Data Lakehouse

What it is, key features, and benefits. This guide defines and compares data lakehouses, data lakes and data warehouses to help you evaluate which structure is best for your organization.

What is a Data Lakehouse?

A data lakehouse is a data management architecture that combines key capabilities of data lakes and data warehouses. It brings the benefits of a data lake, such as low storage cost and broad data access, and the benefits of a data warehouse, such as data structures and management features.

Data Lakehouse Features and Benefits

The lakehouse data platform ensures that data analysts and data scientists can apply the full and most recent data set toward business intelligence, big data analytics and machine learning. And having one system to manage simplifies the enterprise data infrastructure and allows analysts and scientists to work more efficiently.

Here we present the key features of data lakehouses and the benefits they bring to your organization.



Concurrent read & write transactions
Data lakehouses can handle multiple data pipelines.

Multiple users can concurrently read and write ACID-compliant transactions without compromising data integrity.

Data warehouse schema architectures
Data lakehouses can apply a schema to all data.

This means lakehouses can standardize large datasets.

Governance mechanisms
Lakehouses can support strong governance and auditing capabilities.

Having a single control point lets you better control publishing, sharing and user access to data.

Open & standardized storage formats
Lakeshouses use open, standardized storage formats such as AVRO, ORC or Parquet. They additionally support tabular formats too.

Open formats facilitate broad, flexible and efficient data consumption from BI tools to programming languages such as Python and R. Many also support SQL.

Separation of storage & processing
Like some modern data warehouses, lakehouses decouple storage and compute resources by using separate clusters for storing and processing.

You can scale to larger datasets and have more concurrent users. Plus, these clusters run on inexpensive hardware, which saves you money.

Support for diverse data types
Data lakehouses give you access to structured, semi-structured and unstructured data types.

This allows you to store, access, refine and analyze a broad range of data types and applications, such as IoT data, text, images, audio, video, system logs and relational data.

Support for end-to-end streaming
Data lakehouses support data streaming.

This enables real-time reporting and analysis. Plus, you no longer need separate systems dedicated to serving real-time data apps.

Single repository for many applications
Lakehouses allow you to access data for machine learning, data engineering, data science, SQL, and advanced analytics directly on a single repository of clean, integrated source data.

This improves operational efficiency and data quality for business intelligence, ML and other workloads since you only have to maintain one data repository.

3 Ways to Increase Your Data Lake ROI

Data Lakehouse vs Data Warehouse vs Data Lake

Historically, you’ve had two primary options for a big data repository: data lake or data warehouse. To support BI, data science and machine learning, it’s likely that you’ve had to maintain both of these structures simultaneously and link the systems together. This often leads to data duplication, security challenges and additional infrastructure expense. Data lakehouses can overcome these issues.

Data Warehouse

Data Lake

Data Lakehouse

Data Lakehouse Architecture

A lakehouse typically consists of 5 layers: the ingestion layer, storage layer, metadata layer, API layer, and consumption layer. There are four main approaches to developing this architectural pattern. Your choice depends on your specific use case, requirements, and existing ecosystem. Evaluate the trade-offs between features, ease of use, and performance between these approaches:

  1. Delta Lake: This open-source storage layer is the most popular approach today. It leverages ACID (Atomicity, Consistency, Isolation, Durability) compliance from transactional databases to enhance reliability, performance, and flexibility in data lakes. Delta Lake is particularly useful for scenarios requiring transactional capabilities and schema enforcement within your data lake. Notably, it offers features such as scalable metadata handling, data versioning, and schema enforcement for large-scale datasets, ensuring data quality and reliability for advanced analytics and data science tasks.

    As you can see, a Delta Lake uses APIs to integrate with your existing storage systems such as AWS S3 and Azure, compute engines including Spark and Hive, and APIs for Scala, Java, Rust, Ruby, and Python to make streaming and batch data available for analytics and machine learning.

  2. Parquet and Apache Spark: You can use Apache Parquet as the storage format for your data lakehouse. Parquet is columnar storage optimized for analytics workloads. Combine it with Apache Spark for data processing, transformations, and querying. Although it lacks some features of Delta Lake (such as ACID transactions), it’s a lightweight option for certain use cases.

  3. Hudi (Apache Hudi): Apache Hudi (Hadoop Upserts Deletes and Incrementals) is another open-source project that provides features similar to Delta Lake. It offers data versioning, schema evolution, and efficient upserts. You can use Hudi as an alternative to Delta Lake for managing data in your lakehouse.

  4. Custom Solutions: If you have specific requirements, you can build custom solutions using a combination of storage formats (like Parquet or ORC), metadata management tools, and orchestration frameworks. However, this approach requires more development effort and maintenance.

Cloud Data Lake Comparison Guide

Get an unbiased, side-by-side look at all the major cloud data lake vendors, including AWS, Azure, Google, Cloudera, Databricks, and Snowflake.

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