What is a Modern Data Stack? A Practical Guide for 2026
The “modern data stack” (MDS) refers to a collection of cloud-native, best-in-class tools that work together to ingest, transform, store, and analyze data. Unlike monolithic enterprise platforms, the modern data stack uses specialized tools for each layer, connected through standard interfaces.
The Layers of a Modern Data Stack
1. Data Ingestion (Extract & Load)
This layer moves data from source systems into your data warehouse. Modern ingestion tools are fully managed, requiring minimal maintenance.
Popular tools:
- Fivetran — Automated, pre-built connectors for hundreds of sources
- Matillion — Visual ELT with complex transformation capabilities
- Airbyte — Open-source alternative with 600+ connectors across cloud and self-hosted deployments
- Snowflake Openflow — Snowflake’s native ingestion service supporting batch, streaming, and unstructured data from hundreds of sources
2. Data Warehouse (Storage & Compute)
The central platform where all your data lives and is queried. Cloud data warehouses separate storage from compute, enabling independent scaling.
Popular tools:
- Snowflake — Our recommended platform for most organizations, thanks to its unparalleled flexibility in independent compute and storage scaling, competitive pricing, and availability on all major cloud providers
- BigQuery — Google’s serverless data warehouse
- Redshift — AWS-native option with deep AWS integration
3. Data Transformation (Transform)
This layer reshapes raw data into clean, business-ready models. The “T” in ELT happens inside the warehouse using SQL.
Raw data ingested from source systems needs to be reshaped into analytics-ready models. With dbt, developers use SQL enhanced with macros and functions to perform sequential transformations from raw data through staging, intermediate, and analytics layers, producing optimized star schemas that serve data to end-use applications.
Popular tools:
- dbt — The standard for SQL-based transformations with testing and documentation
- Coalesce — Visual, column-aware transformations built for Snowflake, particularly well-suited for teams transitioning from legacy ETL tools or with mixed technical backgrounds
4. Business Intelligence (Analyze & Visualize)
This layer makes data accessible to business users through dashboards, reports, and self-service exploration.
Popular tools:
- Omni — Our top recommendation for teams that value semantic modeling and a code-first approach. Built by former Looker team members, Omni delivers centralized metrics definitions, version-controlled models, and strong governance with a modern interface and genuinely useful self-service capabilities
- Tableau / PowerBI — Strong choices for organizations already invested in those ecosystems, with large communities and broad market adoption
- Sigma Computing — Spreadsheet-like interface for cloud data
5. Data Governance & Cataloging
This layer ensures data quality, security, and discoverability across the organization.
Popular tools:
- Dataedo — Data catalog and documentation with a practical, no-frills approach to metadata management
- Observe (Snowflake) — AI-powered observability platform (acquired by Snowflake in 2026 for unified data and system monitoring)
- Dataedo — Data catalog and documentation
Why the Modern Data Stack Matters
Compared to Legacy Approaches
| Aspect | Legacy (On-Prem) | Modern Data Stack |
|---|---|---|
| Infrastructure | Self-managed servers | Fully managed cloud |
| Scaling | Weeks to provision | Minutes to scale |
| Cost model | Large upfront CapEx | Pay-as-you-go OpEx |
| Maintenance | Full-time DBA team | Minimal operations |
| Time to value | Months | Days to weeks |
Key Benefits
- Modularity: Swap any tool without rebuilding everything
- Scalability: Each layer scales independently
- Speed: Get from data to insights in days, not months
- Cost efficiency: Pay only for what you use
- Best-in-class: Use the best tool for each job
Supporting Practices
The core layers above are essential, but a mature data stack also needs supporting practices and tooling to be resilient, maintainable, and scalable.
Source Code Control
Version control is non-negotiable for any data project. It preserves the integrity of your work and enables collaboration without fear of overwriting changes.
Our recommendation: Git with GitHub. Several BI tools, including Omni and PowerBI, also offer source code control integrations that protect the BI development workflow — take advantage of them.
Orchestration & Automation
The goal is to orchestrate and automate the flow of data from source systems through the warehouse and into BI tools so that manual intervention isn’t required. Many tools in the stack offer scheduling and trigger capabilities, but purpose-built orchestration tools tie everything together.
Our recommendation: GitHub Actions as a low-to-no-cost orchestration solution, thanks to its accessible API, programmatic flexibility, and broad community support.
CI/CD (Continuous Integration / Continuous Deployment)
CI/CD automates the process of integrating code changes and testing them before they reach production. This is the logical next step after implementing source code control and makes your data platform more resilient against accidental downtime.
Our recommendation: Use any purpose-built CI/CD tooling integrated into your project stack — automated testing, deployment gates, and failure notifications are table stakes.
Infrastructure as Code (IaC)
Infrastructure as Code means scripting the deployment of cloud resources through code rather than manual configuration. This enables faster setup, easier scaling, repeatable environments, and auditability.
Our recommendation: Terraform. HashiCorp supports all major cloud providers (AWS, Azure, GCP) and data platforms including Snowflake.
Master Data Management (MDM)
MDM formalizes the business practices surrounding how a company handles its data — defining processes, governance, policies, standards, and tooling. Often this starts by simply documenting how the company currently handles their data (who does what, when, and why).
As data volumes grow and projects increase in complexity, a defined MDM program becomes imperative.
Recommended reading: Non-Invasive Data Governance by Robert S. Seiner — an accessible and actionable primer on the topic.
AI-Assisted Development
Large Language Models have fundamentally changed how data teams write and maintain code. AI coding assistants accelerate development while improving code quality.
Key tools:
- Claude Code (Anthropic) — Command-line AI assistant that excels at understanding complex codebases, writing SQL transformations, debugging dbt models, and explaining legacy code
- GitHub Copilot — Real-time code suggestions in VS Code and other IDEs, especially helpful for repetitive SQL patterns and boilerplate configuration
- Cursor — AI-native code editor combining VS Code familiarity with deeper AI integration
How we use AI assistants at CDC:
- Accelerating dbt development — generating initial model SQL, writing tests, creating documentation
- SQL optimization — reviewing query plans and suggesting performance improvements
- Code review — catching potential issues before they reach production
- Legacy code understanding — quickly comprehending undocumented transformations inherited from previous systems
The key is treating AI as a skilled pair programmer rather than a replacement for data engineering expertise. Human judgment remains essential for understanding business context, validating outputs, and making architectural decisions.
A Practical Example Stack
Here’s what a modern data stack looks like for a typical mid-market company:
- Fivetran pulls data from Salesforce, HubSpot, Stripe, and PostgreSQL
- Snowflake stores everything in a single cloud data warehouse
- Coalesce transforms raw data into clean business models with visual, column-aware transformations — more productive than dbt for our team, paying for itself in time saved
- Omni provides dashboards and self-service analytics for the business team
- Dataedo catalogs all data assets with documentation and lineage
Total setup time: 2-4 weeks for a production-ready analytics platform.
Getting Started
The most important decision is choosing your data warehouse — it’s the foundation everything else connects to. For most organizations, we recommend starting with Snowflake for its simplicity, performance, and ecosystem support.
From there, layer in ingestion (Fivetran or Matillion), transformation (dbt or Coalesce), and BI (Omni or Looker) based on your team’s skills and requirements.
Need Help Building Your Stack?
We’ve helped over 20 companies implement modern data stacks through our platform implementation and data engineering services, with 20+ Snowflake implementations, from scratch and migrate from legacy platforms. Schedule a free consultation to discuss your data strategy.