Understanding Modern Data Stack

(Also watch this video: Introduction to Modern Data Stack)

Data is truly transformative. However, organizations can uncover value only if they can effectively manage data through its lifecycle. A modern data stack is a collection of pluggable tools that help enterprises collect, store, process, share, distribute, and protect data, ultimately helping companies extract more value from their data.

As businesses evolve, what data is collected and how it is used are changing. Data's growing role creates many challenges, including delivering scalable infrastructure, ensuring quality, and protecting data. However, by modernizing their data stack, organizations can address these new and unique data challenges in a way that powers innovation and enhances business productivity.

Characteristics of a modern data stack

Here are the three characteristics of a modern data stack.


Perhaps the most fundamental difference between a legacy and a modern data stack is that a modern data stack is hosted in the cloud. So, organizations can quickly address growing data needs without bearing the financial burdens or long lead time typically associated with traditional scaling options.

Easy to deploy

In addition, a modern data stack requires very little or basic technical configuration by the user. As a result, teams can scale and immediately get to speed with different stack layers.

Plug and play

One of the key characteristics of a modern data stack is that it is a pluggable stack of tools. Companies can pick the best-of-breed services to address their needs.

Layers of a modern data stack

Let's look at key components of a modern data stack. Here is a simple rundown of a modern data stack's core tools and capabilities. (Please note: The stack is evolving, and new layers are added.)

Data Ingestion

This includes pipelines to bring data from various sources, including websites, transaction systems, and various apps, into your central data storage.


Raw data must go through a set of processes, including cleaning, normalizing, joining, and summarizing, to make it consumable across the company. This layer offers frameworks and common features to extract, transform, and load data.

Data storage

Many cloud solutions, such as Snowflake, AWS Redshift, Databricks, etc., offer data warehouses, data lakes, and lakehouses that help businesses to store and process data.

Semantic or Metric Layer

Instead of each BI tool writing SQL queries to fetch data, which could lead to different diverging definitions, the semantic layer provides defined APIs that provide the data objects. Semantic Layer provides APIs that convert metric computation requests into SQL queries and run them against the data warehouse. The semantic layer achieves consistent reporting by sitting between data models and BI tools.

Reverse ETL

These tools move transformed data from the data storage back to third-party business tools such as sales operations and CRMs. Delivering insights back to operational tools improves the velocity and quality of operational decisions.

Business Intelligence Tools

The visualization layer helps teams build and deliver insights into various parts of the business. This layer includes tools that generate reports, dashboards, and alerts.

Data Management, Quality

Helps data engineers to observe data in motion and at rest to ensure data quality. Manage data catalog, lineage, and other metadata to manage data lifecycle.

Privacy and governance

With growing privacy laws, businesses must invest in tools that help them protect data and privacy regulations. Tools like Protecto can help you discover sensitive data, data security gaps, and data privacy vulnerabilities.

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