Data Lake Architecture Pattern

Overview

A Data Lake is a centralized repository that allows you to store all your structured and unstructured data at any scale. You can store data as-is, without having to first structure it, and run different types of analytics.

Architecture

graph TB
    subgraph "Data Sources"
        S1[IoT Devices]
        S2[Applications]
        S3[Databases]
        S4[APIs]
        S5[Files]
    end
    
    subgraph "Ingestion Layer"
        K[Kinesis Firehose]
        DMS[Database Migration Service]
        TF[Transfer Family]
        DS[DataSync]
    end
    
    subgraph "Storage Layer - Data Lake"
        S3Raw[S3 Raw Data Zone]
        S3Clean[S3 Cleaned Data Zone]
        S3Curated[S3 Curated Data Zone]
    end
    
    subgraph "Processing Layer"
        Glue[AWS Glue ETL]
        EMR[EMR Cluster]
        Lambda[Lambda Functions]
    end
    
    subgraph "Catalog & Governance"
        GC[Glue Catalog]
        LF[Lake Formation]
    end
    
    subgraph "Analytics & ML"
        A[Athena]
        RS[Redshift Spectrum]
        SM[SageMaker]
        QS[QuickSight]
    end
    
    S1 --> K
    S2 --> K
    S3 --> DMS
    S4 --> Lambda
    S5 --> TF
    
    K --> S3Raw
    DMS --> S3Raw
    TF --> S3Raw
    DS --> S3Raw
    Lambda --> S3Raw
    
    S3Raw --> Glue
    Glue --> S3Clean
    S3Clean --> EMR
    EMR --> S3Curated
    
    S3Raw --> GC
    S3Clean --> GC
    S3Curated --> GC
    
    GC --> LF
    LF --> A
    LF --> RS
    LF --> SM
    
    A --> QS
    RS --> QS
    
    style S3Raw fill:#f9f
    style S3Clean fill:#9f9
    style S3Curated fill:#99f

Data Lake Zones

Raw Zone (Bronze)

The landing zone for all incoming data in its original format.

Characteristics:

• Data stored as-is
• No transformations applied
• Historical archive
• Immutable storage

AWS Services:

• S3 with appropriate lifecycle policies
• Versioning enabled
• Server-side encryption

Cleaned Zone (Silver)

Data that has been validated, cleansed, and standardized.

Characteristics:

• Schema validation
• Data quality checks
• Deduplication
• Format standardization (Parquet, ORC)

Processing:

Copy
1# Example AWS Glue job for data cleaning
2import sys
3from awsglue.transforms import *
4from awsglue.utils import getResolvedOptions
5from pyspark.context import SparkContext
6from awsglue.context import GlueContext
7from awsglue.job import Job
8
9args = getResolvedOptions(sys.argv, ['JOB_NAME'])
10sc = SparkContext()
11glueContext = GlueContext(sc)
12spark = glueContext.spark_session
13job = Job(glueContext)
14job.init(args['JOB_NAME'], args)
15
16# Read from raw zone
17raw_data = glueContext.create_dynamic_frame.from_catalog(
18    database="raw_db",
19    table_name="raw_table"
20)
21
22# Apply transformations
23cleaned_data = raw_data.filter(
24    f=lambda x: x["status"] == "active"
25).drop_fields(['temp_field'])
26
27# Write to cleaned zone
28glueContext.write_dynamic_frame.from_options(
29    frame=cleaned_data,
30    connection_type="s3",
31    connection_options={
32        "path": "s3://my-bucket/cleaned/",
33        "partitionKeys": ["year", "month", "day"]
34    },
35    format="parquet"
36)
37
38job.commit()

Curated Zone (Gold)

Business-ready data optimized for analytics and reporting.

Characteristics:

• Aggregated data
• Business logic applied
• Optimized for query performance
• Partitioned and indexed

Key Components

AWS Lake Formation

Lake Formation simplifies data lake setup and management.

Features:

AWS Glue

Serverless ETL service for data preparation.

Copy
1// Example: Glue job configuration with CDK
2import * as glue from 'aws-cdk-lib/aws-glue';
3import * as s3 from 'aws-cdk-lib/aws-s3';
4import * as iam from 'aws-cdk-lib/aws-iam';
5
6const glueJob = new glue.CfnJob(this, 'DataCleaningJob', {
7  name: 'data-cleaning-job',
8  role: glueRole.roleArn,
9  command: {
10    name: 'glueetl',
11    pythonVersion: '3',
12    scriptLocation: 's3://my-bucket/scripts/clean_data.py'
13  },
14  defaultArguments: {
15    '--job-language': 'python',
16    '--job-bookmark-option': 'job-bookmark-enable',
17    '--enable-metrics': 'true',
18    '--enable-continuous-cloudwatch-log': 'true'
19  },
20  glueVersion: '4.0',
21  maxRetries: 1,
22  timeout: 60,
23  workerType: 'G.1X',
24  numberOfWorkers: 10
25});

Data Ingestion Patterns

Batch Ingestion

sequenceDiagram
    participant Source
    participant S3
    participant Glue
    participant Catalog
    participant Athena
    
    Source->>S3: Upload batch files
    S3->>Glue: Trigger crawler
    Glue->>Catalog: Update schema
    Catalog-->>Athena: Schema available
    Athena->>S3: Query data

