Data Lake

What is Data Lake?

A data lake is a centralized repository that stores vast amounts of raw data in its native format—structured, semi-structured, and unstructured—until it's needed for analysis or processing. Unlike traditional databases or data warehouses that require data to be processed and structured before storage, data lakes accept data in any format, at any scale, enabling organizations to store everything first and determine how to use it later.

For B2B SaaS and go-to-market teams, data lakes serve as comprehensive storage foundations for diverse data types that don't fit neatly into traditional relational structures. This includes JSON event streams from product analytics, unstructured customer support transcripts, semi-structured API responses from enrichment providers, clickstream data, log files, and large-scale behavioral datasets. Modern GTM operations increasingly generate massive volumes of varied data—website interactions, email engagement, advertising impressions, product telemetry, sales call recordings—that require flexible, scalable storage before transformation into analysis-ready formats.

The data lake concept emerged from the big data movement in the early 2010s, popularized by Hadoop-based systems, and has evolved significantly with cloud storage platforms like Amazon S3, Azure Data Lake Storage, and Google Cloud Storage. These cloud-native implementations provide virtually unlimited storage at low cost, making it economically feasible to retain complete historical data rather than sampling or aggregating prematurely. According to Gartner's research on data lake architectures, organizations implementing modern data lakes report 40-60% reductions in storage costs while enabling previously impossible analytical capabilities through comprehensive historical retention.

Key Takeaways

• Schema-on-read flexibility: Data lakes store raw data without predefined schemas, allowing multiple teams to interpret and structure the same data differently for various use cases

• Multi-format support: Handle structured database exports, semi-structured JSON/XML, and unstructured text, audio, or video in the same repository

• Cost-effective scale: Cloud object storage enables petabyte-scale data retention at a fraction of traditional database costs

• Foundation for advanced analytics: Machine learning, predictive modeling, and AI initiatives require large historical datasets that data lakes provide

• Risk of data swamps: Without governance, metadata management, and clear use cases, data lakes become disorganized "data swamps" with low utilization

How It Works

Data lakes operate through a fundamentally different architecture than traditional structured databases or data warehouses:

Storage Layer

The foundation of a data lake is object storage—typically cloud platforms like Amazon S3, Azure Blob Storage, or Google Cloud Storage. Data is stored as files or objects organized by:

• Landing Zones: Raw data as received from source systems, unmodified and unprocessed

• Staging Areas: Partially processed data undergoing validation, cleansing, or transformation

• Curated Zones: Cleaned, enriched, and organized data ready for analysis

• Archive Storage: Historical data retained for compliance or long-term analysis, often in lower-cost storage tiers

Files are organized using logical partitioning schemes—typically by date, source system, or data type—to enable efficient querying without scanning entire datasets.

Ingestion Processes

Data flows into lakes through various data ingestion patterns:

• Batch Uploads: Scheduled transfers of large datasets via ETL tools or cloud storage sync

• Streaming Ingestion: Real-time event streams from platforms like Kafka, Kinesis, or Pub/Sub writing continuously to object storage

• Direct API Writes: Applications or services writing data directly to lake storage through SDK or REST APIs

• Database Exports: Periodic snapshots or change data capture (CDC) from operational databases

Unlike traditional databases that reject malformed or unexpected data, data lakes accept everything, deferring validation and transformation until data is accessed.

Query and Processing

Modern data lakes use "schema-on-read" approaches where structure is applied when data is queried rather than when stored:

• SQL Query Engines: Tools like AWS Athena, Azure Synapse, Google BigQuery, or Presto/Trino query data directly in object storage using standard SQL

• Data Processing Frameworks: Spark, Flink, or cloud-native services (AWS Glue, Dataflow) read from lakes, transform data, and write results back or to other systems

• Direct File Access: Applications, notebooks, or ML frameworks read raw files directly for specialized processing

This separation of storage and compute allows multiple teams to process the same data using different tools and techniques without copying or moving it.

