Build a reliable multi engine lakehouse catalog with Apache Polaris and keep Iceberg, Delta and your engines under control.
Modern data platforms rarely run on a single engine. Spark, Trino, Flink, managed warehouses and streaming jobs all compete for the same data in object storage. Without a shared catalog, schemas drift, permissions fragment and simple changes become risky and slow.
Build Multi-Engine Lakehouse Catalogs with Apache Polaris gives data platform engineers, architects and senior data engineers a practical way to standardize on an open catalog service. You will learn how to design, deploy and operate Polaris so it can coordinate Iceberg and Delta tables, enforce governance and integrate cleanly with your existing engines and infrastructure.
- Understand the catalog problem in multi engine lakehouses and why open catalog services matter
- Compare Iceberg, Delta, Hudi, Paimon and other table formats from a catalog perspective
- Position Polaris among Hive Metastore, Glue, Unity Catalog, Nessie and similar services
- Model catalogs, namespaces, tables, views and securable objects for real data products
- Configure relational metastores, JDBC pools, retries and capacity planning for high write concurrency
- Design RBAC policies, roles and namespaces that reflect producer and consumer responsibilities
- Authenticate users, services and engines, and implement credential vending for secure object storage access
- Federate existing Hive metastores and external REST catalogs, and govern native and federated data consistently
- Register and query Delta Lake and other generic tables through Polaris alongside Iceberg
- Connect Spark, Trino and managed engines to Polaris using open catalog services
- Build streaming and CDC pipelines with Kafka and Flink that write into Iceberg tables managed by Polaris
- Deploy Polaris on Kubernetes using Helm, TLS, network policies and high availability patterns
- Set up logging, metrics, tracing and SLOs for catalog reliability and on call operations
- Use migration playbooks from Hive and vendor catalogs, including phased rollout, cutover and rollback strategies
- Design multi tenant and compliance focused catalogs, with isolation and sharing patterns that scale
- Harden Polaris against storage, network and metastore failures, and run chaos experiments and recovery drills
- Apply reference architectures for analytics, streaming and hybrid managed or self hosted engines, and avoid common anti patterns
- Follow a pragmatic roadmap and implementation checklist to structure your first Polaris adoption project
The book includes concrete migration playbooks, reference architectures and an implementation checklist so you can move from theory to a staged rollout that fits your platform and governance constraints.
Throughout the chapters you will work with realistic configuration snippets and code examples for Spark catalogs, Trino connectors, streaming jobs and Kubernetes deployments, so you can adapt the patterns directly into your own repositories and environments.
Grab your copy today and design a multi engine lakehouse catalog your data platform can trust.
