Iceberg: Syllabus

Mastering Apache Iceberg: Scalable Data Lakes with MinIO

Apache Iceberg is an open-source table format designed for large-scale data lake storage, bringing SQL table functionality to data lakes while ensuring high performance, ACID compliance, and schema evolution. This book provides a hands-on, practical approach to building, managing, and optimizing Iceberg-based data lakes with MinIO as the storage backend.

Module 1: Introduction to Data Lake Architecture#

• Understanding the evolution of data lakes
• Challenges of traditional data lakes and how Apache Iceberg solves them
• Comparison of Apache Iceberg, Delta Lake, and Apache Hudi
• Introduction to MinIO as an S3-compatible storage backend

Module 2: Setting Up Apache Iceberg with MinIO#

• Installing and configuring MinIO for Iceberg storage
• Deploying Apache Iceberg with Apache Spark, Trino, and Flink
• Connecting Iceberg to MinIO using the S3 API
• Creating and managing Iceberg tables on MinIO

Module 3: Apache Iceberg Table Format and Transactions#

• Deep dive into Iceberg’s table format and metadata handling
• Implementing ACID transactions in a data lake
• Understanding snapshots and time travel queries
• Concurrent reads and writes with optimistic concurrency control

Module 4: Data Ingestion and ETL with Apache Iceberg#

• Batch vs. streaming ingestion with Iceberg
• Writing efficient ETL pipelines using Apache Spark and Flink
• Handling schema evolution and updates in Iceberg tables
• Partitioning and sorting strategies for optimized queries

Module 5: Performance Optimization in Apache Iceberg#

• Optimizing queries with hidden partitioning and pruning
• Compacting small files and optimizing table layouts
• Implementing data skipping and vectorized reads
• Best practices for scaling Apache Iceberg with MinIO

Module 6: Data Governance and Security#

• Implementing fine-grained access control with IAM policies
• Auditing Iceberg table changes and maintaining data integrity
• Encryption at rest and in transit for Iceberg data on MinIO
• Compliance and regulatory considerations (GDPR, HIPAA, SOC 2)

Module 7: Analytics and Machine Learning with Apache Iceberg#

• Querying Iceberg tables with Apache Spark, Trino, and Presto
• Using Iceberg for feature engineering and ML workflows
• Implementing real-time analytics with Apache Flink and Iceberg
• Data versioning and rollback for reproducible ML models

Module 8: Real-Time Streaming and Change Data Capture (CDC)#

• Using Apache Iceberg for incremental data ingestion
• Structured Streaming with Apache Spark and Flink
• Implementing Change Data Capture (CDC) in Iceberg tables
• Managing upserts and deletes efficiently with MERGE operations

Module 9: Cloud-Native Deployment and Scaling#

• Deploying Apache Iceberg on Kubernetes with MinIO
• Running Iceberg in multi-cloud environments (AWS, Azure, GCP)
• Automating Iceberg table management with Terraform and Ansible
• Best practices for high availability and fault tolerance

Hands-On Projects#

Project 1: Building a Data Lake with Apache Iceberg and MinIO#

• Set up MinIO as an object storage backend
• Create and manage Iceberg tables for structured and semi-structured data
• Implement data ingestion, transformations, and querying

Project 2: Real-Time Data Processing Pipeline with Iceberg and Spark Streaming#

• Stream data from Kafka into Apache Iceberg
• Implement time travel and rollback features
• Perform real-time analytics with Spark SQL

Project 3: Feature Store for Machine Learning Using Iceberg#

• Build a feature store using Iceberg tables
• Integrate Iceberg with ML models in Databricks or PyTorch
• Implement snapshot-based versioning for ML datasets

Project 4: Secure and Scalable Iceberg-Based Data Lake#

• Implement IAM policies for MinIO and Iceberg access control
• Encrypt data at rest and in transit using TLS and encryption keys
• Deploy Iceberg with Kubernetes for high availability and security

Project 5: Cloud-Native Data Lake with Serverless Processing#

• Deploy Apache Iceberg with AWS Lambda for serverless processing
• Automate ETL workflows with Apache Airflow and Iceberg
• Optimize storage costs with tiering and lifecycle management

References#

• Apache Iceberg Official Documentation
• MinIO Official Documentation
• Apache Spark and Iceberg Integration
• Best Practices for Data Lakes