Metadata and Cataloging

Effective data lakes require robust metadata management:

• Data Catalogs: Systems like AWS Glue Catalog, Azure Purview, or Alation that track what data exists, where it's located, what format it uses, and what it represents

• Schema Registry: Version-controlled schemas for semi-structured data formats (Avro, Protobuf, JSON Schema)

• Data Lineage: Documentation of data sources, transformation history, and downstream consumers

Without strong metadata management, data lakes quickly become "data swamps"—vast storage repositories where teams cannot find, understand, or trust the data they contain.

Key Features

• Unlimited scalability: Store petabytes of data across millions of files without architectural limits or expensive infrastructure

• Format agnostic: Handle any data type—CSV, JSON, Parquet, Avro, log files, images, audio, video—in the same repository

• Separation of storage and compute: Scale storage and processing independently, paying only for what you use

• Low storage costs: Object storage costs $0.02-0.05 per GB per month, dramatically cheaper than relational database storage

• Complete historical retention: Economically feasible to keep years of granular event data for longitudinal analysis and model training

Use Cases

Use Case 1: Comprehensive Behavioral Event Storage

Marketing and product teams generate millions of behavioral events daily—website pageviews, product feature usage, email opens, ad impressions, form submissions, video plays. Storing this granular, event-level data in traditional data warehouses becomes prohibitively expensive at scale. Data lakes provide cost-effective storage for complete clickstream history, enabling retroactive analysis, machine learning model training, and detailed customer journey mapping. Tools like Segment or Rudderstack can archive complete event streams to data lakes while loading aggregated summaries to warehouses, giving teams both detailed exploration capabilities and efficient querying for common analyses.

Use Case 2: Machine Learning Feature Store and Model Training

Data science teams building predictive analytics models for lead scoring, churn prediction, or account health require large historical datasets spanning years. Data lakes store complete historical behavioral data, transactional records, and outcomes that become training datasets for ML models. For example, building a model to predict which accounts will expand requires 2-3 years of historical product usage events, support interactions, and expansion outcomes. This volume and granularity exceeds practical warehouse storage but fits naturally in data lake architectures. Modern ML platforms like Databricks, SageMaker, or Vertex AI read directly from data lakes, train models on historical data, and write predictions back for activation in GTM systems.

Use Case 3: Multi-Format Data Consolidation

Revenue operations teams increasingly work with diverse data formats beyond traditional structured records. This includes sales call recordings and transcripts, customer support chat logs, product usage telemetry, API interaction logs, and enrichment data responses. Data lakes serve as unified repositories for these mixed formats, enabling centralized storage and access. For instance, storing sales call recordings (audio files) alongside their AI-generated transcripts (text), CRM opportunity data (structured), and email correspondence (semi-structured) in the same lake allows teams to build comprehensive account intelligence by correlating insights across formats. According to Forrester's research on unified data strategies, organizations that consolidate multi-format data report 25-35% improvements in analytical coverage and insight quality.

Implementation Example

Here's a modern data lake architecture for B2B SaaS GTM teams:

