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Data Lakehouse vs. Data Warehouse: Which is Better

Data warehouses store data in their proprietary formats -- the workload you can run on that piece of data is limited by the data warehouse's capabilities. With data stored in open formats and an open catalog service, data lakehouses integrate with a wide range of computing engines, allowing you to maintain a single source of truth for all your data.

 

Explore Lakehouse Architectures
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Why an Open Data Lakehouse?

Enterprises worldwide are embracing the open data lakehouse concept, but why? Here are the key reasons you should consider an open approach to your lakehouse.

Improved Data Governance

Storing data in open formats allows us to unify all workloads on a single source of truth, which is great for data governance.

Increased Flexibility

Having complete ownership of your data with the ability to frictionlessly switch between compute engines.

Optimal Cost-Efficiency

Enjoy the best cost-efficiency by combining the industry's best technologies for each component of your data stack.

Data Lakehouse vs. Data Warehouse: Which is Better

Data warehouses store data in their proprietary formats -- the workload you can run on that piece of data is limited by the data warehouse's capabilities. With data stored in open formats and an open catalog service, data lakehouses integrates with a wide range of computing engines, allowing you to maintain a single source of truth for all your data.

 

Data Lakehouses offer better data freshness than traditional data lakes. However, for real-time analytics, real-time data warehouses still have specialized tools that offer better data freshness than data lakehouse systems. Read more here.

 

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Popular Lakehouse Table Formats

Open table formats are the heart of every lakehouse architecture, providing standardized formats as well as data warehouse-like capabilities.
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Apache Iceberg

Apache Iceberg is a high-performance format for huge analytic tables.

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Apache Hudi

Apache Hudi is a transactional data lake platform that brings database and data warehouse capabilities to the data lake.

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Delta Lake

Delta Lake is an open-source storage framework that enables building a format agnostic Lakehouse architecture

Key Lakehouse Features

No matter how you approach the data lakehouse, you'll want to pay special attention to all solutions and architectures in regards to how they support, provide, or interact with the following capabilities.

ACID Compliance

Ensures data integrity by supporting Atomicity, Consistency, Isolation, and Durability in transactions.

Compaction

Optimizes storage by periodically merging small files into larger ones, improving query performance.

Near-Real-Time Analytics

Enables fast data processing and querying, providing insights almost instantly after data ingestion.

Schema Evolution

Allows the schema to adapt dynamically to changes in data structure without downtime.

Lakehouse Limitations

Data lakehouses promise flexibility, scalability, and cost-effectiveness but often fail to deliver these benefits due to slow query performance. This has forced users to copy their data from the lakehouse into proprietary data warehouses to achieve their desired query performance—through a complex, costly ingestion pipeline that undermines data governance and freshness.

 

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Why Query Engines Matter for Lakehouses

Maximize your lakehouse's potential by choosing the right query engine for each task. With open formats, you can layer multiple engines over the same data, each tailored for specific purposes. Lakehouse engines excel at specialized tasks—like using Spark for batch processing and StarRocks for low-latency queries.

 

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The Optimal Lakehouse Architecture

Architectures abound when it comes to lakehouse analytics. While the optimal architecture depends on your specific use case and business priorities, most approaches should prioritize the following capabilities.

Catalog Service

Utilize catalog services with an open source variant to ensure seamless interoperabality across different table formats. This approach enhances flexibility and makes it easier to manage and access data across your lakehouse architecture.

 

 

Compute Engine

Select the most suitable compute engine for each specific task to optimize performance. In the lakehouse architecture, switching between different compute engines is effortless, allowing you to adapt quickly to changing requirements.

Table and File Format

Adopt open table formats like Apache Iceberg, which integrates with open file formats. This ensures compatibility and scalability, allowing your lakehouse to grow and evolve without locking you into proprietary solutions.