Iceberg Data Lakehouse: Revolutionizing Data Management

Imagine having a single platform that combines the best of data lakes and data warehouses. That's exactly what Iceberg Data Lakehouse offers, revolutionizing the way we manage data.

Iceberg Data Lakehouse is built on top of Apache Iceberg, an open-source project that provides a scalable and highly available data storage solution. By leveraging this technology, Iceberg Data Lakehouse enables organizations to store and manage large amounts of data in a unified and efficient way.

Data lakes and data warehouses have traditionally been separate entities, each with their own strengths and weaknesses. Data lakes are great for storing raw, unprocessed data, while data warehouses are optimized for querying and analyzing data. Iceberg Data Lakehouse bridges this gap by providing a single platform that can handle both use cases.

A Data Lakehouse is a combination of a Data Lake and a Data Warehouse, where companies store large amounts of data in a raw format, like Parquet or CSV files, and also have the ability to run SQL queries, batch jobs, and set up data governance schemes.

It's like having the best of both worlds – the flexibility of a Data Lake and the structure of a Data Warehouse.

Data Lakehouses use scalable storage, like HDFS or a Cloud Blob Store like S3, as the location for all the data.

They've also optimized their query engines to be very fast on these storage engines.

Data Lakehouses are basically the open version of a Data Warehouse, allowing different tools across vendors and platforms to access the data.

Some examples of Data Lakehouse products are Databricks and Dremio.

Companies like Snowflake and BigQuery are adding support for open data formats like Iceberg, making the line between a Data Warehouse and a Data Lakehouse more blurry.

This will be very interesting to observe over the years to come, as companies continue to adopt these new technologies.

Snowflake's Iceberg Tables rely on an architecture that uses the open table format Apache Iceberg as the table format and Apache Parquet as the data file format to store and process large datasets from a data lake.

The storage engine supports writing data with Apache Iceberg metadata and provides access to data through Snowflake's Iceberg catalog.

Data analysts can write the same queries they would normally write against Snowflake native tables, but now with Iceberg, Snowflake returns results after scanning files from object storage.

At Capital One, we created a data lake pipeline that is Iceberg-aware, meaning we generate Iceberg metadata on top of our data.

We use AWS Glue, which comes with native support for Iceberg tables, specifically for tracking Iceberg metadata.

Our lakehouse architecture ensures that Snowflake leverages the latest metadata.

Companies can store their data in a lakehouse architecture with consumption pointed to it, avoiding the costs of loading and storing data separately.

Snowflake’s Iceberg Tables provide a way to read and write data from external storage, such as a data lake, while enjoying the querying capabilities of Snowflake and the functionality of native Snowflake tables.

A data lakehouse architecture offers numerous benefits and advantages, making it an attractive solution for businesses. By combining the capabilities of a data lake and data warehouse, organizations can ingest data from a big repository of raw data into data lake storage while enabling structured data and transaction management features.

One of the key advantages of a data lakehouse is its ability to resolve issues found in the two-tier architecture, such as data redundancies and discrepancies, through a single system that captures all types of data in a low-cost store while employing data management features for governance, organization, and exploration.

Implementing Apache Iceberg can result in significant cost savings, with a 30% reduction in storage costs compared to storing data in regular Parquet format, and a 20% reduction in overall S3 costs.

Apache Iceberg also offers improved query performance, with optimized data management and indexing resulting in significant improvements in query performance and data retrieval times. This is particularly beneficial for organizations with large datasets, as it enables faster and more efficient data analysis.

Simplifying data updates is another advantage of using Apache Iceberg. With Iceberg, organizations can achieve this by running a few update commands, simplifying the process and reducing the need for complex batch jobs.

Data partitioning is also a key feature of Apache Iceberg, allowing for easier implementation of access policies on specific buckets and streamlining access management. This is particularly beneficial for organizations with sensitive data, as it enables more secure and controlled access to data.

Here are some of the key benefits of using Apache Iceberg:

Overall, a data lakehouse architecture and Apache Iceberg offer numerous benefits and advantages, making them attractive solutions for businesses looking to optimize their data management and analysis processes.

Implementing a lakehouse architecture using Iceberg Tables is a viable option for companies looking to leverage Snowflake's querying capabilities and native table functionality. Companies can store their data in a lakehouse architecture with consumption pointed to it, avoiding the costs of loading and storing data separately.

