In today’s digital world, data is not just an asset; it is the lifeblood of modern organizations. It plays a pivotal role in shaping the trajectory of businesses. Whether an organization wants to figure out historical trends of annual product sales or look into the crystal ball to predict future opportunities, data serves as an inevitable tool for decision making. It guides developers, engineers and the rest of the organization’s stakeholders through uncharted waters.
The inevitable need has led to an increasing demand for collecting, storing and analyzing data. While databases and data warehouses have been around for this purpose, they seem to be inefficient for gigantic datasets. Here comes the need of “data lake”. Data lakes extend support in analyzing and predicting decisions for the business – using Business Intelligence (BI) and Machine Learning (ML) tools.
Now a query arises: how can we manage data in data lakes? The answer is through “Apache Iceberg”.
Let’s begin with a brief introduction of Apache Iceberg, what’s the role of it in your data storage and querying needs, and why you should use it for your data lake.
But first, we’ll begin with “what led to the invention of Apache Iceberg?”
Bits From the History
In the past, data engineers required something to organize data files and arrange them in an accessible manner. They came up with the “Tables” format. Apache Hive, developed by Facebook, was the first-ever table format that intended to organize complex databases from HDFS (Hadoop distributed file system). Hive was predominantly used for semi-structured and structured databases. It was a popular table format until the need for scalability and handling complex data structures increased.
The drawbacks in Hive led to the invention of Apache Iceberg.
What Do You Mean By Apache Iceberg?
Apache Iceberg is an open-source table format designed to address the complexities of managing and querying large datasets in data lakes and other storage systems. Iceberg creates metadata around files and helps tools to see them as tables. This also leverages the functionalities of SQL tables within the traditional bases. Its robust features and capabilities make it a compelling choice for organizations seeking a structured and efficient approach to data management.
How is Apache Iceberg Better Than Apache Hive?
In Apache Hive, data is managed and tracked at the folder level. Whereas, Apache Iceberg tracks data at file levels. The file-level data tracking allows users to micromanage datasets and makes it easy to change any record without going through massive alterations within the whole directory.
The flaw is within the Hive as it takes a lot more time to track data. By tracking a table as a list of directories and subdirectories, performance is being compromised.
Compelling Reasons to Choose Apache Iceberg
There are umpteen reasons to choose Iceberg over Apache Hive. We are going to discuss a few below:
1. Schema Evolution
Another big challenge for hive was to configure physical optimization of data. Apache Iceberg offers comprehensive support for schema evolution, a critical feature in dynamic data environments. This means you can seamlessly add, remove, or modify columns within your datasets without rewriting existing data or queries. It provides the necessary flexibility for accommodating changes in data structures over time.
2. Transaction Support
Iceberg is built with transactional capabilities at its core, ensuring compliance with the principles of ACID (Atomicity, Consistency, Isolation, Durability). This feature is paramount when dealing with concurrent data writes, maintaining data integrity, and preventing inconsistencies during complex data operations.
3. Time Travel
A standout feature of Apache Iceberg is its “versioning feature”. It facilitates time travel queries by keeping a record of past changes. This means you can access historical data snapshots at any point in time.
Usually, Iceberg allows you to roll back and see past versions. It ensures that no data is lost and users can also compare existing data with the old data.
4. Snapshot Isolation
Iceberg employs snapshot isolation, guaranteeing that concurrent reads and writes do not interfere with each other. This robust concurrency control mechanism ensures consistency and reliability in query results, even in highly concurrent data processing scenarios.
Apache Iceberg ensures that every read of the dataset produces an accurate snapshot. The last committed value that was present at the moment the read was done is essentially what is read. For example, suppose that Y commits its changes prior to X while both X and Y are simultaneously updating a record. A snapshot is made and added to the record’s metadata. Now, a check is made to determine if the update is utilizing the most recent snapshot (Y updates) after X finishes its update and it is prepared to be merged. There is no controversy as a result. When X and Y both commit, the record is changed, and a fresh snapshot is made to reflect the most recent change.
5. Metadata Management
Effective metadata management is essential in understanding and optimizing data utilization. Apache Iceberg maintains extensive metadata about the tables and data it manages. This metadata encompasses details about partitions, schema, and statistics, providing valuable insights into your data assets.
6. Performance Optimization
Performance optimization is a no-brainer for Iceberg. Iceberg incorporates features for optimizing query performance, including file pruning, predicate pushdown, and statistics collection. There is metadata for each file with every transaction that occurs, along with other relevant information for each file. This makes data scanning an easy, cost-effective and efficient task for users. Apache Iceberg directs users only on the file that matches the query and enhances query execution times.
7. Compatibility & Flexibility
Apache Iceberg is designed to be compatible with popular query engines such as Apache Spark, Apache Hive, and Presto. This compatibility ensures a seamless integration with your preferred tools and frameworks, allowing you to leverage the benefits of Iceberg’s features within your existing data ecosystem.
Iceberg also supports developers to choose from various file formats such as Hive, Arvo, ORC, Parquet, all thanks to its adaptable nature to embrace different table formats.
8. Open Source and Community-Driven
Being an open-source project, Apache Iceberg benefits from a vibrant and active community of contributors and users. This collective effort not only ensures the continuous improvement of the platform but also enables you to tailor Iceberg to meet your specific requirements and challenges.
When Not To Use Apache Iceberg
Undoubtedly, Iceberg is a chart-topping table format for managing massive data, there are instances when it might not be the best option for you. Here are a few of them.
When Having Small Datasets:
Iceberg is particularly developed for large datasets. If you’ve limited data that does not require a data lake, incorporating Iceberg would just be a waste of time, money and effort.
When Managing Real-time Data Needs:
If your business demands real-time data management and manipulation, then Iceberg is not an ideal choice for you. Apache Iceberg is more geared towards batch processing.
If your objective doesn’t involve distributed computing frameworks, Apache Iceberg may not align with your goals. It was purpose-built for distributed computing environments, where data is processed concurrently across multiple nodes.
In these scenarios, it’s worth considering more lightweight or specialized tools that better suit the specific needs of your data operations.
Apache Iceberg emerges as a robust and versatile solution for managing and querying data in the context of data lakes and storage systems. Its support for schema evolution, transactional capabilities, time travel queries, snapshot isolation, metadata management, performance optimization, compatibility with leading query engines, and active open-source community make it a compelling choice for organizations.
Businesses seeking to navigate the complexities of modern data management should try this reliable, flexible and cost-effective data management solution.
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