Title here
Summary here
Nessie: Syllabus
Mastering Apache Nessie: Data Versioning for Data Lakes with MinIO & Iceberg
Apache Nessie is an open-source, Git-like data catalog designed for version control in data lakes. It enables branching, merging, and time-travel capabilities in structured and unstructured datasets, making it a powerful tool when combined with MinIO and Apache Iceberg. This book provides a hands-on, implementation-first approach to mastering Apache Nessie and its integrations in cloud-native data lakes.
Module 1: Introduction to Data Lake Versioning
- Understanding the need for data versioning in modern data lakes
- Comparison of Apache Nessie, Delta Lake, and Apache Iceberg
- Benefits of Git-like version control for structured datasets
- The role of MinIO in modern object storage
Module 2: Setting Up Apache Nessie with MinIO & Iceberg
- Installing and configuring MinIO as the object storage backend
- Deploying Apache Nessie with Docker and Kubernetes
- Connecting Apache Iceberg with Nessie for table versioning
- Understanding the Nessie catalog and metadata handling
Module 3: Apache Nessie Data Versioning Model
- Introduction to commits, branches, and tags in Nessie
- Implementing time travel for dataset auditing
- Creating, merging, and rolling back dataset versions
- Managing concurrent transactions in multi-user environments
Module 4: Managing Schema Evolution with Nessie
- Implementing schema versioning with Apache Iceberg
- Handling schema changes without breaking downstream applications
- Using Nessie for structured and unstructured data tracking
- Best practices for schema enforcement and validation
Module 5: Data Governance and Security in Nessie
- Implementing role-based access control for Nessie
- Auditing data changes and tracking lineage
- Encryption and security best practices with MinIO and Nessie
- Compliance considerations (GDPR, HIPAA, SOC 2)
Module 6: Performance Optimization for Large-Scale Data Lakes
- Optimizing Nessie performance for high-scale workloads
- Managing metadata efficiently for fast query performance
- Partitioning and compaction strategies for large datasets
- Scaling Nessie and Iceberg clusters in multi-cloud environments
Module 7: Real-Time Data Processing with Apache Nessie
- Integrating Nessie with Apache Spark and Flink
- Implementing Change Data Capture (CDC) with Nessie branches
- Streaming data ingestion and real-time updates
- Querying versioned datasets with Presto and Trino
Module 8: Deploying Apache Nessie in Production
- Deploying Nessie with Kubernetes and Helm charts
- Running Nessie in cloud environments (AWS, Azure, GCP)
- Implementing CI/CD pipelines for automated data versioning
- Best practices for monitoring and troubleshooting Nessie deployments
Hands-On Projects
Project 1: Building a Version-Controlled Data Lake with Nessie, Iceberg & MinIO
- Set up a data lake architecture using Nessie, Iceberg, and MinIO
- Implement Git-like branching for different datasets
- Perform schema versioning and rollback operations
Project 2: Real-Time Data Ingestion with Nessie & Spark Streaming
- Stream real-time data into Iceberg tables tracked by Nessie
- Implement time travel queries for historical data retrieval
- Optimize streaming performance with partitioned datasets
Project 3: Implementing Change Data Capture (CDC) with Nessie
- Track data changes in a multi-branch Iceberg dataset
- Implement automatic rollback for incorrect data updates
- Integrate Nessie CDC with external analytics tools
Project 4: Secure and Compliant Data Lake with Nessie
- Implement IAM policies for fine-grained access control
- Secure data transactions with encryption and access logging
- Deploy Nessie on Kubernetes with high availability settings
Project 5: Data Lakehouse with Nessie, Iceberg & MinIO for ML Pipelines
- Use Nessie to track ML training datasets and feature stores
- Implement versioned model training pipelines
- Automate data lineage tracking for explainability and compliance