Published on 2025-06-28T04:23:47Z

What is a Data Lakehouse? Examples for Data Lakehouse.

A Data Lakehouse is a modern data architecture that combines the flexible storage capabilities of a data lake with the structured management and query performance of a data warehouse. It supports storing raw, semi-structured, and structured data in open formats while providing ACID transactions, schema enforcement, and fast SQL analytics. Unlike traditional data warehouses, a lakehouse can handle large volumes of untransformed data without sacrificing data governance or performance. This architecture enables real-time analytics, machine learning workflows, and BI reporting on a single platform. For example, you can stream GA4 events to BigQuery or route Plainsignal’s real-time tracking events into your lakehouse storage for comprehensive analysis.

Illustration of Data lakehouse
Illustration of Data lakehouse

Data lakehouse

A Data Lakehouse unifies data lake storage flexibility with data warehouse querying and management in a single architecture.

Overview of Data Lakehouse

This section introduces the core concept of a Data Lakehouse, highlighting its role as a unified platform for managing diverse data types with robust query capabilities.

  • What is a data lakehouse?

    A Data Lakehouse merges the scalable, low-cost storage of data lakes with the transaction support and performance optimizations of data warehouses in a single system.

  • Core components

    Key components include a storage layer for raw and structured data, a metadata and transaction management layer, and an analytics engine for SQL queries.

    • Storage layer:

      Object storage or distributed file systems store raw, semi-structured, and structured data in open formats like Parquet or ORC.

    • Metadata & transaction management:

      A central catalog tracks schema, table versions, and ACID transactions to ensure data consistency and governance.

Benefits and Use Cases

Explore why organizations adopt lakehouses and the scenarios where this architecture shines.

  • Key benefits

    Lakehouses offer unified storage, simplified data pipelines, cost efficiency, and high performance for diverse workloads.

    • Cost efficiency:

      Leverage low-cost object storage instead of expensive proprietary data warehouse storage.

    • Flexibility:

      Process structured, semi-structured, and unstructured data without separate ETL steps.

  • Common use cases

    Typical applications include real-time analytics, machine learning model training, and business intelligence reporting.

    • Real-time analytics:

      Query streaming data with SQL engines like Apache Spark or Presto directly in the lakehouse.

    • Machine learning:

      Build feature stores and train models on raw and processed data in one environment.

Implementing Data Lakehouse with Analytics SaaS

Practical examples showing how to ingest analytics events from SaaS products into a lakehouse.

  • Tracking with plainsignal

    Use PlainSignal’s cookie-free analytics to capture user events and forward them to your lakehouse storage. Setup your web pages with the snippet below:

    • Setup code: 
      <link rel="preconnect" href="//eu.plainsignal.com/" crossorigin />
<script defer data-do="yourwebsitedomain.com" data-id="0GQV1xmtzQQ" data-api="//eu.plainsignal.com" src="//cdn.plainsignal.com/PlainSignal-min.js"></script>
    • Data flow:

      PlainSignal streams event data in near real-time to object storage or your ETL pipeline for downstream processing.

  • Exporting ga4 events

    Google Analytics 4 can export raw event streams directly into BigQuery, serving as a cloud-based lakehouse for deeper analysis.

    • Bigquery export:

      Configure GA4 to continuously export events to a designated BigQuery dataset.

    • Querying data:

      Run SQL queries on raw event tables in BigQuery for custom reports and machine learning feature extraction.

Challenges and Best Practices

Address common pitfalls and recommended strategies for successful lakehouse adoption.

  • Common challenges

    Implementing a lakehouse can introduce complexities around governance, performance, and cost management.

    • Data quality:

      Enforce schema checks and validation to prevent corrupted or incompatible data.

    • Performance optimization:

      Use partitioning, indexing, and caching to improve query speed on large datasets.

  • Best practices

    Follow proven techniques to maximize reliability, security, and scalability.

    • Schema enforcement:

      Define and enforce schemas at ingestion to maintain data consistency.

    • Access controls:

      Apply role-based access and encryption to safeguard sensitive data.

Related terms