GTM Data Lake Architecture

Cloud-Native Data Lake Implementation
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
<p>INGESTION SOURCES          LAKE ZONES                CONSUMPTION<br>─────────────────          ──────────                ───────────</p>
<p>Product Events    ──→   ┌─────────────────┐<br>(Segment)               │  RAW / LANDING   │<br>└─ JSON stream        │  ────────────    │<br>└─ 5M events/day      │  /raw/segment/   │      SQL Query Engine<br>│  /raw/salesforce/│   ──→ (AWS Athena)<br>CRM Data         ──→    │  /raw/hubspot/   │        └─ Ad-hoc analysis<br>(Salesforce API)        │                  │        └─ Exploration<br>└─ Daily export       │  Format: Native  │<br>└─ JSON/CSV           │  Retention: 90d  │<br>└─────────────────┘      Data Processing<br>Enrichment Data  ──→                           ──→ (Spark/Databricks)<br>(Clearbit API)          ┌─────────────────┐        └─ ML training<br>└─ Weekly batch       │  STAGED          │        └─ Transformations<br>└─ JSON               │  ───────         │<br>│  /staged/events/ │<br>Call Recordings  ──→    │  /staged/crm/    │      Direct Access<br>(Gong API)              │                  │   ──→ (Python/R notebooks)<br>└─ Audio + JSON       │  Format: Parquet │        └─ Data science<br>└─ Transcripts        │  Partitioned     │        └─ Custom tools<br>│  Retention: 1yr  │<br>└─────────────────┘      Data Warehouse<br>Log Files        ──→                           ──→ (Snowflake)<br>(Application)           ┌─────────────────┐        └─ Aggregated views<br>└─ Daily dump         │  CURATED         │        └─ BI reporting<br>└─ Compressed         │  ────────        │<br>│  /curated/       │<br>│  └─ accounts/    │      ML Platforms<br>│  └─ contacts/    │   ──→ (SageMaker)<br>│  └─ events/      │        └─ Model training<br>│                  │        └─ Predictions<br>│  Format: Parquet │<br>│  Optimized       │<br>│  Retention: 3yr  │      Reverse ETL<br>└─────────────────┘   ──→ (Hightouch)<br>└─ CRM activation<br>┌─────────────────┐        └─ Personalization<br>│  ARCHIVE         │<br>│  ───────         │<br>│  /archive/       │<br>│                  │<br>│  Low-cost tier   │<br>│  Retention: 7yr  │<br>└─────────────────┘</p>
<pre><code>                 METADATA &amp; GOVERNANCE
                 ─────────────────────
                 ├─ AWS Glue Catalog
                 ├─ Schema registry
                 ├─ Data lineage tracking
                 ├─ Access controls (IAM)
                 └─ Cost monitoring
</code></pre>

Data Lake Storage Organization

Data Catalog Metadata Structure

Table: product_events_staged
─────────────────────────────────────────────
Location: s3://company-datalake/staged/events/
Format: Parquet
Partitioned By: date, event_type
Columns:
  - event_id (STRING)
  - user_id (STRING)
  - account_id (STRING)
  - event_type (STRING)
  - timestamp (TIMESTAMP)
  - properties (STRUCT)
Owner: Product Analytics Team
Lineage:
  ← Ingested from: Segment webhook
  → Consumed by: [ML training pipeline, Athena queries, Databricks notebooks]
Update Frequency: Real-time streaming
Row Count: ~5M/day, 1.8B total
Quality Checks:
  ✓ event_id uniqueness
  ✓ timestamp validity
  ✓ account_id presence (99.2% complete)

This architecture demonstrates how modern data lakes use cloud object storage, partitioning strategies, and metadata catalogs to provide scalable, cost-effective foundations for diverse GTM data needs. The key is maintaining clear zone separation, comprehensive metadata, and well-defined consumption patterns that prevent the lake from becoming a data swamp.

Related Terms

• Data Warehouse: Structured, schema-on-write repositories optimized for SQL analytics and BI reporting

• Data Ingestion: Processes that move data from sources into lakes or warehouses

• Customer Data Platform: Operational systems that may use data lakes for historical storage while maintaining real-time profile stores

• Identity Resolution: Matching and merging records across datasets, often performed on lake-stored data

• Predictive Analytics: ML models trained on large historical datasets stored in data lakes

• Product Analytics: Behavioral analysis platforms that often archive raw events to data lakes

• Reverse ETL: Moving processed data from lakes or warehouses back to operational tools

Frequently Asked Questions

What is a data lake?

Quick Answer: A data lake is a centralized repository that stores all your raw data—structured, semi-structured, and unstructured—in its native format at any scale, allowing you to process and analyze it later without requiring upfront schema definition.

Data lakes provide cost-effective, flexible storage for diverse data types that don't fit well in traditional databases or warehouses. They're particularly valuable for high-volume behavioral data, multi-format consolidation, and machine learning applications requiring large historical datasets.

What's the difference between a data lake and a data warehouse?

Quick Answer: Data lakes store raw, unprocessed data in native formats with schema-on-read, while data warehouses store structured, processed data with schema-on-write, optimized for SQL queries and business intelligence reporting.