To transition to a lakehouse with Apache Iceberg, you'll need to configure storage, such as Amazon S3, to store raw and processed data. This involves setting up a scalable storage solution and defining the target buckets to store data and metadata.

The transition process involves several steps, including migrating existing Parquet data to Iceberg tables using Apache Spark, and ingesting data from Redshift into Iceberg format. This requires modifying the sink configuration for ETL jobs to write in Iceberg format.

Here's a summary of the steps to transition to a lakehouse with Apache Iceberg:

Partition evolution is a game-changer for large-scale data management.

This feature allows users to modify their partitioning scheme at any time without rewriting the entire table.

Partition evolution is unique to Iceberg and has significant implications, especially for petabyte-scale tables where altering partitioning can be complex and costly.

Altering partitioning in large-scale tables can be a complex and costly process, but partition evolution makes it much more manageable.

With partition evolution, users can easily revert any changes to the partitioning scheme by rolling back to a previous snapshot of the table.

This flexibility is a considerable advantage in managing large-scale data efficiently, and it's a key benefit of using Apache Iceberg.

Apache Iceberg's hidden partitioning feature simplifies workflows for data engineers and analysts.

In traditional partitioning approaches, data engineers often create additional partitioning columns, increasing storage requirements and complicating data ingestion.

This extra work can lead to errors and inefficiencies, making the process more prone to mistakes.

Data analysts must also be aware of these extra columns, or they'll risk full table scans, undermining efficiency.

With hidden partitioning, the system partitions tables based on the transformed value of a column, eliminating the need for physical partitioning columns in the data files.

This means analysts can apply filters directly on the original columns and still benefit from optimized performance.

The result is streamlined operations for both data engineers and analysts, making the process more efficient and less prone to error.

Transitioning to a lakehouse architecture can be a game-changer for companies looking to leverage the benefits of a unified data platform. To make this transition, you'll need to configure storage and install Iceberg.

Configure storage by setting up a scalable storage solution, such as Amazon S3, to store raw and processed data. Define the target buckets to store data and metadata.

To install and configure Iceberg, you'll need to add the necessary configurations while creating the Spark session. This will enable you to use Iceberg's features and functionality.

Here's a step-by-step guide to help you transition to a lakehouse architecture:

By following these steps, you'll be well on your way to implementing a lakehouse architecture using Iceberg Tables.

Getting started with a new data solution can be overwhelming, but it doesn't have to be. You can start querying your data in minutes without moving it.

Access to all your data is key, and with a free data lakehouse powered by Apache Iceberg, you can do just that.

Optimizing Iceberg Tables for performance is crucial to get the most out of your data lakehouse. By applying the same optimizations as Snowflake native tables, you can achieve near-native performance.

Data layout optimizations are key to performance. This includes file size, row group size, number of partitions, and compression method. These factors can make a significant difference in query performance.

Queries run faster when they can leverage indexes and statistics. These are collected in the metadata while writing the data, minimizing the number of files to open or scan.

Storage maintenance is essential to keep your data store running smoothly. This includes cleaning up older, unused snapshots and compacting files to the optimal size.

Caching can also be implemented within the lake storage layer. Precomputing the storage layout during ingestion can improve performance.

The Iceberg spec supports two patterns for performing row-level updates: copy-on-write and merge-on-read. Choosing the merge-on-read pattern can speed up workloads that are heavy on updates and deletes.

Here are some key areas to focus on for performance optimization:

Apache Iceberg's growing roster of vendors offers a diverse array of table management features, including compaction, sorting, and snapshot cleanup.

This variety gives users the flexibility to choose a vendor that best fits their needs, enhancing Iceberg's appeal as a versatile and user-friendly data management solution.

Versioning is an invaluable feature that makes it easy to isolate changes, roll back to previous versions, and experiment with new changes without affecting the main data.

Apache Iceberg uniquely incorporates branching, tagging, and merging into its core table format, which allows for more advanced versioning capabilities.

This means you can create separate branches for different projects or features, and then merge them back into the main branch when ready.

Apache Iceberg is the only format compatible with Nessie, an open-source project that takes versioning to the next level by including commits, branches, tags, and merges at the multi-table catalog level.

This unlocks a whole new world of possibilities for data management.

