"Data Lakehouse" is one of the biggest buzzwords in our field, but what does it actually look like in production? If you ask five different companies what their lakehouse is, you'll likely get five different answers. The truth is, a lakehouse isn't a single product; it's an architectural concept.
This article aims to bring order to that chaos. We're going to move beyond the hype and establish a clear framework for understanding the landscape. I'm calling them the 6 Archetypes of the Modern Data Lakehouse—a set of the most common and powerful patterns defining the industry today. By the end, you'll have the language and mental model to make better architectural decisions for your own projects.
Let's dive in.
Pattern 1: The Cloud-Native Lakehouse (Most Popular)
This is the most popular pattern in the industry today. The core philosophy here is to go "all-in" with a single cloud provider's ecosystem, leveraging their suite of managed services for storage, compute, and governance. This approach prioritizes seamless integration and minimizes the operational burden on your team.
The Stack: The typical stack consists of Cloud Storage, Managed Compute, and a Unified Catalog.
AWS: S3 for storage, EMR or Glue for compute, Lake Formation for the catalog, and Athena or Redshift Spectrum for querying.
Azure: ADLS Gen2 for storage, Synapse Analytics for compute, and Purview for the catalog.
GCP: Cloud Storage, Dataproc or BigQuery for compute, and Dataplex as the catalog.
Best For: Teams that are deeply invested in a single cloud ecosystem (like AWS, Azure, or GCP) and want to get up and running quickly with services that are designed to work together out of the box.
Pattern 2: The Databricks-Centric Lakehouse (Very Popular in Enterprise)
This pattern is extremely popular in large enterprises and is built entirely around the Databricks ecosystem. Instead of mixing and matching services from a cloud provider, this approach consolidates the entire data and AI workflow into a single, unified platform. It's a powerful, all-in-one solution that often runs across multiple clouds.
The Stack: The architecture is defined by Databricks' core components.
Table Format: Delta Lake.
Compute: Databricks' Spark-based runtime environment.
Governance: Unity Catalog is used for governance.
Best For: Large organizations looking for a comprehensive, end-to-end platform for both data engineering and AI/ML. It’s particularly well-suited for teams that are already heavily invested in or have deep expertise with Apache Spark.
Pattern 3: The Open Source Lakehouse (Growing Rapidly)
For teams that prioritize flexibility and want to avoid vendor lock-in, the open-source approach is gaining momentum fast. This is a vendor-agnostic pattern where you assemble your lakehouse from the best-in-class open-source components, giving you maximum control and customization.
The Stack: The stack is defined by its modular, open-source components.
Table Format: Apache Iceberg or Hudi.
Compute: Apache Spark is often used for ETL, while Trino is used for fast, interactive queries. Modern implementations of this pattern are increasingly using powerful engines like Dremio for its semantic layer and DuckDB for blazing-fast, in-process analytics directly on the lake.
Governance: Tools like Apache Ranger are used for security and governance.
Best For: Teams with strong engineering talent who want to maintain control over their stack and have the freedom to swap components as technology evolves. It's a future-proof approach for those willing to manage the integration themselves.
Pattern 4: The Warehouse-Lake Hybrid (Traditional Enterprises)
This pattern is a practical, evolutionary step for established enterprises that already have a significant investment in a traditional cloud data warehouse. Instead of ripping and replacing, they extend their existing warehouse to query data that lives directly in a low-cost data lake. This creates a hybrid system that balances the performance of the warehouse with the flexibility and scale of the lake.
The Stack: This pattern pairs an existing warehouse with a lake extension. The general strategy is to keep "hot," frequently accessed data in the warehouse while using the lake for "cold" or raw data.
Examples:
Snowflake using external tables to query data in S3.
BigQuery using BigLake to interface with data in Google Cloud Storage.
Amazon Redshift using Redshift Spectrum.
Best For: Organizations that want to leverage their existing investment and skill sets in a cloud data warehouse (like Snowflake, BigQuery, or Redshift) while gradually incorporating the cost and scale benefits of a data lake.
Pattern 5: The Real-Time Lakehouse (Emerging)
This is a powerful and emerging pattern designed to handle streaming data at scale. Instead of relying on nightly batch jobs, this architecture integrates streaming sources directly into the lakehouse, enabling real-time analytics and dashboards on the freshest data possible.
The Stack: This pattern is built for speed and continuous data ingestion.
It integrates streaming sources like Kafka or Kinesis with lakehouse table formats such as Delta Lake or Iceberg. Alongside these, modern streaming platforms like Redpanda (a Kafka-compatible alternative) and lightweight solutions like Redis Streams are also popular choices for data ingestion.
It often involves Change Data Capture (CDC) integration.
Stream processing is handled by engines like Spark Streaming or Flink.
Best For: Use cases that require up-to-the-second data, such as fraud detection, IoT analytics, and live operational dashboards where data latency is a critical factor.
Pattern 6: The ML-Focused Lakehouse (Specialized)
This is a specialized pattern that tightly integrates data management with the machine learning lifecycle (MLOps). The primary goal is to create a unified platform for both data and models, streamlining the end-to-end workflow. Crucially, this stack must be able to detect model drift as new data comes in and provide the capability to retrain models on massive amounts of historical data as patterns change, ensuring that models in production remain accurate and relevant.
The Stack: This architecture brings data and ML tooling together.
It often includes a feature store built on Delta Lake or Iceberg for feature storage.
It integrates with ML platforms like MLflow or Kubeflow for managing the end-to-end ML lifecycle.
It supports both real-time and batch feature serving.
Best For: Companies where machine learning is a core business driver and who need to shorten the cycle time from data preparation to model deployment.
Conclusion: Choose Your Path
The "data lakehouse" is not a monolithic product but a powerful set of architectural patterns. By understanding these six core archetypes—from the provider-led Cloud-Native approach to the flexible Open Source model—you now have a framework to analyze your own needs and make better decisions. The right pattern for you depends on your team's skills, your company's existing technology investments, and your ultimate business goals.
The one constant is change. As the tools and trends continue to evolve, I'll be here to help you navigate them.
Which of these patterns are you using today? What are the trade-offs you've seen? Share your experience in the comments.
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