The Future of Data Lakehouses: Apache Iceberg Explained

We know that data management today is changing completely. For decades, businesses relied on data warehouses, which stored information in an appropriate manner. They are structured, governed, and quick to extract information from, although expensive and rigid in nature. In contrast, data lakes are more efficient and allow for the storage of enormous amounts of data regardless of structure. However, the emergence of the data lakehouse architecture combines the benefits of the data lakes and data warehouses. Lakehouse models allow the retention of the flexibility provided by data lakes while integrating the reliability, governance, and performance of a data warehouse.

The most notable open-source table format created for large-scale data analytics is Apache Iceberg. Iceberg is at the forefront of this transformation and enhances the value of data in the lakehouse architecture. Additionally, Iceberg provides solutions for many of the problems that data lakes face, including schema evolution, ACID transactions, data consistency, and query performance. 

This blog post is the first in a three-part series exploring Apache Iceberg and its role in modern data architectures. In this post, the focus will be on the following topics:

1. The evolution from data warehouses to data lakes
2. The challenges associated with traditional approaches
3. How Apache Iceberg addresses these limitations

The following posts in this series will take a deeper dive into the architecture of Iceberg and explore how queries work within Iceberg tables.

Brief Summary: The Evolution of Data Lakes from Data Warehouses

For countless years, businesses have depended upon data warehouses, which served as the central foundation for analytics. A data warehouse acts as a centralized repository meant to capture structured data from varied sources to produce reports and insights efficiently. Nowadays, data warehouses facilitate rapid query performance along with robust governance mechanisms, enabling them to be reliable and well-structured.

Nonetheless, organizations encountered new problems as data volume increased.

1. Increasing costs of storage because of high compute and storage fees
2. Schema enforcement that was rigid, which made the integration of semi-structured or unstructured data challenging
3. Support for AI and machine learning workloads was limited

As a solution, companies began making use of data lakes. Doing so enabled businesses to retain raw data, structured data, and unstructured data within cheap storage such as Amazon S3, Azure Data Lake Storage, Google Cloud Storage, and Hadoop Distributed File System.

The benefits of data lakes encompassed the following factors:

1. Storage cost reduction within particular cloud environments
2. Use of novel data formats such as unstructured, semi-structured, and even structured data
3. Enhanced usage of AI and machine learning applications

Even with these advantages, data lakes presented new problems like:

1. The absence of governance and schema, which caused inconsistent data sets.
2. The inefficient utilization of indexing and full table scans made the query performance slower.
3. The absence of ACID transactions made the assurance of data integrity in multi-user settings strenuous.

The Emergence of the Data Lakehouse

A data lakehouse merges all the scalability and economical advantages of a data lake with the productivity, dependability, and transactional features of a data warehouse. This is a modern design paradigm.

The main advantages of a data lake house include:

1. Accommodating both storage and processing functions at a lower price
2. Efficient data control through ACID transactions 
3. Modification of Schema without affecting existing queries - Schema evolution
4. Retrieval of table’s previous versions through Time travel capabilities

The introduction of these capabilities resulted in the development of modern table formats such as Delta Lake, Apache Hudi, and Apache Iceberg. These structures made it easier for data lakes to operate like data warehouses while still having the freedom to remain flexible and unstructured due to the introduction of structured metadata layers. Among these table formats, Apache Iceberg has emerged as a leading choice, offering a robust solution for organizations looking to transition to data lakehouse architectures.

Apache Iceberg: A Game-Changer for Data Lakehouses

What Is a Table Format?

Using a table format provides a way to effectively manage large sets of information stored in a data lake. It encapsulates a few functions, including:

1. Structuring of data into tables for effective management and queries.
2. Efficiently eliminating and updating data, as well as altering schemas.
3. Improving performance on query response times by changing the metadata.

As with other traditional table formats like Hive, there was reliance on storage that was directory-based meaning that tables were constructed from the contents of certain file directories. This method, however, created a performance bottleneck since query engines had to filter through whole directories prior to executing a query.

The Evolution from Hive to Iceberg

The Hive table format sought to solve the problem of structuring data lakes in the early years. The purpose was to let users organize datasets into tables with queries such as those used in SQL by Apache Hive and Presto. However, there are significant drawbacks to the Hive format:

1. Inconsistencies due to concurrent writes from different sources because of lack of ACID transactions.
2. Expensive law file listing operations performed due to inefficient management of metadata.
3. Slow queries caused due to the manual optimization partitioning challenges.

How Apache Iceberg Solves These Challenges

Apache Iceberg is a modern table format that eliminates the issues presented with Hive while providing ACID transactions, schema evolution, and rapid performance on queries with data lakes.

Among the important benefits of Apache Iceberg are:

1. ACID transactions that guarantee reliable data updates and consistency.
2. Capability to travel through time and query the historical data snapshots.
3. Schema evolution which allows adding, renaming, or dropping columns without breaking existing queries.
4. Evolution of partition that automatically optimizes partitioning strategies.
5. Reduction of unnecessary file scans enables faster query execution because of the efficient management of metadata.

Final Remarks

Data management has changed dramatically with the emergence of Apache Iceberg. It allows companies transitioning into a data lakehouse approach to manage their information in a cost-effective and highly scalable way without sacrificing performance. It's clear that analytics have been transformed with the invention of Apache Iceberg, and its usage is increasing constantly. Watch out for the final post of this series. For the following two blog posts, the focus is going to shift to:

• Part 2: Apache Iceberg's architecture, including the data, metadata, and catalog layers.
• Part 3: The mechanisms of queries in Iceberg, including read and write, time travel, and query optimization methods.