Optimizing Data Warehouse Cost using Apache Iceberg

Data warehouses bring phenomenal results from well-informed, data-driven decision-making for an organization. There were times when only companies with large capital, and substantial IT infrastructures invested time and effort, let alone money, in a data warehouse. 

But what if I say that it is now convenient even for small startups to gear up and start building data warehouses for their organization today? The advent of technologies like Apache Iceberg has made it possible for small businesses and organizations to build a warehouse-like, feature-rich data infrastructure to drive their business decisions based on data and statistics. 

The world is moving towards a modern approach to building a data architecture, The Lakehouse, and the core of a lakehouse architecture is technologies like Apache Iceberg. 

Sounds cool, Right?

Let’s explore how we can optimize data warehouse costs with Apache Iceberg!

Data Warehouse Vs Data Lakehouse

Let us start by looking into the differences between these two data architectures:

Aspect	Data Warehouse	Data Lakehouse
Data Types Support	They primarily store structured data.	They support structured, semi-structured as well as unstructured data.
Data Processing	Best suited for Batch processing and ETL Workloads	Supports both batch as well as real-time data processing. (ETL & ELT)
Data Schema	Data stores in a warehouse require a predefined schema before writing the data also known as schema-on-write	Lakehouses offer schema flexibility with schema-on-read (schema is evaluated at the time of reading the data)
Architecture	Storage & computing layers coupled together.	The storage and computing layer is decoupled to scale independently as per need.
Technology	Traditional relational databases (Eg. SQL-based databases)	Modern technologies and platforms like Apache Iceberg, Delta Lake, etc.

Why are Data Warehouses Expensive?

Whether traditional monolithic data warehouse tools or modern-day cloud data warehouse technologies that businesses use today, they tend to become unsustainable over time in terms of development, operation, maintenance, and cost.

The pricing model of some modern data warehousing services has become almost un-economical serving the needs of modern data requirements and access patterns. Many organizations realize this when it’s too late. Some of the responsible factors  are:

1. Infrastructure Cost:

Data warehouses are powered by sophisticated computing, storage, and networking infrastructure that are costly to use. As the size of data in the warehouse increases, so does the cost of storing, processing, and consuming. On top of that, once we think of replicating our warehouse for the sake of fault tolerance and disaster recovery, the cost it incurs skyrockets. 

2. Ever-changing data needs with ETL/ELT

Warehouses are primarily built to serve structured data for peculiar business intelligence needs. However, with the ever-changing data requirements of an organization, engineers use multiple ETL solutions to keep the data available in the warehouse relevant. This adds to the operational and maintenance costs of a warehouse.

3. Vendor Lock-in and Licensing

Using any commercial technology or a proprietary data warehousing solution can lead to either an expensive Licensing Fee and/or a Vendor Lock-in situation. This can lead to dependency on vendors for software updates, integration compatibility, limited flexibility, and finally increased cost while transitioning to an alternative solution.

4. Support & Maintenance

Different cloud warehousing solutions have different subscription models for technical and maintenance support.

Open Table Format to Rescue

Wouldn’t it be amazingly efficient if we could just store data in some inexpensive object storage solutions as flat files (CSVs, JSONs, etc.) and be able to run SQL-like queries on top to analyze our data? Open Table Format (OTFs) does exactly that.

Let us now discuss, how OTFs like Apache Iceberg make our warehousing solutions efficient and scalable at the same time.

1. Storage Efficiency

OTFs like Apache Iceberg use columnar data formats to efficiently store data as parquet files. On top of that, the use of efficient data compression techniques minimizes the storage footprint of the lakehouse. This plays a crucial role in cutting down a majority of the cost when storing exabytes scale of data in a lakehouse. Apache Iceberg has been statistically proven to store compressed data without any significant impact on the query performance.

2. Decoupled Architecture

The storage and compute layers of the lakehouse are independent of each other and can be scaled independently as per the need. This gives flexibility in resource planning and cuts costs wherever possible.

3. Efficient Incremental Updates

While some data warehousing solutions require expensive data merge operations and total table rebuild in some cases to update the data in the warehouse. Apache Iceberg supports swiftly efficient incremental updates.

4. Schema Evolution

Apache Iceberg supports in-place table evolution. It doesn’t require any rewriting of table data or migrating to a new table whatsoever. We can evolve tables even in nested structures or alter the partitioning of a table when data volume switches its direction.

5. Operational Efficiency

OTFs like Apache Iceberg have evolved in their core to optimize read and write operations. Optimized metadata management adds to efficient transactions and lowers overhead compared to proprietary solutions.

6. Ecosystem, Integration, and Compatibility

Apache Iceberg is supported by a wide range of tools and platforms, from processing engines like Spark, and Flink to query engines like Trino, and Presto. This promotes interoperability and avoids vendor lock-in. Organizations can save costs by leveraging existing tools throughout their data ecosystem.

