Data isn’t just growing — it’s exploding. But wrangling it shouldn’t feel like rocket science. That’s where the Modern Data Stack comes in.

Shreya Mehta
Jun 6
5 min read

What is the Modern Data Stack (MDS)?

The Modern Data Stack (MDS) is like the ultimate DIY toolkit for working with data. It’s made up of specialized tools that help you pull data from different places, store it safely, clean it up, and turn it into something useful — like insights, reports, or even automated actions.

The best part? It’s modular. Each tool handles one job really well, and you can mix and match them based on what you need — no one-size-fits-all approach here.

Whether you’re wrangling messy spreadsheets or syncing live dashboards, MDS gives you the flexibility and power to build a data setup that actually works for you — and can grow with you.

Let’s unpack the Modern Data Stack — one layer at a time.

Layer	Purpose	Example Tools
Data Ingestion	Pulls data from various sources (APIs, apps, DBs)	Fivetran, Airbyte, Stitch
Data Storage	Central warehouse where raw data lives	Snowflake, BigQuery, Amazon Redshift
Data Transformation	Clean, model, and prepare data for analysis (usually in SQL)	dbt (data build tool)
Orchestration	Schedule and monitor data workflows	Airflow, Prefect
Business Intelligence (BI)	Analyze and visualize data	Looker, Tableau, Power BI
Reverse ETL	Push transformed data back to apps (like CRMs or marketing tools)	Census, Hightouch
Data Observability	Monitor data quality, freshness, lineage	Monte Carlo, Datafold

Data Ingestion

The process of bringing data from different sources (like apps, websites, databases, CRMs, etc.) into a central place — usually a data warehouse.

It allows you to collect data from multiple tools and sources that your client’s business might be using.

Example:

You have data in SAP, Shopify, Google Ads, and Stripe.
A tool like Fivetran or Airbyte automatically pulls data from these tools and loads it into your warehouse (like BigQuery,Snowflake or databricks).

Data Storage

Data Storage refers to where the ingested (collected) data is kept and managed — usually in a cloud data warehouse or data lake.

This is the central place where data lives, and where analysts and tools can access it. It ensures data is safe, scalable, and accessible to everyone who needs it.

Examples:

Cloud data warehouses: Snowflake, Google BigQuery, Amazon Redshift
Data lakes: Amazon S3, Databricks, Delta Lake

Data Transformation

Data transformation refers to the process of converting data from its original format or structure into a new format that is better suited for analysis, querying, or integration.

Data transformation means changing raw data (often messy, inconsistent, or unstructured) into a clean, structured, and usable format. It makes it analysis-ready, so teams can make informed decisions faster.

Example:

Raw Data	Transformation	Transformed Data
"01/06/2025"	Convert to ISO date format	"2025-06-01"
"Yes" / "No"	Convert to Boolean	TRUE / FALSE
name = "john doe"	Capitalize properly	name = "John Doe"
Multiple columns (first name, last name)	Combine into full name	John Doe
Currency in INR	Convert to USD	1000 INR → 12.5 USD

Orchestration

Automation is about setting up a program inside a tool — for example, to ingest data — so that manual effort is reduced. Orchestration takes it a step further: it connects and coordinates multiple automations across different tools in the Modern Data Stack (MDS), ensuring that each task (like loading, transforming, or ingesting data) runs at the right time and with all the required inputs in place to succeed.

For Example: A transform automation in a transforming tool should only run when the ingestion automation in ingesting tool has completed.

You can build pipelines to achieve this.

Without orchestration, you’d have to manually run scripts every time data needs to move or change. Orchestration ensures that data pipelines run automatically, reliably, and on time.

Popular orchestration tools:

Airflow
Prefect
Dagster

Pipelines:

In the Modern Data Stack (MDS), the term "pipelines" refers to data pipelines — automated processes that move, transform, and load data from one place to another.

Pipeline Type	What It Does	Example Use Case
Ingestion Pipelines	Bring raw data from sources (e.g., APIs, apps) into a data warehouse	Sync Salesforce CRM(Campaign) or SAP (Sales, Inventory) data into Snowflake
Transformation Pipelines	Clean, format, and shape the data using pyspark or dbt	Convert date formats, create revenue models
Modeling Pipelines	Create reusable data models for reporting or ML	Build customer churn or lifetime value models
Reverse ETL Pipelines	Send processed data back into operational tools	Push churn scores from warehouse to HubSpot / CleverTap/ Braze/ Moengage
Orchestration Pipelines	Schedule and monitor jobs/tasks across the stack	Run ingestion at 2 AM, then transform at 3 AM with exception handling

Business Intelligence (BI)

Business Intelligence (BI) refers to the tools and processes that help you analyze data and turn it into actionable insights through reports, dashboards, and visualizations.

Popular BI tools:

Looker
Power BI
Tableau
Metabase
Mode

BI tools sit at the end of the modern data stack pipeline. They let business teams use data to make decisions without needing to write code or SQL.

Reverse ETL

Reverse ETL is the final step in the data pipeline — it takes clean, transformed data from your warehouse and delivers it into operational tools like CRMs, marketing platforms, or support systems.

Instead of keeping insights locked away in dashboards, Reverse ETL puts them to work — syncing modeled data into platforms like Braze, MoEngage, or CleverTap, so teams can trigger personalized engagement at scale.

Popular Reverse ETL tools:

Hightouch
Census
RudderStack

While most of MDS focuses on getting data in for analysis, Reverse ETL makes data go out where action happens — enabling data-driven operations across departments.

Data Observability

Data Observability is the ability to monitor, track, and ensure the health of your data pipelines.

It’s like having a health check system for your data. It alerts you when something breaks, like missing data, sudden drops in values, or delayed updates — so you can catch issues before they cause problems.

What it checks:

Freshness – Is the data up-to-date?
Volume – Is the expected amount of data arriving?
Schema – Did the structure of the data change unexpectedly?
Accuracy – Are the numbers or formats suspicious?
Lineage – Where did the data come from and how did it change?

Popular tools:

Monte Carlo
Databand
Bigeye
Metaplane

Let’s face it — modern data pipelines aren’t just complex, they’re chaotic. And without observability, it’s like flying blind. One broken link and suddenly your reports are lying to you. Not cool.

Thankfully, the Modern Data Stack stepped in to clean things up — bringing speed, flexibility, and a whole bunch of slick tools to the table. But here’s the twist: the stack isn’t done evolving.

Data’s growing faster than ever, and the need for real-time, reliable decisions is pushing us into the next big leap — AI-powered everything.

From auto-generating models to spotting weird data patterns before your dashboard even blinks, AI is quietly becoming the smartest member of the data team.

Curious what that future looks like? We’ve got you. In our next blog, we’ll explore how AI is reshaping the Modern Data Stack — and why your data stack might soon be smarter than you think.