https://wkodate.github.io/til/
- Airflow
- Ansible
- AWS
- C++
- Computer Architecture
- Data Engineering
- DataEngWeekly
- Elasticsearch
- GCP
- Git
- Go
- Java
- Kafka
- Kotlin
- Kubernetes
- Linux
- Prometheus
- Programming
- Python
- Self-Development
- Shell
- Software Architecture
- Spark
- SQL
- Solution Atchitect Associate
- Well-Architected Framework
- JAWS-UG Beginner #17
- JAWS-UG Beginner #20
- JAWS-UG Beginner #24
- 手を動かしながら2週間で学ぶAWS 基本から応用まで - Udemy
- Storm
- Flink Elasticsearch Connector
- 1001 Data Engineering Interview Questions
- Designing Data-Intensive Applications - Part 1. Foundations of Data System
- Designing Data-Intensive Applications - Part 2. Distributed Data
- データ分析基盤入門
- Fundamentals of Data Engineering
- 1. Data Engineering Described
- 2. The Data Engineering Lifecycle
- 3. Designing Good Data Architecture
- 4. Choosing Technologies Across The Data Engineering Lifecycle
- 5. Data Generation In Source Systems
- 6. Storage
- 7. Ingestion
- 8. Queries, Modeling, And Transformation
- 9. Serving Data For Analytics, Machine Learning, And Reverse ETL
- 10. Security And Privacy
- 11. The Future Of Data Engineering
- Appendix A. Serialization And Compression Technical Details
- Appendix B. Cloud Networking
- The Cloud Data Lake
- 1. Big Data: Beyond the Buzz
- 2. Big Data Architectures on the Cloud
- 3. Design Considerations for Your Data Lake
- 4. Scalable Data Lakes
- 5. Optimizing Cloud Data Lake Architectures for Performance
- 6. Deep Dive on Data Formats
- 7. Decision Framework for Your Architecture
- 8. Six Lessons for a Data Informed Future
- GCP Hands-on
- Google Cloud Platform Big Data and Machine Learning Fundamentals
- Serverless Machine Learning with Tensorflow on Google Cloud Platform
- Software Architecture Metrics
- 1. Four Key Metrics Unleashed
- 2. The Fitness Function Testing Pyramid: An Analogy for Architectural Tests and Metrics
- 3. Evolutionary Architecture: Guiding Architecture with Testability and Deployability
- 4. Improve Your Architecture with the Modularity Maturity Index
- 5. Private Builds and Metrics: Tools for Surviving DevOps Transitions
- 6. Scaling an Organization: The Central Role of Software Architecture
- 7. The Role of Measurement in Software Architecture
- 8. Progressing from Metrics to Engineering
- 9. Using Software Metrics to Ensure Maintainability
- 10. Measure the Unknown with the Goal-Question-Metric Approach