Data Warehouse vs Lake vs Lakehouse: An AI/ML Perspective

The world of data is rapidly evolving. Enterprises are dealing with an explosion of big data while facing pressure to extract more value with AI and machine learning. This has major implications for data architectures.

Traditionally, data warehouses powered BI and reporting on structured data. But the rise of unstructured data gave birth to data lakes. Now a new paradigm called the lakehouse aims to unify them. As an AI/ML expert, which approach should you build on – a data warehouse, data lake, or lakehouse?

In this in-depth guide, we‘ll compare these architectures and provide a framework for choosing the right one for your AI/ML needs. We‘ll dive deep into the technical differences, weigh the pros and cons, and share the latest research and insights. By the end, you‘ll be equipped to make the right decision for your use case.

Data Warehouses: Structured Data Powerhouses Falling Short for ML

Data warehouses are purpose-built databases optimized for SQL analytics on structured data. They‘ve been the foundation of BI for decades, but face challenges in the age of AI. Let‘s explore why.

How Data Warehouses Work

Data warehouses employ a few key techniques to achieve fast SQL performance on large volumes of data:

• Denormalized schemas that pre-join data for specific queries
• Columnar storage that drastically speeds up reads
• Indexes and partitions that allow skipping irrelevant data
• Query optimizers that choose the most efficient plan

On top of this, data warehouses provide ACID transactions. This guarantees reliable, consistent results for concurrent workloads. Leading cloud data warehouses like Snowflake can automatically scale compute and cache data for maximum performance.

Data Warehouses Falling Short for AI/ML

However, data warehouses are not built with AI/ML use cases in mind. Some key limitations include:

• Unstructured data: warehouses only support structured, tabular data. But most data science and ML requires semi-structured and unstructured data like text, images, logs, etc.
• Real-time/streaming: most data warehouses (except Snowflake) can‘t natively ingest streaming data. ML applications often require real-time features and model scoring.
• Rigid schemas: warehouse schemas are defined upfront for specific queries. This makes it hard to evolve data pipelines or store raw data that doesn‘t fit the schema.
• Expensive storage: highly-structured warehouse storage is often much more expensive per TB than object storage used by data lakes.

Due to these limitations, only 28% of enterprises use data warehouses for ML and data science use cases according to a 2022 Forrester survey. The other 72% rely on data lakes and lakehouses. Let‘s examine why.

Data Lakes: Flexible Big Data Platform for AI/ML

Data lakes emerged to address the challenges posed by massive volumes and varieties of big data. By decoupling storage and compute on cheap object storage, lakes offer major benefits for AI/ML.

How Data Lakes Work

In contrast to data warehouses, data lakes:

• Store data in raw, open file formats rather than proprietary databases
• Decouple storage and compute, scaling them independently
• Apply schema-on-read, allowing structure to be defined at usage time
• Leverage distributed processing engines like Spark to query data

Modern cloud data lakes typically use services like AWS S3 or Azure Data Lake Storage (ADLS) for storage. Spark, Presto, or serverless query engines then process data in an elastic fashion.

Data Lakes Enabling Large-Scale AI/ML

Data lakes have become a foundational component of the modern AI/ML stack. Some key advantages include:

• Store and process unstructured data used in computer vision, NLP, etc.
• Ingest streaming data in real-time for ML model features
• Flexibly explore data and iterate on features without upfront modeling
• Scaling to petabyte-scale data sets needed for deep learning
• Leveraging data sharing and third-party data for ML

According to the Forrester survey, 46% of enterprises now use data lakes as their primary platform for data science and ML. Lakes provide the massive data volumes, variety, and agility needed for AI.

Challenges with Data Lakes for AI/ML

However, data lakes still face significant challenges for operationalizing AI/ML:

• Data quality and consistency issues, lack of ACID transactions
• Processing data at usage-time is expensive, leading to stale models
• Difficult to serve ML models and features in real-time
• Lack of semantic layer and governance turns lakes into swamps
• Multiple copies and pipeline complexity when combined with warehouses

These factors have provided an opening for a new paradigm – lakehouses – to address the challenges of both data warehouses and lakes.

Lakehouses: Converging Storage and Compute for AI

Lakehouses aim to combine the key benefits of data warehouses (data management, quality, ACID) with the flexibility, scale, and low-cost storage of data lakes. They provide a single platform to power all analytics workloads, including BI, data science, and machine learning.

How Lakehouses Work

Lakehouses implement a new table format and processing layer directly on top of data lakes. For example, Databricks‘ open-source Delta Lake uses Parquet files in object storage. Delta adds several key warehouse-like features:

• ACID transactions for multiple concurrent reads and writes
• Schema validation and schema evolution
• Time travel for querying historical data
• Indexing and statistics for performance optimization

This allows large-scale data ingestion and batch processing like a data lake. But it also provides key capabilities to power BI and data science workloads with high-quality, fresh data.

