKV Cluster After Multiple Node Failures

This article demonstrates how to simulate and recover TiKV cluster failures by shutting down one, two, or three nodes, explains the impact on Raft groups and region availability, and provides step‑by‑step commands for disabling PD scheduling, using tikv‑ctl, and restoring data integrity.

Data LossPDRaft
0 likes · 28 min read
How to Recover a TiKV Cluster After Multiple Node Failures
Aikesheng Open Source Community
Aikesheng Open Source Community
Dec 17, 2021 · Databases

Root Cause Analysis of Missing Data Using MySQL Binlog Parsing

This article presents a step‑by‑step investigation of a data‑loss incident in a MySQL table by examining binlog files, identifying unintended TRUNCATE operations, and offering practical binlog‑parsing techniques and configuration tips for reliable troubleshooting.

Data LossDatabaseMySQL
0 likes · 8 min read
Root Cause Analysis of Missing Data Using MySQL Binlog Parsing
Selected Java Interview Questions
Selected Java Interview Questions
Apr 21, 2021 · Databases

Redis Data Loss Scenarios and Mitigation Strategies

The article explains how asynchronous replication and split‑brain situations can cause data loss in Redis clusters, and describes configuration parameters and client‑side techniques to minimize such loss while maintaining high availability.

ClusterData LossHigh Availability
0 likes · 6 min read
Redis Data Loss Scenarios and Mitigation Strategies
Full-Stack Internet Architecture
Full-Stack Internet Architecture
Dec 31, 2020 · Databases

Problems Caused by NULL Values in MySQL Columns and Their Solutions

This article explains five common issues caused by NULL values in MySQL columns—data loss with COUNT, DISTINCT, and inequality queries, NULL‑induced NULL results in SUM leading to NPE, increased query complexity, and index behavior—while providing practical SQL solutions and best‑practice recommendations.

Data LossDatabaseMySQL
0 likes · 8 min read
Problems Caused by NULL Values in MySQL Columns and Their Solutions
Aikesheng Open Source Community
Aikesheng Open Source Community
Mar 16, 2020 · Databases

Analyzing Data Loss When Restoring MySQL Master from Replica Backup Using GTID

This article explains a MySQL replication experiment where a master is restored from an outdated backup, leading to missing GTID transactions due to server_id filtering, and provides analysis and solutions such as changing server_id or enabling replicate-same-server-id to prevent data loss.

BackupData LossGTID
0 likes · 7 min read
Analyzing Data Loss When Restoring MySQL Master from Replica Backup Using GTID
Aikesheng Open Source Community
Aikesheng Open Source Community
Apr 11, 2019 · Databases

MySQL Replication server_id and server_uuid: Pitfalls, Causes of Data Loss, and Best‑Practice Recommendations

This article explains how duplicate MySQL server_id or server_uuid values in replication topologies can cause data loss during high‑availability failover, illustrates the underlying mechanisms with diagrams, and provides practical configuration recommendations to avoid these issues.

Data LossHigh AvailabilityMySQL
0 likes · 6 min read
MySQL Replication server_id and server_uuid: Pitfalls, Causes of Data Loss, and Best‑Practice Recommendations