7. Scalability with Performance

Iceberg is designed with scalability and performance in mind. Query performance on iceberg tables remains unimpacted by a proportionate increase in the volume of data stored underneath the tables. Unlike traditional data warehouses which may face concurrency challenges along with query optimization costs.

8. Community Community Community

Finally, Apache Iceberg is an Open Source technology that benefits from contributions and continuous improvement from a diverse community of developers and organizations. Unlike proprietary solutions that are impacted by the direction of a single organization, open-source technologies like Iceberg evolve to meet new challenges and thus we can look forward to saving more costs over time.

Industry Adoption and Results for Optimizing Data Warehouse Costs

Data Lakehouse is an emerging data architecture and has been dirt-tested for several real-world use cases with Apache Iceberg at its core. Let us now explore how different organizations have leveraged the Apache Iceberg and the fruits they have reaped out of it.

1. Netflix

Netflix is one of the early adopters of Apache Iceberg and has contributed enormously to its development and innovations. Let us discuss what were the challenges and solutions around the adoption of Iceberg for Netflix

• Challenge: 

It is in this context, that before Iceberg, Netflix utilized Apache Hive Tables. Their need was to fix the escalating challenges in analyzing fast-developing data and to enable agile incremental handling while ensuring consistency in their data.

• Iceberg Solution: 

Netflix managed to transition its core to a data architecture powered entirely by Apache Iceberg. They used extensively the expressive SQL capability of Iceberg while performing data operations like updates, merges, and targeted deletes coherently. This transition has enhanced Netflix’s capabilities in a broader data analytics arena.

2. Adobe

Adobe customers heavily use the Adobe Experience Platform to centralize and standardize data across the enterprise.

• Challenge:

Adobe handles petabytes of data on the Adobe Experience Platform. And, data consistency on its analytics pipeline with schema evolution has been a critical challenge it has been struggling with.

• Iceberg Solution: 

Apache Iceberg’s schema evolution and time travel feature have provided a robust solution to Adobe’s data challenges. Adobe makes use of the hidden partitioning feature of Iceberg tables to optimize their query performance along with its centralized metadata management framework to maintain consistency in its data across multiple data and analytics applications/platforms.

3. Bloomberg

• Challenges:

Before modernizing its data architecture with Apache Iceberg, Bloomberg had massive pain in the consistency of data and metadata management. Other key concerns were performance with huge amounts of data and changes to the schema across data applications.

• Iceberg Solution:

Bloomberg had a varied set of features to be used in Apache Iceberg for consistency in data, performance optimization, and scalable metadata management. Apache Iceberg also resolved the challenge of schema maintenance, as it natively supported schema evolution. Further, Iceberg’s distributed metadata management approach reduced latency and improved query responsiveness, solving Bloomberg’s performance challenges.

Iceberg’s Best Practices and Desing Patterns

Designing a lakehouse architecture can be challenging and cumbersome. Making the most out of what Apache Iceberg has to offer with balanced cost and performance is another race to win. Following industry-approved and widely accepted best practices and design patterns can save us from the whirlwind of architectural mesh. Following are some of the best practices one can follow while making design decisions:

1. Bucketing and Partitioning

We run multiple complex queries to power downstream data and analytics applications. Efficient partitioning of data that aligns with common data query patterns can reduce scan time and storage usage. Iceberg’s hidden partitioning can further enhance query performance and thus cut costs.

2. Data Lifecycle Management

Having a data lifecycle in place makes it convenient to manage the storage footprint of iceberg tables. We can define data retention policies to automatically prune any unwanted data and free storage space. On the other hand, the iceberg’s point-in-time recovery can be used for auditing and compliance purposes without having redundant copies of data across the lakehouse.

3. Efficient Query Planning and Execution

Always enable predicate pushdown. This will filter the data at an early stage, reducing the amount of data to be scanned and processed in the downstream stages of query execution. We can also use vectorized query engines like Apache Arrow with junction to Iceberg for efficiency and reduced compute costs.

4. Governance

Governance is the master key to successful lakehouse architecture. It ensures data quality and compliance requirements are met to avoid costly data issues down the lane.

Learn More About:

Iceberg Architecture

Conclusion

Data warehouses tend to be costly, so organizations must adopt modern data architectures like Data Lakehouse. Open table format (OTFs) like Apache Iceberg are the heart of such architectures, which are critical to operational efficiency while optimizing cost with consistent performance.

Iceberg’s out-of-the-box features, such as hidden partitioning, schema evolution, efficient incremental data updates, and streamlined data management, reduce the Lakehouse’s operational and maintenance costs. 

Moreover, it is integrable, and its open-source and community-driven development model brings a very rapid rate of innovation without the shackles of vendor lock-in. Ultimately, Iceberg helps an organization scale, perform, and be agile while managing data warehouse environments at lower costs, hence remaining one of the most pivotal choices in modern data architecture strategies.

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