Designing a Modern Data Stack Architecture

The modern data stack has revolutionized how organizations collect, process, and analyze data. This guide explores the key components and design principles for building a scalable, cost-effective data architecture.

Prerequisites

This guide assumes familiarity with:

• Cloud computing concepts (AWS, GCP, or Azure)
• SQL and basic data modeling
• ETL/ELT concepts
• Data warehousing fundamentals

What is the Modern Data Stack?

The modern data stack is a cloud-native collection of tools that work together to handle the complete data lifecycle. Key characteristics include:

• Cloud-native: Built for cloud scalability and economics
• Modular: Best-of-breed tools that integrate via APIs
• Managed: SaaS or managed services reduce operational burden
• SQL-centric: Familiar interface for analysts and engineers

Core Components

1. Data Sources

Modern architectures must handle diverse data sources:

• Operational databases: PostgreSQL, MySQL, MongoDB
• SaaS applications: Salesforce, HubSpot, Stripe
• Event streams: Mobile apps, websites, IoT devices
• Files: S3, GCS, Azure Blob Storage

2. Data Ingestion

Move data from sources to your data platform.

Batch Ingestion Tools:

• Fivetran
• Airbyte
• Stitch

Real-time Streaming:

• Kafka
• AWS Kinesis
• Confluent Cloud

Design Considerations:

┌─────────────────────────────────────────────────────┐
│                    Data Sources                      │
│  ┌─────┐  ┌─────┐  ┌─────┐  ┌─────┐  ┌─────┐       │
│  │ DB  │  │ API │  │Events│  │Files│  │ SaaS│       │
│  └──┬──┘  └──┬──┘  └──┬──┘  └──┬──┘  └──┬──┘       │
└─────┼────────┼───────┼────────┼────────┼───────────┘
      │        │       │        │        │
      ▼        ▼       ▼        ▼        ▼
┌─────────────────────────────────────────────────────┐
│            Ingestion Layer                           │
│  ┌──────────────────┐  ┌──────────────────────┐    │
│  │  Batch (Fivetran)│  │  Stream (Kafka)      │    │
│  └────────┬─────────┘  └──────────┬───────────┘    │
└───────────┼───────────────────────┼─────────────────┘
            │                       │
            ▼                       ▼
┌─────────────────────────────────────────────────────┐
│                 Data Lake / Lakehouse               │
└─────────────────────────────────────────────────────┘

3. Data Storage

The storage layer is the foundation of your architecture.

Cloud Data Warehouses:

Lakehouse Considerations:

• Combines data lake flexibility with warehouse performance
• Supports structured and unstructured data
• Enables ML workloads alongside BI

4. Data Transformation

Transform raw data into analytics-ready models.

dbt (data build tool): The standard for transformation in the modern stack.

-- models/marts/customers/dim_customers.sql
{{ config(materialized='table') }}

with customers as (
    select * from {{ ref('stg_customers') }}
),

orders as (
    select * from {{ ref('stg_orders') }}
),

customer_orders as (
    select
        customer_id,
        count(*) as order_count,
        sum(amount) as lifetime_value
    from orders
    group by customer_id
)

select
    c.customer_id,
    c.name,
    c.email,
    co.order_count,
    co.lifetime_value
from customers c
left join customer_orders co on c.customer_id = co.customer_id

5. Data Orchestration

Coordinate and schedule data pipelines.

Popular Tools:

• Airflow / Astronomer
• Dagster
• Prefect
• dbt Cloud

Key Features:

• Dependency management
• Failure handling and alerting
• Monitoring and observability
• Backfill capabilities

6. Business Intelligence

Enable self-service analytics and reporting.

BI Platforms:

• Looker
• Tableau
• Power BI
• Metabase
• Mode

Semantic Layer: Consider a semantic layer for consistent metrics:

• dbt Metrics
• Cube
• AtScale

7. Data Observability

Monitor data quality and pipeline health.

Observability Tools:

• Monte Carlo
• Anomalo
• Great Expectations
• Soda

Key Capabilities:

• Freshness monitoring
• Volume tracking
• Schema change detection
• Anomaly detection

Reference Architecture

┌─────────────────────────────────────────────────────────────┐
│                         DATA SOURCES                         │
│   Databases    SaaS Apps    Events    Files    APIs         │
└──────────────────────────┬──────────────────────────────────┘
                           │
                           ▼
┌─────────────────────────────────────────────────────────────┐
│                    INGESTION LAYER                           │
│              Fivetran / Airbyte / Kafka                     │
└──────────────────────────┬──────────────────────────────────┘
                           │
                           ▼
┌─────────────────────────────────────────────────────────────┐
│                    STORAGE LAYER                             │
│         Snowflake / BigQuery / Databricks                   │
│   ┌─────────────┐  ┌─────────────┐  ┌─────────────┐        │
│   │  Raw Layer  │→│ Staging     │→│ Marts       │        │
│   └─────────────┘  └─────────────┘  └─────────────┘        │
└──────────────────────────┬──────────────────────────────────┘
                           │
                           ▼
┌─────────────────────────────────────────────────────────────┐
│                 TRANSFORMATION LAYER                         │
│                       dbt                                    │
└──────────────────────────┬──────────────────────────────────┘
                           │
                           ▼
┌─────────────────────────────────────────────────────────────┐
│                   CONSUMPTION LAYER                          │
│   BI Tools    Notebooks    Applications    ML Platforms     │
└─────────────────────────────────────────────────────────────┘

Summary

The modern data stack provides a flexible, scalable foundation for data-driven organizations. Key principles:

1. Choose cloud-native tools that scale with your needs
2. Embrace modularity with best-of-breed components
3. Invest in transformation with dbt for maintainable data models
4. Prioritize observability to catch issues before they impact decisions
5. Enable self-service with semantic layers and quality BI tools

Next Steps

1. Assess your current architecture against modern patterns
2. Identify your highest-priority use cases
3. Select your core storage platform
4. Implement dbt for transformations
5. Add observability early, not as an afterthought

Related Resources

• Building a Data Governance Framework From Scratch - Architecture must support governance requirements
• Data Strategy Assessment Checklist - Assessment includes architecture evaluation
• What is Data Strategy and Why Does Your Organization Need One? - Architecture implements strategy objectives
• How to Measure Data Quality - Learn to implement quality monitoring in your data pipelines
• Browse all guides - Explore more comprehensive implementation guides

Process Flow Diagram

flowchart TD
  A["Designing a Modern Data Stack Architecture"] --> B["Define scope"]
  B --> C["Map stakeholders & data assets"]
  C --> D["Implement controls & workflows"]
  D --> E["Measure outcomes and iterate"]