Simplifying and Accelerating the AI/ML Lifecycle

Lakehouses offer major benefits for the end-to-end AI/ML lifecycle by:

• Providing a single source of truth, avoiding data drift between lake and warehouse
• Enforcing data quality through schemas and constraints
• Enabling ML model training and serving on the same data store
• Powering real-time features and model inference
• Supporting feature stores and ML metadata management

According to Databricks, organizations that standardized on the lakehouse are 9 times more likely to have fully deployed ML applications in production. Adopting an integrated lakehouse platform accelerated deployment of ML use cases by 6-12 months.

Enterprise Adoption and Ecosystem Growth

While lakehouses are a relatively new paradigm, adoption is growing rapidly. According to Databricks:

• More than 5,000 organizations now use the lakehouse architecture in production
• Data processed on lakehouses surpassed 100 exabytes in 2021

Startups like Dremio and Starburst have raised hundreds of millions to further evolve the lakehouse paradigm. And all the major cloud providers have now embraced the architecture. AWS, Databricks, Google, and Microsoft now offer lakehouse solutions like Delta Lake on Databricks and Azure Synapse Analytics.

How to Choose the Right Architecture for Your AI/ML Needs

So should you use a data warehouse, lake, or lakehouse for your AI/ML use cases? Here‘s a simple decision tree based on key criteria:

graph TD
    A[Data types?] --> B{Mostly structured}
    A --> C{Mostly unstructured} 
    B --> D{Workloads?}
    C --> H{Workloads?}
    D --> E{BI/reporting} 
    D --> F{Data science/ML}
    H --> I{Traditional data science}
    H --> J{Deep learning/advanced analytics}
    E --> G[Data warehouse]
    F --> K{New data platform?}  
    I --> L[Data lake]  
    J --> M[Lakehouse]
    K --> |Yes| M
    K --> |No| N[Lake + Warehouse] 

Here are the key decision criteria in more detail:

1. Data types: If you primarily have structured, tabular data, a data warehouse may suffice. But if you have lots of unstructured data (text, images, video, etc.), you‘ll need a data lake or lakehouse.

2. Workloads: For traditional BI and reporting, a data warehouse is purpose-built and still the best choice. For data science and ML, a data lake or lakehouse is preferred.

3. Implementation: If you‘re building a new, greenfield data platform, a lakehouse provides the most flexibility and future-proofing. If you have existing investments in both a data warehouse and lake, you may need to combine them.

4. Ecosystem and skills: Also consider your team‘s skills and existing tool ecosystem. If you‘re heavily invested in a data warehouse and don‘t need advanced ML capabilities, adding a data lake may suffice.

Real-World Case Studies

Finally, let‘s look at a couple real-world architectures and their rationale.

Combining a Data Lake and Warehouse at Netflix

Netflix uses a combination of data warehouse and data lake to power analytics across the company. Specifically:

• Structured data is stored in Redshift and Snowflake warehouses. This powers financial reporting, marketing campaign measurement, and product analytics.
• The S3-based data lake stores raw customer interaction events and semi-structured data. This feeds into Netflix‘s famous recommendation and personalization models using Spark on EMR.

Unifying BI and ML on Lakehouses at Uber

Uber adopted Delta Lake on Databricks to simplify its data architecture and accelerate AI use cases. Previously:

• Uber had separate Hadoop data lake and Vertica data warehouse. This led to data discrepancies, pipeline sprawl, and long lead times for new projects.
• By moving to Delta Lake, Uber unified its data warehousing and data science/ML use cases on a single platform.

This enabled new applications like ML-powered forecasting and anomaly detection. Data scientists now build and deploy models up to 10x faster.

Conclusion

In summary, choosing between a data warehouse, data lake, and lakehouse depends on your data, workloads, maturity, and goals:

• Data warehouses excel at SQL analytics on structured data. But they are not built for data science or advanced AI/ML.
• Data lakes provide flexible, cost-effective storage for big data and AI/ML. But they lack data management capabilities critical for many use cases.
• Lakehouses are a promising new paradigm to power all analytics workloads. They are a great fit for AI-driven organizations building new data platforms.

Ultimately, an AI-powered future will require data architectures that can handle the scale, complexity, and real-time demands of ML. For many, the lakehouse promises to be that architecture.

But don‘t just take my word for it. Carefully evaluate your specific requirements and use the decision framework provided. With the right data foundation and an agile approach, you‘ll be on your way to AI success.