Modern Data Stack Components: Building Blocks for Data Infrastructure

The modern data stack is a collection of cloud-native, specialized tools that work together to move, store, transform, and analyze data. Unlike monolithic legacy systems, the modern data stack emphasizes best-of-breed components, managed services, and interoperability.

This architecture pattern has become dominant because it offers flexibility, scalability, and faster time to value compared to traditional approaches.

Modern Data Stack Philosophy

Cloud-Native

Components run in the cloud as managed services:

• No infrastructure to maintain
• Elastic scaling based on demand
• Pay for what you use
• Vendor handles operations

Cloud-native reduces operational burden.

Best-of-Breed

Choose the best tool for each function:

• Specialized tools excel at specific tasks
• Avoid monolithic compromises
• Switch components independently
• Innovation happens at component level

Specialization beats generalization.

ELT Over ETL

Extract-Load-Transform replaces Extract-Transform-Load:

• Load raw data first
• Transform in the warehouse
• Leverage warehouse compute
• Preserve source fidelity

Warehouses handle transformation better than external tools.

Separation of Concerns

Different tools handle different jobs:

• Ingestion tools move data
• Warehouses store data
• Transformation tools shape data
• BI tools visualize data

Separation enables optimization and replacement.

Core Components

Data Ingestion

Moving data from sources to storage:

Batch ingestion: Scheduled bulk transfers from databases, files, and APIs. Tools like Fivetran, Airbyte, and Stitch handle common sources automatically.

Streaming ingestion: Real-time data from events and logs. Kafka, Kinesis, and cloud pub/sub services provide streaming infrastructure.

CDC (Change Data Capture): Tracking changes in source databases. Debezium and database-native CDC capture incremental changes.

Ingestion is table stakes - reliable data movement enables everything else.

Data Storage

Where data lives:

Cloud data warehouses: Snowflake, BigQuery, Redshift, and Databricks provide scalable SQL-based storage with separated compute. They're the center of most modern data stacks.

Data lakes: S3, GCS, and Azure Data Lake store raw and unstructured data. Lakes complement warehouses for certain use cases.

Data lakehouses: Architectures combining lake storage with warehouse capabilities. Delta Lake, Iceberg, and Hudi add reliability to lake storage.

Storage choice affects performance, cost, and capabilities.

Data Transformation

Shaping raw data into useful models:

dbt: The dominant transformation tool, enabling SQL-based transformations with testing, documentation, and version control.

SQL-based tools: Various platforms offer SQL transformation capabilities, often integrated with warehouses.

Python-based tools: For complex transformations that exceed SQL capabilities.

Transformation turns raw data into analytics-ready datasets.

Orchestration

Coordinating workflows:

Airflow: The most common orchestrator, defining workflows as directed acyclic graphs.

Dagster: Software-defined data assets with strong typing and testing.

Prefect: Modern workflow orchestration with simpler API.

Cloud-native options: Step Functions, Cloud Workflows, and similar services.

Orchestration ensures data flows reliably and on schedule.

Semantic Layer

Business meaning on top of data:

Metrics stores: Central definitions for business metrics.

Semantic models: Business entities and relationships.

Governance: Access control and certification.

Codd Integrations connect the semantic layer to other modern data stack components, ensuring that business context flows through the entire stack rather than being siloed in a single tool.

Business Intelligence

Visualizing and exploring data:

Traditional BI: Tableau, Power BI, Looker provide dashboarding and reporting.

Modern BI: Lightweight tools focused on speed and exploration.

Embedded analytics: BI capabilities within applications.

Notebooks: Jupyter and similar for exploratory analysis.

BI is how most users interact with data.

Reverse ETL

Pushing data back to operational systems:

Sync to CRM: Customer data enrichment in Salesforce, HubSpot.

Ad platforms: Audience sync to Google, Facebook, LinkedIn.

Support systems: Customer context in Zendesk, Intercom.

Reverse ETL activates analytics in operational workflows.

Data Observability

Monitoring data health:

Quality monitoring: Tracking freshness, volume, and quality.

Lineage: Understanding data flow and dependencies.

Alerting: Notification when issues occur.

Observability ensures the stack runs reliably.

How Components Connect

Data Flow

A typical flow through the modern data stack:

Sources → Ingestion → Warehouse → Transformation → Semantic Layer → BI
                                       ↓
                                 Reverse ETL → Operational Systems

Data moves from sources through processing to consumption.

Metadata Flow

Metadata connects components:

• Ingestion tools describe what they loaded
• Warehouses track schema and statistics
• Transformation tools define lineage
• Semantic layers add business context
• Observability tools aggregate health metrics

Metadata enables understanding and governance.

Integration Patterns

Components integrate through:

APIs: Programmatic connections between services.

Shared storage: Data lakes as common data plane.

Orchestration: Workflows coordinating multiple tools.

Catalogs: Metadata aggregation across components.

Integration determines how well the stack works together.

Building Your Stack

Start with Core Components

Every stack needs:

1. Data warehouse: The foundation for analytical work
2. Ingestion: Moving data into the warehouse
3. Transformation: Shaping data for use
4. BI tool: Visualizing and exploring data

These four components enable basic analytics.

Add Based on Needs

Expand as requirements emerge:

Orchestration: When workflows become complex.

Semantic layer: When consistency and governance matter.

Observability: When reliability becomes critical.

Reverse ETL: When analytics should drive operations.

Don't overbuild initially.

Consider Integration

Choose components that work together:

• Check API and connector availability
• Look for existing integrations
• Consider data format compatibility
• Evaluate metadata exchange capabilities

Integration ease affects long-term productivity.

Plan for Evolution

The stack will change:

• New requirements emerge
• Better tools appear
• Needs outgrow initial choices
• Team skills develop

Design for replaceability.

Modern Data Stack Challenges

Tool Sprawl

Many components mean many tools:

• Multiple contracts and vendors
• Different interfaces to learn
• Integration maintenance burden
• Potential security surface increase

Manage sprawl consciously.

Skills Requirements

Operating the stack requires breadth:

• Cloud infrastructure knowledge
• SQL fluency
• Understanding of each tool
• Integration and debugging skills

Invest in team development.

Cost Management

Usage-based pricing can surprise:

• Warehouse compute costs
• Data volume charges
• Per-user licensing
• Integration tool fees

Monitor costs and optimize usage.

Data Governance

Distributed architecture complicates governance:

• Data in multiple places
• Access control across tools
• Lineage across boundaries
• Compliance complexity

Establish governance practices early.

Modern Data Stack and AI

The modern data stack creates infrastructure for AI analytics:

Clean data: Transformation ensures data quality.

Accessible data: Warehouses provide SQL access for AI.

Contextual metadata: Semantic layers provide business meaning.

Operational integration: Reverse ETL activates AI insights.

Organizations with mature modern data stacks can deploy AI analytics more easily because the foundational infrastructure already exists.

Getting Started

Organizations adopting the modern data stack should:

1. Assess current state: What infrastructure exists today?
2. Define requirements: What must the stack enable?
3. Select core components: Warehouse, ingestion, transformation, BI
4. Implement incrementally: Start with highest-value use cases
5. Expand thoughtfully: Add components as needs emerge
6. Optimize continuously: Monitor costs, performance, and adoption

The modern data stack transforms data infrastructure from a monolithic system to a flexible, evolvable platform that grows with organizational needs.