The Rise of Data Lakehouses: A Deep Dive

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If you‘ve been following the data management space, you‘ve likely heard the buzz around "data lakehouses". But what exactly are lakehouses, and why are they gaining so much traction? In this comprehensive guide, we‘ll go deep on lakehouse architecture, benefits, and implementation best practices.

The Evolution of Data Platforms

To understand why lakehouses are gaining popularity, we first need to understand how data platforms have evolved:

Data Warehouses were the traditional standard for analytics. They allowed for SQL querying and BI on structured data. But warehouses required rigid schemas, were expensive, and inefficient for unstructured data.

Hadoop Data Lakes emerged to cost-effectively store all raw data, without schemas. But lack of governance and performance for analytics use cases were issues.

Data Lakehouses aim to deliver the best of both architectures – scalable storage, governance and SQL support for all data types.

The rise of lakehouses is being driven by data volume growing at over 50% CAGR. Unstructured data like JSON, images, video and audio are exploding. Traditional data warehouses can‘t keep up economically.

According to Gartner, 75% of new enterprise data lakes will be migrated to data lakehouses by 2025.

Detailed Lakehouse Architecture

Under the hood, what sets lakehouses apart is how they separate storage and computing:

Lakehouse Architecture

Storage Layer – Distributed object storage like S3, ADLS for unstructured data at scale.

Processing Layer – Fault-tolerant engines like Spark, Flink or Dremio for ETL and distributed SQL.

Metadata Catalog – Tracks schema lineage, data discovery and governance.

Serving Layer – Indexes and caches data for fast SQL at petabyte scale.

Orchestration – Workflow engines like Airflow for managing ETL pipelines.

This separation of storage and compute enables independent scaling and cost optimization. The storage layer can scale affordably with data growth. The compute layer provides elastic resources for processing.

Real-World Use Cases

Here are a few examples of how leading companies are using lakehouses:

  • Snap built a 50+ petabyte lakehouse on Google Cloud to power advertising analytics and ML at scale.

  • Fannie Mae migrated 4 petabytes of mortgage data from Teradata and Oracle to a Snowflake lakehouse. This reduced costs by 70%.

  • Ritual uses a Databricks lakehouse to analyze 200 billion food ordering events per year and deliver personalized recommendations.

  • Netflix built a lakehouse architecture on S3 and Presto to run analytics on their massive datasets around movies, users and viewing activity.

The ability to handle huge volumes of data cost-effectively while enabling analytics, ML and SQL access is driving rapid lakehouse adoption.

Leading Lakehouse Technologies

While you can build a lakehouse using standard open source tools, several commercial offerings are emerging:

Delta Lake brings ACID transactions, caching and indexing to data lakes on Spark. Created at Databricks.

Snowflake added support for semi-structured data, making their cloud data warehouse a lakehouse.

Iceberg is an open table format that adds performance and governance on top of cloud object stores.

Apache Hudi enables incremental processing and low-latency queries on data lakes. Created at Uber.

Dremio can turn object storage like S3 into a fast lakehouse enabling live SQL and BI directly on data lakes.

Each technology brings its own strengths – you can evaluate them against your specific data architecture needs.

Best Practices for Lakehouse Implementation

Here are some key best practices I‘ve found critical for successful lakehouse adoption:

  • Start with an MVP focused on a high value use case like customer 360 or real-time analytics. Learn and then expand.

  • Invest in data governance early – schema management, security, access controls and auditing. This avoids "data swamp" issues.

  • Modernize ETL processes using workflow orchestration and incremental algorithms. This enables near real-time insights.

  • Don‘t eject your data warehouse prematurely. Run the lakehouse in parallel until stability and performance is validated.

  • Embrace agile delivery and fail fast mindset. Be ready to iterate and improve the architecture as you scale.

  • Involve data engineers, analysts and IT early and often. Cross-functional collaboration is key.

With careful architecture, governance and an agile approach, lakehouses can deliver immense value as the foundational data platform.

The Future is Lakehouses

It‘s clear that data lakehouses represent the future of modern data management. They offer the cost-efficiency of lakes combined with the performance, governance and reliability expected from a warehouse.

For organizations looking to leverage data for competitive advantage, lakehouses will soon become the de facto standard. The most innovative companies are already adopting lakehouse architectures to power their analytics and AI initiatives.

I hope this guide gave you a comprehensive overview of lakehouse capabilities and implementation considerations. Let me know if you have any other questions! I‘m excited to see lakehouses unlock new possibilities for data-driven organizations.


Written by Alexis Kestler

A female web designer and programmer - Now is a 36-year IT professional with over 15 years of experience living in NorCal. I enjoy keeping my feet wet in the world of technology through reading, working, and researching topics that pique my interest.