Real-time Ingestion

sequenceDiagram
    participant IoT
    participant Kinesis
    participant Firehose
    participant S3
    participant Lambda
    
    IoT->>Kinesis: Stream events
    Kinesis->>Firehose: Deliver stream
    Firehose->>S3: Write batches
    S3->>Lambda: Trigger processing
    Lambda->>S3: Write processed data

Query Patterns

Amazon Athena

Copy
1-- Example: Analyzing user behavior
2SELECT 
3    user_id,
4    COUNT(*) as event_count,
5    COUNT(DISTINCT session_id) as sessions,
6    DATE_TRUNC('day', event_timestamp) as event_date
7FROM 
8    curated.user_events
9WHERE 
10    event_date >= DATE '2025-01-01'
11    AND event_type = 'page_view'
12GROUP BY 
13    user_id,
14    DATE_TRUNC('day', event_timestamp)
15HAVING 
16    COUNT(*) > 10
17ORDER BY 
18    event_count DESC
19LIMIT 100;

Redshift Spectrum

Copy
1-- Example: Joining data lake with warehouse
2SELECT 
3    dw.customer_id,
4    dw.customer_name,
5    COUNT(DISTINCT dl.order_id) as total_orders,
6    SUM(dl.order_amount) as total_revenue
7FROM 
8    warehouse.customers dw
9INNER JOIN 
10    spectrum.orders dl ON dw.customer_id = dl.customer_id
11WHERE 
12    dl.order_date >= '2025-01-01'
13GROUP BY 
14    dw.customer_id,
15    dw.customer_name
16ORDER BY 
17    total_revenue DESC;

Security Best Practices

Access Control Layers

graph TD
    A[User Request] --> B{IAM Authentication}
    B -->|Authenticated| C{Lake Formation Permissions}
    B -->|Failed| Z[Deny Access]
    C -->|Authorized| D{S3 Bucket Policy}
    C -->|Denied| Z
    D -->|Allowed| E{Resource Policy}
    D -->|Denied| Z
    E -->|Granted| F[Access Data]
    E -->|Denied| Z
    
    style B fill:#ffe0b2
    style C fill:#fff3e0
    style D fill:#f3e5f5
    style E fill:#e1bee7
    style F fill:#c5e1a5
    style Z fill:#ffcdd2

Encryption Strategy

1. At Rest

   • S3 bucket encryption (SSE-S3, SSE-KMS)
   • RDS encryption
   • EBS encryption

2. In Transit

   • TLS/SSL for all connections
   • VPC endpoints for private connectivity
   • Certificate management with ACM

3. Application Level

   • Field-level encryption for sensitive data
   • Tokenization for PII
   • Key rotation policies

Data Governance

Data Quality Framework

Copy
1# AWS Glue Data Quality example
2import boto3
3from awsglue.context import GlueContext
4
5def validate_data_quality(df, rules):
6    """
7    Validate data against quality rules
8    """
9    quality_checks = {
10        'completeness': check_completeness(df, rules['required_fields']),
11        'accuracy': check_accuracy(df, rules['validation_rules']),
12        'consistency': check_consistency(df, rules['consistency_rules']),
13        'timeliness': check_timeliness(df, rules['freshness_threshold'])
14    }
15    
16    return quality_checks
17
18def check_completeness(df, required_fields):
19    """Check for null values in required fields"""
20    results = {}
21    for field in required_fields:
22        null_count = df.filter(df[field].isNull()).count()
23        total_count = df.count()
24        completeness_rate = 1 - (null_count / total_count)
25        results[field] = {
26            'completeness': completeness_rate,
27            'passed': completeness_rate >= 0.95
28        }
29    return results

Cost Optimization

Storage Tiering

Processing Optimization

• Partitioning: Reduce data scanned
• File Format: Use columnar formats (Parquet, ORC)
• Compression: Enable compression (Snappy, Gzip)
• Glue Jobs: Right-size worker types and counts
• Athena: Use result reuse and query optimization

Monitoring and Observability

Key Metrics

graph LR
    A[CloudWatch Metrics] --> B[Data Ingestion Rate]
    A --> C[Query Performance]
    A --> D[ETL Job Duration]
    A --> E[Storage Usage]
    A --> F[Data Quality Score]
    
    B --> G[CloudWatch Dashboard]
    C --> G
    D --> G
    E --> G
    F --> G
    
    G --> H[SNS Alerts]
    G --> I[Lambda Actions]
    
    style G fill:#e3f2fd
    style H fill:#ffcdd2
    style I fill:#c5e1a5

Use Cases

1. Customer 360 Analytics

Combine data from multiple sources to create a unified customer view.

2. IoT Data Processing

Collect, store, and analyze IoT sensor data at scale.

3. Log Analytics

Centralize and analyze application and infrastructure logs.

4. Machine Learning

Train ML models on large datasets with SageMaker.

5. Data Archival

Long-term storage of historical data for compliance.

Implementation Checklist

• Define data lake zones and policies
• Set up S3 buckets with encryption
• Configure Lake Formation permissions
• Create Glue crawlers and ETL jobs
• Set up data quality checks
• Configure monitoring and alerting
• Implement data lifecycle policies
• Document data catalog
• Train users on query tools
• Establish data governance processes

Related Patterns

• Serverless Pattern
• Event-Driven Design
• Analytics Architecture

Resources

• AWS Lake Formation Documentation
• AWS Glue Best Practices
• Amazon Athena Performance Tuning