Ifkarsyah
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    • Apache Flink
    • Apache Kafka
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  • Blog
  • Portofolio
    • Clickstreamer: Clickstream Analytic powered by Apache Flink
    • Gardevoir: Fraud detection and prevention engine powered by Apache Flink
    • Pasin
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  • Blog
    • The Founding Data Engineer’s Playbook: How to Build a Lean, Effective Data Stack
    • Clickstreamer: Clickstream Analytic powered by Apache Flink
    • Gardevoir: Fraud detectio and prevention engine powered by Apache Flink
    • 🎉 Easily create your own simple yet highly customizable blog
    • 🧠 Sharpen your thinking with a second brain
    • 📈 Communicate your results effectively with the best data visualizations
    • 👩🏼‍🏫 Teach academic courses
    • ✅ Manage your projects
  • Book Review
    • Building Machine Learning Pipelines
    • Cost-Effective Data Pipelines
    • Data Intensive Application
    • Data Pipelines Pocket Reference
    • Database Internal
    • Database Internal
    • Deciphering Data Architectures
    • Fundamental of Data Engineering
    • Kubeflow for Machine Learning
    • Streaming Systems
    • Streaming Systems
  • Projects
  • Flink
    • 1. Flink Fundamental
      • Chapter 1
      • Chapter 2
    • 2. Flink for Developer
      • Chapter 1
      • Chapter 2
    • 3. Flink for SRE
      • Chapter 1
      • Chapter 2
    • 4. Flink Internal
      • Chapter 1
      • Chapter 2
  • Resume
  • Agnostic-Engineers
    • Different Type of Query Access Pattern
    • Dual-Write Problem and Outbox Transactional Pattern
    • Same Data in Different Format
    • SQL Tips
  • Apache Kafka
    • 1. Kafka Fundamental
      • 1.1. Introduction to Kafka
      • 1.2. Kafka History
      • 1.3. Kafka Quickstart
      • 1.4. Kafka Topic and Partition
      • 1.5. Kafka Producer and Consumer
      • 1.6. Kafka Producer and Consumer
      • 1.8. Kafka Broker, Cluster, Zookepeer/Raft
    • 2. Kafka for Developer
      • 2. Kafka for e-Commerce
      • 2. Kafka for Ride Hailing
      • Kafka for Fraud Detection
      • Kafka for Realtime Dashboard
      • kafka for realtime notification
    • 3. Kafka for Operator
      • 3.1. Deploying Kafka
      • 3.2. Monitoring Kafka
      • 3.2. Securing Kafka
      • 3.4. Upgrading Kafka
      • Democratizing Kafka in Organizations
  • Choosing-Tools
    • Business Intelligence: Superset vs. Metabase vs. Redash
    • Compute Engine: Spark vs. Flink
    • Data Format: Avro vs. Protobuf vs. Parquet
    • Database Comparison: PostgreSQL vs. MySQL
    • Streaming SQL Engine Comparison
    • Table Format: Hudi vs. Iceberg vs. Delta Lake
  • PostgreSQL
    • 1. Postgres Fundamental
    • 2. PostgreSQL for Developers
      • Ways to Export Data from PosgreSQL to External Sources
      • Ways to Import Data to PostgreSQL
    • 3. Postgres for Operator
    • 4. PostgreSQL Internal
      • How PostgreSQL maps your tables into physical files
      • What is Write-ahead Log? How it Works?
  • Thinking-Frameworks
    • My Framework for Reading (Technical) Books
    • My Framework when Choosing a new Tool
  • 1. Kafka Fundamental
    • 1.1. Introduction to Kafka
    • 1.2. Kafka History
    • 1.3. Kafka Quickstart
    • 1.4. Kafka Topic and Partition
    • 1.5. Kafka Producer and Consumer
    • 1.6. Kafka Producer and Consumer
    • 1.8. Kafka Broker, Cluster, Zookepeer/Raft
  • 2. Kafka for Developer
    • 2. Kafka for e-Commerce
    • 2. Kafka for Ride Hailing
    • Kafka for Fraud Detection
    • Kafka for Realtime Dashboard
    • kafka for realtime notification
  • 3. Kafka for Operator
    • 3.1. Deploying Kafka
    • 3.2. Monitoring Kafka
    • 3.2. Securing Kafka
    • 3.4. Upgrading Kafka
    • Democratizing Kafka in Organizations
Apache Kafka
3. Kafka for Operator
3.4. Upgrading Kafka

3.4. Upgrading Kafka

← 3.2. Securing Kafka Jan 1, 0001
Democratizing Kafka in Organizations Jan 1, 0001 →

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