Is Apache Iceberg a game-changer for Data Lakehouse Implementations?
To understand how Apache Iceberg fits the Data Architecture, let us demystify the hype.
Apache Iceberg
Traditional Apache Hive data warehouse implementations on the Hadoop Framework addressed the big datastore issue using structured data.
Alongside MapReduce Java APIs, the Hive-based SQL query-like (HiveQL) interface used to query distributed data had limitations.
With the increasing demand for faster and more reliable data analysis, Hive faced three significant challenges, highlighting the urgent need for a better solution -
There is no guarantee of accuracy and no stable ACID transactions.
Could not scale and perform for larger workload with faster responses.
Cumbersome operations to maintain the tables.
Apache Iceberg originally started at Netflix as a Ryan Blue and Dan Weeks project to address these issues. It was later released under the Apache Licenses.
The advantage of being a high-performant table format and a common source for multiple SQL query engines, streaming engines, and data warehouses to connect simultaneously. Some well-known data processors that work efficiently with Apache Iceberg are - Spark, Flink, Trino, Presto, Hive, Impala, etc.
Why is there a sudden hype about Apache Iceberg?
Aside from its recent popularity in the field of Data Engineering due to Apache Iceberg's flexibility, performance, and other technical advantages - the hype started around the recent Snowflake and Databricks summits.
Databricks acquired a data management startup, Tabular (founded by the original creators of Apache Iceberg), which solidified the need for Iceberg in the Data Lakehouse Architecture.
This acquisition's main driver is simplifying the data infrastructure and compatibility by eliminating format limitations and standardizing interoperability.
Where does Apache Iceberg fit alongside Apache Hudi and Delta Lake?
Iceberg, Hudi, and Delta Lake are popular data formats for separate use cases.
While Iceberg focuses on Data Lakehouse analytics (bridging the Data Lake and Data Warehouse) and provides efficient query performance, Hudi fits well for real-time data processing and streaming analysis use cases requiring change data capture (CDC). Delta Lake, however, works well for traditional analysis based on Data Lakes.
So, each format operates well for various types of analytics use cases based on the organization's foundational architecture setup.
Who should consider the Apache Iceberg format in their Data Architecture?
Apache Iceberg promises interoperability by allowing multiple data-processing engines to access the open-table format simultaneously.
If the current implementation is based on Delta Lake and supports ACID transactions through SparkSQL without requiring inheriting legacy Data Warehouses or real-time streaming data analytics, then adding Iceberg or Hudi will make the architecture complex and have no value proposition.
However, suppose the current requirements are changed to consolidating legacy warehouses, and those use cases align with Apache Iceberg's strengths. In that case, it can fit well alongside or migrate out of Delta Lake. It can eliminate the need for Hudi (assuming no CDC or real-time analytics needs).