3 Reasons Data Engineers Should Embrace Apache Iceberg
Tags: datascience, dataengineering, apacheiceberg
Published: 2024-05-15 16:34:09

Data engineers are constantly seeking ways to streamline workflows and enhance data management efficiency. [Apache Iceberg, a high-performance table format](https://www.dremio.com/blog/apache-iceberg-101-your-guide-to-learning-apache-iceberg-concepts-and-practices/) for huge analytic datasets, has emerged as a game-changer in the field. By offering powerful features such as hidden partitioning, seamless partition evolution, and extensive tool compatibility, Iceberg simplifies data engineering tasks and boosts productivity. In this blog, we will delve into three key reasons why data engineers should embrace Apache Iceberg and how it can make their lives easier.

## 1. Hidden Partitioning

Traditionally, with Hive tables, data engineers often needed to create additional columns, such as day, month, and year, derived from a timestamp column for partitioning. This not only added extra work at the ingestion stage but also increased the size of data files. Moreover, data analysts had to be educated on how to query these columns to take advantage of partitioning. 

Apache Iceberg revolutionizes this process with its [hidden partitioning feature](https://www.dremio.com/subsurface/fewer-accidental-full-table-scans-brought-to-you-by-apache-icebergs-hidden-partitioning/). Partitioning in Iceberg is a metadata operation, allowing you to express transforms like day, month, and year directly in your table's DDL. Instead of inflating data files, Iceberg tracks partition value ranges in the metadata, making the relationships between columns explicit. As a result, analysts do not need to update their queries to benefit from partitioning. This significantly reduces both the complexity of managing partitioned data and the overhead on data storage, leading to more efficient and streamlined data processing workflows.

## 2. Seamless Partition Evolution

As data needs change, so too must the ways in which we partition our tables. For example, you might start with year-based partitioning and later realize that month-based partitioning would better suit your queries and data access patterns. In the past, changing the partitioning scheme required rewriting the entire table and all its data, a time-consuming and resource-intensive process.

[Apache Iceberg offers a much more flexible approach with seamless partition evolution](https://www.dremio.com/subsurface/future-proof-partitioning-and-fewer-table-rewrites-with-apache-iceberg/). In Iceberg, you can update a table's partitioning scheme without having to rewrite existing data. Simply modify the partitioning strategy with an ALTER TABLE statement, and all future data writes will use the new scheme. The metadata keeps track of which data files are associated with which partitioning scheme, allowing for smooth transitions and backward compatibility. This feature greatly simplifies the process of adapting to evolving data requirements, saving time and reducing operational complexity for data engineers.

## 3. Extensive Tool Compatibility

One of the standout features of [Apache Iceberg is its vast ecosystem of tools for reading, writing, and managing Iceberg tables](https://www.youtube.com/watch?v=hh7wU9H2jz8&pp=ygUYQXBhY2hlIEljZWJlcmcgZWNvc3lzdGVt). Unlike other solutions that may confine you to a limited set of tools, Iceberg integrates seamlessly with a wide range of technologies, allowing you to choose the tools that best fit your workflow and preferences.

While Iceberg works exceptionally well with staple technologies like Apache Flink and Apache Spark, its compatibility extends far beyond these. You can leverage tools such as Dremio, Upsolver, Fivetran, Airbyte, Kafka Connect, Puppygraph, and many more. This extensive compatibility ensures that you are not locked into a specific technology stack and can adopt the tools that offer the most value for your specific use cases. The flexibility and choice provided by Iceberg's ecosystem empower data engineers to build more efficient, scalable, and adaptable data pipelines.

## Conclusion

Apache Iceberg is transforming the way data engineers manage and optimize large datasets. With features like hidden partitioning, seamless partition evolution, and extensive tool compatibility, Iceberg not only simplifies complex data engineering tasks but also enhances the overall efficiency and flexibility of data workflows. By embracing Apache Iceberg, data engineers can reduce operational overhead, streamline data processing, and leverage a robust ecosystem of tools to meet their evolving data needs. The adoption of Apache Iceberg is a strategic move towards building more scalable, adaptable, and performant data platforms.

### Exercises to Get Hands-on with Apache Iceberg on Your Laptop
- [Intro to Apache Iceberg, Nessie and Dremio on your Laptop](https://bit.ly/am-dremio-lakehouse-laptop)
- [JSON/CSV/Parquet to Apache Iceberg to BI Dashboard](https://bit.ly/am-json-csv-parquet-dremio)
- [From MongoDB to Apache Iceberg to BI Dashboard](https://bit.ly/am-mongodb-dashboard)
- [From SQLServer to Apache Iceberg to BI Dashboard](https://bit.ly/am-sqlserver-dashboard)
- [From Postgres to Apache Iceberg to BI Dashboard](https://bit.ly/am-postgres-to-dashboard)
- [Mongo/Postgres to Apache Iceberg to BI Dashboard using Git for Data and DBT](https://bit.ly/dremio-experience)
- [Elasticsearch to Apache Iceberg to BI Dashboard](https://bit.ly/am-dremio-elastic)
- [MySQL to Apache Iceberg to BI Dashboard](https://bit.ly/am-dremio-mysql-dashboard)
- [Apache Kafka to Apache Iceberg to Dremio](https://bit.ly/am-kafka-connect-dremio)
- [Apache Iceberg Lakehouse Engineering Video Playlist](https://bit.ly/am-iceberg-lakehouse-engineering)
- [Apache Druid to Apache Iceberg to BI Dashboard](https://bit.ly/am-druid-dremio)
- [Postgres to Apache Iceberg to Dashboard with Spark & Dremio](https://bit.ly/end-to-end-de-tutorial)