Iceberg Backup, Recovery, and Disaster Recovery: A Complete Guide
How to back up Apache Iceberg tables, recover dropped or purged tables from S3, and set up cross-region disaster recovery — covering register_table, rewrite_table_path, S3 versioning, and production recovery playbooks.
Iceberg Query Performance Tuning: Partition Pruning, Bloom Filters, and Spark Configs
Deep dive into how Apache Iceberg makes queries fast — from partition pruning and file-level data skipping with column statistics, to Parquet bloom filters for point lookups, and the Spark configurations that control it all.
Iceberg Table Design: Properties, Partitioning, and Commit Best Practices
A comprehensive guide to designing Apache Iceberg tables — covering every table property that matters, partition spec design with bucket, day, and other transforms, sizing guidelines, commit conflict resolution, and best practices for partitioned and non-partitioned tables.
How Apache Iceberg Makes Your Data AI-Ready: Feature Stores, Training Pipelines, and Agentic AI
AI workloads need versioned, reproducible, high-quality data — and Apache Iceberg delivers all three. Here is how to use Iceberg for feature stores, ML training data versioning, LLM fine-tuning pipelines, and agentic AI data access patterns.
Migrating From Hive Tables to Apache Iceberg: The Complete Guide — From On-Prem Hadoop to Cloud Lakehouse
The definitive guide to migrating Hive tables to Apache Iceberg — whether your data sits in on-prem Hadoop HDFS or cloud S3. Covers data transfer strategies, partition-by-partition migration with state tracking, running Hive and Iceberg in parallel, data validation, and production cutover.
Time Travel in Apache Iceberg: Beyond the Basics — Auditing, Debugging, and ML Reproducibility
Everyone knows Iceberg supports time travel. But most teams barely scratch the surface. Here is how to use snapshots, branches, tags, and the Write-Audit-Publish pattern for regulatory compliance, production debugging, and machine learning reproducibility.
Schema Evolution in Apache Iceberg: The Feature That Saves Data Teams Thousands of Hours
Adding a column to a production table should not require a migration project. Apache Iceberg's schema evolution lets you add, drop, rename, and widen columns through metadata-only changes — no data rewrites, no downtime, no broken pipelines.
Apache Polaris: How Policy-Managed Table Maintenance Eliminates Iceberg Operational Overhead
Apache Polaris brings policy-based table maintenance to Apache Iceberg — automatic compaction, snapshot expiry, orphan file removal, and metadata optimization. Learn how Cazpian's managed Polaris catalog eliminates the operational burden of Iceberg table maintenance entirely.
Mastering Iceberg File Sizes: How Spark Write Controls and Table Optimization Prevent the Small File Nightmare
Small files silently degrade your Iceberg table performance. Learn how Spark's write distribution modes, advisory partition sizing, target file size controls, and Iceberg's table maintenance procedures work together to keep your lakehouse fast and cost-efficient.
Why Every Data Company Is Betting on Apache Iceberg — And What It Means for AI
Snowflake, Databricks, Dremio, Starburst, Teradata — every major data platform is converging on Apache Iceberg. Here is why the open table format war is over, and how Iceberg is quietly becoming the foundation for the next generation of AI workloads.
One Engine, Two Access Paths: How Arrow Flight SQL Makes a Single-Engine Lakehouse Possible
The reason most lakehouses run two engines is not that Spark cannot query data — it is that Spark cannot deliver results fast enough for BI tools. Arrow Flight SQL changes that equation entirely.
Why Your Data Platform Runs Two Engines — And Why That's Costing You
Most lakehouse architectures quietly run Spark for ETL and Trino or Dremio for analytics. That dual-runtime setup has hidden costs in infrastructure, operations, governance, and engineering time that rarely show up on a single dashboard.