What’s a Data Lake and What Does It Mean For My Open Source ClickHouse® Stack?

In today’s data-driven landscape, organizations are generating massive volumes of structured and unstructured data from various sources. As businesses seek to harness this information for competitive advantage, understanding data architecture concepts like data lakes becomes crucial, especially when working with high-performance analytical databases like ClickHouse®.

Understanding Data Lakes: The Foundation of Modern Analytics

What is a Data Lake?

A data lake is a centralized repository that allows you to store vast amounts of raw data in its native format until it’s needed. Unlike traditional data warehouses that require data to be structured before storage, data lakes can accommodate:

• Structured data (databases, spreadsheets)
• Semi-structured data (JSON, XML, logs)
• Unstructured data (images, videos, documents, social media content)

This flexibility makes data lakes particularly valuable for organizations dealing with diverse data sources and formats.

Key Characteristics of Data Lakes

-- Example: Raw log data stored in data lake format
{
  "timestamp": "2025-06-24T10:30:00Z",
  "user_id": "12345",
  "event_type": "page_view",
  "url": "/products/analytics",
  "session_id": "abc123",
  "metadata": {
    "browser": "Chrome",
    "device": "desktop"
  }
}

Data lakes offer several distinctive features:

• Schema-on-Read: Data structure is applied when accessed, not when stored
• Cost-Effective Storage: Typically uses commodity hardware and cloud storage
• Scalability: Can handle petabytes of data across distributed systems
• Data Variety: Supports multiple data formats and sources
• Processing Flexibility: Enables various analytics approaches

ClickHouse® and the Data Lake Ecosystem

ClickHouse® Overview

ClickHouse® is an open-source columnar database management system designed for online analytical processing (OLAP). Its architecture makes it exceptionally well-suited for real-time analytics on large datasets, with capabilities including:

• Columnar Storage: Optimized for analytical queries
• Vectorized Query Execution: High-performance processing
• Distributed Architecture: Horizontal scaling capabilities
• Real-Time Ingestion: Low-latency data processing

How ClickHouse® Integrates with Data Lakes

ClickHouse® can serve multiple roles within a data lake architecture:

1. High-Performance Query Engine

-- Example: Querying data lake files directly from ClickHouse®
SELECT 
    toDate(timestamp) as date,
    count(*) as events,
    uniq(user_id) as unique_users
FROM s3('https://my-data-lake/events/*.parquet')
WHERE timestamp >= today() - 7
GROUP BY date
ORDER BY date;

ClickHouse® can directly query data stored in data lake formats like Parquet, ORC, and JSON files stored in cloud storage systems.

2. Real-Time Analytics Layer

While data lakes excel at storing raw data, ClickHouse® provides the analytical horsepower needed for:

• Real-time dashboards
• Interactive analytics
• Complex aggregations
• Time-series analysis

3. Data Processing Hub

-- Example: ETL processing within ClickHouse®
INSERT INTO processed_events
SELECT 
    user_id,
    event_type,
    toDateTime(timestamp) as event_time,
    JSONExtractString(metadata, 'browser') as browser
FROM raw_events
WHERE event_time >= now() - INTERVAL 1 HOUR;

Architectural Patterns: ClickHouse® in Data Lake Environments

Pattern 1: Lambda Architecture

In a lambda architecture, ClickHouse® can serve as both the speed layer for real-time processing and part of the serving layer for analytical queries:

Data Sources → Kafka → ClickHouse® (Speed Layer)
     ↓
Data Lake (Batch Layer) → Spark → ClickHouse® (Serving Layer)

Pattern 2: Lakehouse Architecture

The lakehouse pattern combines data lake flexibility with data warehouse performance:

-- Example: Creating external table for lakehouse pattern
CREATE TABLE events_external AS s3(
    'https://data-lake/events/{year}/{month}/{day}/*.parquet',
    'Parquet'
) PARTITION BY (year, month, day);

Pattern 3: Data Mesh with ClickHouse®

In a data mesh architecture, ClickHouse® can serve as domain-specific analytical engines:

• Marketing Domain: ClickHouse® cluster for campaign analytics
• Product Domain: ClickHouse® cluster for user behavior analysis
• Operations Domain: ClickHouse® cluster for system monitoring

Implementation Strategies

Data Ingestion Patterns

Streaming Ingestion

-- Kafka integration for real-time data
CREATE TABLE kafka_events (
    user_id UInt64,
    event_type String,
    timestamp DateTime,
    properties String
) ENGINE = Kafka()
SETTINGS 
    kafka_broker_list = 'localhost:9092',
    kafka_topic_list = 'events',
    kafka_group_name = 'clickhouse_consumer';

Batch Processing

-- S3 batch ingestion
INSERT INTO events
SELECT * FROM s3(
    'https://data-lake/daily-batch/2025-06-24/*.parquet',
    'Parquet'
);

Storage Optimization

ClickHouse® offers several storage optimizations for data lake scenarios:

-- Partitioning strategy for time-series data
CREATE TABLE events (
    timestamp DateTime,
    user_id UInt64,
    event_type String,
    properties String
) ENGINE = MergeTree()
PARTITION BY toYYYYMM(timestamp)
ORDER BY (timestamp, user_id);

Query Optimization

-- Materialized views for common aggregations
CREATE MATERIALIZED VIEW daily_metrics
ENGINE = SummingMergeTree()
ORDER BY (date, event_type)
AS SELECT
    toDate(timestamp) as date,
    event_type,
    count() as event_count,
    uniq(user_id) as unique_users
FROM events
GROUP BY date, event_type;

Benefits of ClickHouse® in Data Lake Architecture

Performance Advantages

• Sub-second Query Response: Even on petabyte-scale datasets
• Compression Efficiency: Reduces storage costs by 10-40x
• Parallel Processing: Utilizes all available CPU cores
• Memory Optimization: Efficient memory usage for large datasets

Cost Optimization

-- Example: Cost-effective data tiering
-- Hot data in ClickHouse® for fast access
-- Cold data in S3 for long-term storage
ALTER TABLE events MOVE PARTITION '202501' TO DISK 's3_cold';

Operational Benefits

• Simplified Architecture: Reduces the number of components needed
• Real-Time Capabilities: Eliminates batch processing delays
• SQL Compatibility: Familiar query interface for analysts
• Open Source Flexibility: No vendor lock-in

Best Practices and Considerations

Data Modeling

-- Denormalized structure optimized for analytics
CREATE TABLE user_events_denormalized (
    timestamp DateTime,
    user_id UInt64,
    user_segment String,
    event_type String,
    product_category String,
    revenue Decimal(10,2)
) ENGINE = MergeTree()
ORDER BY (user_segment, timestamp);

-- System monitoring queries
SELECT 
    database,
    table,
    formatReadableSize(sum(bytes_on_disk)) as size,
    sum(rows) as row_count
FROM system.parts
WHERE active = 1
GROUP BY database, table
ORDER BY sum(bytes_on_disk) DESC;