23 posts tagged with "Apache Iceberg"

You have an operational database — PostgreSQL, MySQL, or DynamoDB — and you need its data in your Iceberg lakehouse. Not a daily snapshot dump. Not a nightly batch export. You need changes replicated continuously so that your analytics, ML models, and dashboards reflect reality within minutes.

This is Change Data Capture (CDC) on Iceberg, and it is one of the most common — and most operationally challenging — data engineering patterns in production today. The ingestion part is straightforward. The hard parts are handling deletes efficiently, keeping read performance from degrading, managing schema changes, and operating the pipeline at scale without it falling over at 3 AM.

This guide covers the two primary CDC architectures (direct materialization and the bronze-silver pattern), table design for CDC workloads, Iceberg's built-in CDC capabilities, compaction strategies, and the operational patterns that keep CDC pipelines healthy in production.

MERGE INTO is the most powerful and the most misused operation in Apache Iceberg. It handles upserts, conditional deletes, SCD Type-2 updates, and CDC application — all in a single atomic statement. But it is also the operation most likely to trigger a full table scan, blow up your compute costs, and produce thousands of small files if you do not write it carefully.

The difference between a well-written and a poorly-written MERGE INTO on the same table can be the difference between 30 seconds and 30 minutes — and between $2 and $200 in compute cost. This post shows you exactly how to write it right.

Someone dropped the table. Or worse — they dropped it and ran expire_snapshots and remove_orphan_files. The catalog entry is gone. The metadata cleanup already happened. Your Slack channel is on fire. Can you recover?

The answer depends entirely on what you set up before the disaster. Apache Iceberg does not have a built-in backup command. There is no UNDROP TABLE that magically restores everything. But Iceberg's architecture — with its layered metadata files, immutable snapshots, and absolute file paths — gives you powerful building blocks for backup and recovery if you understand how they work.

This guide covers three scenarios: recovering a dropped table when data files still exist on S3, building a proper backup strategy so you are always prepared, and setting up cross-region disaster recovery for production-critical tables.

Your Iceberg tables are created with the right properties. Your partitions are well-designed. But your queries are still slower than you expected. The dashboard that should load in 3 seconds takes 45. The data scientist's notebook times out. The problem is not your table design — it is that you have not tuned the layers between the query and the data.

Apache Iceberg has a sophisticated query planning pipeline that can skip entire partitions, skip individual files within a partition, and even skip row groups within a file. But each of these layers only works if you configure it correctly. This post walks through every pruning layer, explains exactly how Iceberg uses metadata to skip work, and gives you the Spark configurations to control it all.

You have just migrated to Apache Iceberg — or you are about to create your first Iceberg table. You open the documentation and find dozens of table properties, multiple partition transforms, and configuration knobs that interact with each other in non-obvious ways. Where do you start? Which properties actually matter? How many buckets should you use? What happens when two jobs write to the same table at the same time?

This guide answers all of those questions. We will walk through every table property that matters for production Iceberg tables, explain how to design partition specs that balance read and write performance, cover commit conflict resolution, and give you concrete recommendations for both partitioned and non-partitioned tables.

Every AI project starts with the same bottleneck: data. Not the volume of data — most organizations have plenty of that. The bottleneck is data quality, data versioning, and data reproducibility. Can you guarantee that the dataset you trained on last month has not changed? Can you trace exactly which features went into a model prediction? Can you roll back a corrupted training set in minutes instead of days?

These are data engineering problems, not machine learning problems. And Apache Iceberg — originally built for large-scale analytics — turns out to solve them remarkably well.

This post covers four concrete patterns for using Iceberg as the data foundation for AI workloads: feature stores, training data versioning, LLM fine-tuning pipelines, and agentic AI data access.

If you are reading this, you probably fall into one of two camps. Either your Hive tables are already on cloud object storage (S3, GCS, ADLS) and you want to convert them to Iceberg format. Or — and this is the harder problem — your Hive tables are sitting on an on-premises Hadoop cluster with HDFS, and you need to move everything to a cloud-based lakehouse with Iceberg.

This guide covers both scenarios. We start with the harder one — migrating from on-prem Hadoop HDFS to a cloud data lake with Iceberg — because that is where most teams get stuck. Then we cover the table format conversion for data already on cloud storage. Both paths converge at the same destination: a modern, open lakehouse built on Apache Iceberg.

Every Apache Iceberg overview mentions time travel. "Query your data as it existed at any point in time." It sounds impressive, gets a mention in the feature list, and then most teams never use it beyond the occasional ad-hoc debugging query.

That is a missed opportunity. Iceberg's snapshot system is not just a convenience feature — it is a production-grade capability that can replace custom auditing infrastructure, eliminate data recovery anxiety, and solve one of machine learning's hardest problems: dataset reproducibility.

This post goes beyond the basics. We will cover the snapshot architecture, the practical query patterns, branching and tagging, the Write-Audit-Publish pattern, and real-world use cases that make time travel indispensable.

Every data engineer has lived this nightmare: a product team needs a new field in the events table. In a traditional data warehouse, this means a migration ticket, a maintenance window, potentially hours of data rewriting, and a prayer that no downstream pipeline breaks. In a Hive-based data lake, it is even worse — you add the column, but old Parquet files do not have it, partition metadata gets confused, and three different teams spend a week debugging null values.

Apache Iceberg eliminates this entire class of problems. Schema evolution in Iceberg is a metadata-only operation. No data rewrites. No downtime. No broken queries. And the mechanism that makes this possible is both simple and elegant.

In our previous post, we covered how to control Iceberg file sizes at write time and how to fix small file problems with Iceberg's table maintenance procedures. The conclusion was clear: the tools are excellent, but manually scheduling and managing maintenance across dozens or hundreds of tables does not scale.

This post is about the layer that solves that problem: Apache Polaris — the open-source Iceberg catalog that introduces policy-based table maintenance, letting you define optimization rules once and have them applied automatically across your entire lakehouse.