Other formats typically use file-level versioning, which requires using command-line interfaces and imperative programming for management, making them less approachable and more cumbersome to use.

Apache Iceberg's advanced versioning features are accessible through ergonomic SQL interfaces, making them user-friendly and easily integrated into data workflows.

Apache Iceberg's growing roster of vendors is making it easier to manage tables at scale. With multiple vendors offering various levels of table management features, users have the flexibility to choose the best fit for their needs.

This diversity of vendors includes Dremio, Tabular, Upsolver, AWS, and Snowflake, each providing unique features such as compaction, sorting, and snapshot cleanup. This variety of tools and services makes managing Iceberg tables as straightforward as using traditional databases or data warehouses.

With a single tool or vendor, data management can lead to vendor lock-in, but Iceberg's diverse array of vendors eliminates this risk. By choosing from a range of vendors, users can select the one that best suits their needs, enhancing Iceberg's appeal as a versatile and user-friendly data management solution.

As a result, managing Iceberg tables becomes more manageable, allowing users to focus on their core tasks rather than dealing with complex data management issues.

Apache Iceberg has some notable competitors in the data lakehouse space. Delta Lake and Apache Hudi are two key players that offer similar features to Iceberg.

All three formats provide core features such as ACID transactions, time-travel, and schema evolution. This makes them strong contenders for enabling database-like tables on your data lake.

Delta Lake and Apache Hudi are worth considering, but Apache Iceberg has some unique aspects that make it a noteworthy option.

Catalogs provide a shared definition of the dataset structure within data lake storage.

They do not coordinate data changes or schema evolution between applications in a transactionally consistent manner.

A large dataset with hundreds of thousands of files can be a challenge to manage, and catalogs don't define which data files are present and part of the dataset.

Applications must rely on reading file metadata in data lake storage to identify which files are part of a dataset at any given time.

As long as the dataset is static and doesn't change, different applications can operate on a consistent view of the dataset.

However, challenges are created when one application writes to and modifies the dataset, and those changes need to be coordinated with another application that reads from the same dataset.

Without automatic coordination of data changes between applications in the data lake, organizations need to create complicated pipelines or staging areas that can be brittle and difficult to manage manually.

Traditional catalogs like Hive Metastore and AWS Glue Data Catalog are widely used in the industry, but they have their limitations. They only describe a dataset's current schema without historical information or data changes with time travel.

In contrast, Iceberg describes the complete history of tables, including schema and data changes. This is a significant difference, as it allows for time travel and querying historical data to verify changes between updates.

Iceberg's open architecture enables all applications to directly operate on tables within data lake storage, unlike traditional catalogs which require all access to go through a single system. This increases flexibility and agility, and lowers costs by taking advantage of data lake architectures.

Here are some key differences between Iceberg and traditional catalogs:

Overall, Iceberg offers a more flexible and scalable solution for managing data in data lakes, with its open architecture and ability to track changes over time.

Iceberg is a format that stands out from the crowd due to its independence from governance and engine or tool lock-in.

One of the key advantages of Iceberg is that all applications have equal access, providing organizations with the flexibility to customize their data lake as needed.

This means that organizations can choose the best tools and engines for their specific needs, without being tied to a particular format.

Iceberg's performance optimizations, based on best practices, enable fast and cost-efficient access to data.

Fully storage-system agnostic, Iceberg has no file system dependencies, offering flexibility when choosing and migrating storage systems as required.

Multiple successful production deployments with tens of petabytes and millions of partitions demonstrate Iceberg's scalability and reliability.

Iceberg is 100% open source and independently governed, allowing for community-driven development and contribution.

This results in a format that is highly adaptable and responsive to the needs of its users.

If you're looking for alternatives to Iceberg, you've got a few options. Delta Lake, developed by Databricks, is one of the most popular alternatives. It's sponsored by Databricks and has some common functionality with Iceberg, but it's not fully open source.

Delta Lake and Iceberg have some key differences. Delta Lake is only available through Spark for write operations, whereas Iceberg can be updated by any engine. Hive ACID tables are another alternative, but they're fully open source and depend on the ORC file format.

Here's a comparison of the three:

Hive ACID tables and Delta Lake have been around longer than Iceberg, but Iceberg is quickly gaining adoption and additional features as more companies contribute to the format.