Apache Iceberg: What It Does

Iceberg is a table format for managing large datasets in data lakes. It helps organize data efficiently by allowing you to work with tables that store both metadata and actual data (in formats like Parquet or Avro). Think of it like a super-powered file system that also keeps track of changes and versions, making it easy to query data as it was at any point in time, plus it’s great for big-scale, real-time, and batch data.

How Iceberg Works Internally

Iceberg Tables:

• Iceberg organizes data into tables. Each table is a collection of data files (like Parquet or Avro files) and has metadata to track how data is partitioned, how it’s evolving, and what version it’s at.
• The cool thing is, you don’t have to manage the partitions or versions manually — it does it for you. It lets you handle big data just like a relational database but for distributed systems.

Metadata and Versioning:

• Iceberg uses a system of metadata files to track the state of the data, such as which files belong to which partition and how they should be read.
• Every change creates a new snapshot of the table. You can look back at older versions of the data (like “time travel”), which is useful for things like data audits, troubleshooting, or re-running analysis from past data states.

Partitioning:

• You can choose how data is partitioned (e.g., by date or other columns), which helps Iceberg figure out which pieces of the data to read when you run a query, improving speed and efficiency.
• Iceberg does this automatically, so it’s like the table is constantly optimized for performance.

Schema Evolution:

• With Iceberg, if you want to add new columns or change the structure of your data, you can do so without breaking anything. It keeps track of schema changes over time so that even if the data structure changes, you can still read the data as it was at any point in time.

Working with Parquet and Avro:

• Iceberg supports Parquet (columnar format, great for analytics) and Avro (row-based, good for streaming data). It uses these formats for storing the actual data inside Iceberg tables, but it provides extra features on top like versioning, partitioning, and schema evolution.

Iceberg + Parquet + Avro: How They Work Together

Imagine you have a data pipeline that pulls data from Kafka or batch jobs, formats it in Avro (if it’s row-based data) or Parquet (if it’s more analytical), and stores it in an Iceberg table.

Here’s what happens:

1. The data gets ingested and stored in Parquet or Avro files inside an Iceberg table.
2. Iceberg helps organize it into partitions and tracks the schema and data versions.
3. Then, when you query the data using engines like Spark or Presto, Iceberg makes sure you only read the data you need, improving performance.

Comparison of Iceberg with Other Formats

Putting It All Together

1. Data Ingestion: You can pull in data from various sources (like Kafka or batch processes) and store it in Avro (for event data) or Parquet (for analytical data).
2. Storage: Iceberg manages everything at the table level. It organizes your data into partitions, tracks versions, and helps you evolve your schema over time without breaking anything.
3. Querying: When you need to run queries, Iceberg ensures that only the data you need is scanned, and it even supports time travel (looking at the data as it was at any specific point in time).

Why Use Iceberg?

• Iceberg is perfect when you need large-scale data management in your data lake but also want to have full control over versioning, schema evolution, and data partitioning. It abstracts away a lot of the complexity and makes it easier to manage Parquet and Avro files at a table level.

So, if you’re working with huge datasets and want to track changes, work with different file formats, or support real-time and batch workloads, Iceberg is a powerful tool to have in your data pipeline!