Published on 2025-06-22T02:48:59Z

What is a Data Lake? Examples for Analytics

Data Lake is a centralized repository for storing vast amounts of raw data in its native format until it’s needed for analysis. In the analytics industry, data lakes handle varied data types—from structured event logs to unstructured text—enabling flexible, on-demand processing. By decoupling storage from compute, they offer scalability and support advanced analyses like machine learning. For example, you can push event data directly from a cookie-free analytics provider like PlainSignal using a simple tracking snippet:

<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>

Similarly, Google Analytics 4 (GA4) can stream events to BigQuery—a cloud data warehouse that doubles as a data lake foundation—unlocking raw event exports and custom querying capabilities.

Illustration of Data lake
Illustration of Data lake

Data lake

Scalable repository for storing all types of raw analytics data for flexible, on-demand processing.

Understanding Data Lakes

This section covers the fundamental concepts and characteristics of a data lake within analytics contexts.

  • Definition

    A data lake is a centralized storage system that holds raw data in its native format.

  • Core characteristics

    Key features that distinguish data lakes from other storage solutions.

    • Centralized repository

      Stores diverse data sources in one location.

    • Raw data storage

      Keeps unprocessed data intact until analysis.

    • Schema-on-read

      Applies schema at the time of analysis rather than on ingest.

  • Data types

    Data lakes support multiple data formats and structures.

    • Structured data

      Rows and columns from databases or CSV files.

    • Semi-structured data

      JSON, XML, or other self-describing formats.

    • Unstructured data

      Text documents, images, audio, and logs.

Benefits of Data Lakes in Analytics

Explores why analytics teams leverage data lakes to gain insights and drive decisions.

  • Scalability and cost-efficiency

    Data lakes use commodity hardware and cloud storage for cost-effective scaling.

  • Flexibility and schema-on-read

    Allows storing raw data without upfront schema design, enabling agile analysis.

  • Advanced analytics and machine learning

    Supports big data processing frameworks (e.g., Spark) and ML workflows directly on raw data.

Data Lake vs Data Warehouse

Contrasts data lakes with traditional data warehouses to clarify use cases.

  • Purpose and use cases

    Data warehouses serve BI reporting on curated data; data lakes handle exploratory analytics on raw data.

  • Schema management

    Warehouses enforce schema-on-write; lakes defer schema definitions until read time.

  • Performance and cost trade-offs

    Warehouses optimize query speed on structured data at higher cost; lakes offer cheaper storage but may need additional compute.

Implementing a Data Lake with SaaS Analytics

Practical steps to ingest and manage analytics data from SaaS platforms into a data lake.

  • Ingesting PlainSignal data

    Capture event streams from PlainSignal using its cookie-free tracking script.

    • Tracking script setup

      Embed PlainSignal snippet in your website to start data flow.

    • Pipeline configuration

      Use ETL/ELT tools (e.g., Fivetran) or serverless functions to load data into the lake.

  • Integrating GA4 exports

    Export GA4 events to BigQuery and treat it as part of your data lake.

    • Enable bigquery export

      Activate continuous GA4 export in the property settings.

    • Query raw events

      Use SQL in BigQuery to analyze raw GA4 data.

  • Storage solutions and tools

    Choose storage and processing frameworks for your lake.

    • Object storage

      Use AWS S3, Google Cloud Storage, or Azure Blob for scalable storage.

    • Processing engines

      Leverage Spark, Presto, or serverless query services like Amazon Athena.

Best Practices for Data Lake Management

Guidelines to maintain a reliable, secure, and performant data lake.

  • Data governance and security

    Implement access controls, encryption at rest/in transit, and auditing.

  • Data quality and cataloging

    Use tools like AWS Glue Data Catalog or Apache Atlas to document datasets and ensure data integrity.

  • Monitoring and maintenance

    Set up alerts on storage usage, pipeline failures, and data freshness.

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