Tags¶
Following is a list of relevant tags:
Apache Airflow¶
Apache Flink¶
Apache Hive¶
Apache Iceberg¶
- Apache Iceberg in Production: Insights from Netflix in 2023
- Best Practices for Optimizing Apache Iceberg Workloads in AWS
- Ch4 Optimizing the Performance of Apache Iceberg
- Compaction - AWS EMR vs. AWS S3 Tables
- Deep Dive into Kafka Connect Icerberg Sink Connector
- Efficient Column Update 與 Column Families：Iceberg 對 AI/ML Wide Table 的回應
- Iceberg Table Maintenance at Scale: Lessons from 6 Big Companies
- Lessons from Slack：在 180PB 規模上維運 Iceberg
- Migrate from Hive to Iceberg
- Re-thinking Iceberg Metadata Structure in v4
- Retail Lakehouse Platform
- The Lakehouse Series: Apache Iceberg Overview
- 從 Netflix 看 Iceberg 在 Exabyte 規模下還沒解決的問題
- 從三家 OLAP 產品反推 Iceberg 的設計挑戰
Apache Kafka¶
- Deep Dive into Kafka Connect Icerberg Sink Connector
- Exactly Once Semantics in Kafka
- Retail Lakehouse Platform
Apache Polaris¶
Apache Spark¶
- Apache Spark
- Ch1 & 2 Introduction to Spark
- Ch3 Structured APIs
- Ch7 Optimizing and Tuning Spark Applications
- How Spark Works
- Spark Performance Tuning
- Streaming Processing Window Types
Apple¶
Argo CD¶
Claude Code¶
DDIA¶
DynamoDB¶
Elasticsearch¶
Feast¶
Flagger¶
Go¶
- Collections
- Concurrency
- Conditionals
- Error Handling & Defer
- Functions
- Language Overview
- Loops & Iteration
- Primitive Types & Variables
- Rosetta
- Structs, Methods & Interfaces
How It Works¶
- How Airflow Works?
- How Flagger Works? - Istio Integration
- How Flink Works
- How Istio Works?
- How KEDA Works
- How KServe Works?
- How Karpenter Works
- How Ray Works?
- How Spark Works
- What Is Knative?
Istio¶
JWT¶
KServe¶
Karpenter¶
Keda¶
Knative¶
Kubernetes¶
LinkedIn¶
MLOps¶
MLflow¶
Netflix¶
- Apache Iceberg in Production: Insights from Netflix in 2023
- Iceberg Table Maintenance at Scale: Lessons from 6 Big Companies
- 從 Netflix 看 Iceberg 在 Exabyte 規模下還沒解決的問題
OpenTelemetry¶
- Ch2 Why Use OpenTelemetry?
- Ch3 Overview
- Ch4 Architecture
- Ch7 Observing Infrastructure
- Ch8 Designing Telemetry Pipelines
- Collector
- How OpenTelemetry Works?
- Retail Lakehouse Platform
- What is OpenTelemetry?
Progressive Delivery¶
Prometheus¶
Python¶
- Collections
- Concurrency
- Conditionals
- Error Handling & Defer
- Functions
- Language Overview
- Loops & Iteration
- Primitive Types & Variables
- Rosetta
- Structs, Methods & Interfaces
Ray¶
Redis¶
RisingWave¶
SQLMesh¶
SRE¶
- Ch2 Why Use OpenTelemetry?
- Ch3 Instrumentation
- Ch3 Overview
- Ch4 Architecture
- Ch5 Labels
- Ch7 Observing Infrastructure
- Ch8 Designing Telemetry Pipelines
- Collector
- How OpenTelemetry Works?
- How Prometheus Works?
- How Thanos Works?
- What is OpenTelemetry?
Security¶
Service Mesh¶
Slack¶
- Iceberg Table Maintenance at Scale: Lessons from 6 Big Companies
- Lessons from Slack：在 180PB 規模上維運 Iceberg
Streaming Processing¶
System Design¶
- API Performance Optimization
- CAP
- Consensus Algorithms
- Consistent Hashing
- Database Performance Optimization
- Design Dropbox
- Design FB Post Search
- Design a Distributed Message Queue
- Design a Key-Value Store
- Design a Messaging Service Like WhatsApp
- Design a Notification Service
- Design a Rate Limiter
- Design a Ride-Sharing Service Like Uber
- Design a Ticket Booking Site Like Ticketmaster
- Design a Top K Heavy Hitters Service
- Indexing
- Locking
Thanos¶
The Lakehouse Series¶
- Apache Iceberg in Production: Insights from Netflix in 2023
- Best Practices for Optimizing Apache Iceberg Workloads in AWS
- Efficient Column Update 與 Column Families：Iceberg 對 AI/ML Wide Table 的回應
- Iceberg Table Maintenance at Scale: Lessons from 6 Big Companies
- Lessons from Slack：在 180PB 規模上維運 Iceberg
- Migrate from Hive to Iceberg
- Re-thinking Iceberg Metadata Structure in v4
- The Lakehouse Series: Apache Hudi Overview
- The Lakehouse Series: Apache Iceberg Overview
- The Lakehouse Series: From Data Lakes to Data Lakehouses
- The Lakehouse Series: OLTP vs. OLAP (A Parquet Primer)
- 從 Netflix 看 Iceberg 在 Exabyte 規模下還沒解決的問題
- 從三家 OLAP 產品反推 Iceberg 的設計挑戰
Trino¶
- Configure OAuth 2.0 Authentication
- Event Listeners
- Fault-tolerant Execution in Trino
- How It Works?
- Join Optimization
- Monitoring with Prometheus and Grafana
- Retail Lakehouse Platform