Think of data warehouses as highly organized filing cabinets where everything is labeled, categorized, and ready for quick retrieval—but you must decide the organization upfront. Data lakes are more like storage units where you can throw everything in any format and organize it later when you know what you need. Warehouses excel at structured analysis and BI dashboards with consistent queries. Lakes excel at exploratory analysis, machine learning, and handling diverse data formats. Modern architectures often use both—lakes for comprehensive storage and initial processing, warehouses for cleaned, analysis-ready datasets. This "lakehouse" pattern combines the flexibility of lakes with the performance of warehouses.

When should GTM teams use a data lake?

Quick Answer: Use data lakes when you have high-volume event data (millions of behavioral events), diverse data formats (audio, logs, JSON), require years of historical retention for ML, or need cost-effective storage for exploratory analytics and data science workloads.

If your primary need is structured reporting and dashboards with well-defined queries, a data warehouse alone is likely sufficient and simpler. Consider adding a data lake when warehouse costs become prohibitive due to data volume, when you need to retain raw event-level data for retroactive analysis, when building ML models requiring large training datasets, or when consolidating multi-format data like call recordings, logs, and structured records. Many teams start with warehouses and add lakes as data volumes and analytical sophistication grow.

What prevents data lakes from becoming data swamps?

Data lakes become "data swamps"—disorganized repositories where teams cannot find or trust data—without strong governance practices. Prevent this by implementing comprehensive data catalogs that document what data exists and what it represents, establishing clear zone separation (raw, staged, curated) with defined transformation processes, enforcing metadata standards and naming conventions, tracking data lineage from source to consumption, implementing data quality monitoring, and ensuring every ingested dataset has a defined owner and use case. Tools like AWS Glue Catalog, Azure Purview, Alation, or Collibra help automate metadata management and discovery. The key cultural practice is "no data without metadata"—never ingest data without documenting its source, purpose, and structure.

How much does a data lake cost?

Data lake storage costs are remarkably low—typically $20-25 per terabyte per month for frequently accessed data (AWS S3 Standard, Azure Blob Hot tier) and $1-4 per terabyte for archive storage (AWS Glacier, Azure Archive). For comparison, data warehouse storage often costs $23-40+ per terabyte per month with compute charges on top. However, total lake costs include compute for processing queries (AWS Athena charges $5 per terabyte scanned), data transfer fees, and metadata catalog costs. A typical B2B SaaS company storing 10TB of GTM data might pay $250-300/month for storage plus $200-500/month for query processing, dramatically cheaper than warehouse-only approaches at scale. Use cost monitoring tools and implement partitioning strategies to minimize query scanning costs.

Conclusion

Data lakes have evolved from niche big data infrastructure into essential components of modern GTM data architectures. For B2B SaaS teams generating millions of behavioral events, working with diverse data formats, and building advanced analytics capabilities, data lakes provide the scalable, cost-effective storage foundation that traditional data warehouses cannot match economically. The ability to retain complete historical data—years of granular clickstream events, product telemetry, engagement signals, and transactional records—at minimal cost enables analytical capabilities and machine learning applications that were previously impossible.

The key to successful data lake implementation is avoiding the "data swamp" trap through disciplined governance practices. This means comprehensive metadata catalogs documenting what data exists and what it represents, clear zone separation between raw ingestion and curated analysis-ready data, well-defined data lineage tracking, and strong ownership models ensuring every dataset has defined purposes and maintainers. Marketing operations, data science, and revenue analytics teams benefit most when lakes complement rather than replace data warehouses—using lakes for comprehensive storage and initial processing while warehouses serve cleaned, aggregated datasets for standard reporting.

The future of GTM data infrastructure increasingly embraces "lakehouse" architectures that combine data lake flexibility and economics with data warehouse performance and usability. Modern platforms like Databricks, Snowflake (with external tables), and Google BigQuery (with BigLake) blur the traditional boundaries, offering unified systems that provide both capabilities. As machine learning, AI-powered workflows, and real-time personalization become standard practice, the comprehensive historical datasets that data lakes enable will become increasingly critical for competitive advantage. Start by identifying high-volume, multi-format, or exploratory analytics use cases where lakes add value beyond your existing warehouse, implement strong governance foundations from day one, and systematically expand lake usage as your data sophistication grows. Related concepts to explore include data ingestion patterns and identity resolution at scale.

Last Updated: January 18, 2026