The Modern Data Stack Explained: What to Know in 2025

Key takeaways

• Organizations are transforming data stacks from legacy on-premises systems to modern, flexible, cloud-based solutions that align with the need for speed and supporting AI initiatives facilitated by decentralization and scalability.

• Modern data stacks enable faster insights through real-time data access from cloud data warehouses, which drives greater agility and quicker access to valuable data from growing data stores.

• By democratizing access to data, enhancing data governance, and improving data quality, modern data stacks enable organizations of all sizes to make more informed decisions, power emerging AI innovations for trusted results, and build a stronger data culture for a snowball effect on growth. 

Chances are, you’ve heard of the term “modern data stack.” But you may not know what it really means because many people use it as a buzzword.

In fact, it isn’t all that confusing, and understanding what it means can have huge benefits for your organization.

In this article, we will explore the modern data stack in detail, list some benefits, and discuss what the future holds.

What is the modern data stack? A definition and core components

The modern data stack is a combination of various software tools, processes, and strategies used to collect, process, and store data on a well-integrated cloud-based data platform. Building an effective data stack is known to have benefits in handling data due to the added robustness, speed, and scalability over traditional methods.

Components of a modern data stack.

A typical modern data stack consists of the following:

• Extract, load, Transform (ELT) tools

• Data ingestion/integration services

• Data migration tools

• A data warehouse

• Data orchestration tools

• A data catalog

• Business intelligence (BI) platforms

• Reverse ETL tools

These tools are used to manage big data for enterprises. Big data is typically defined as data that is too large or complex to be processed by traditional means, which means you need specialized tools and infrastructures to build your own data stack.

The evolution of the modern data stack: From on-premises to the cloud

The rise of cloud computing and cloud data warehousing has catalyzed the growth of the modern data stack in enterprise organizations. The modern data stack is also the consequence of a shift in analysis workflow, from extract, transform, load (ETL) to extract, load, transform (ELT). This simple change in the process has allowed for connectivity and more flexible usage of different data services within the modern data stack.

This shift also addresses a growing demand for data access, which the modern data stack enables through cloud-based migrations, services, and integrations. There has also been a paradigm shift toward agile analytics and flexible options, where data assets can be moved around more quickly and easily instead of being locked to a single vendor.

The modern data stack started to gain popularity in the early 2010s as companies began to realize the benefits of big data and the unnecessary expense of the traditional on-premises data stack. While the term “big data” may have fallen behind more current terms like “agentic AI” and “digital sovereignty” it is still useful when describing the challenges enterprises face with mounting data volumes.

With the explosion of cloud computing power and the equally dramatic drop in usage prices, cloud computing has become much faster and cheaper than on-premises methods, making it the core technology driving the structure of the modern data stack.

The modern data stack began when some of the popular cloud data warehouses such as BigQuery, Redshift, and Snowflake began to appear in the early 2010s. Later, BI tools such as Looker and Tableau arrived on the data scene. Realizing that many organizations wanted these to be well integrated, data ingestion tools like Stitch and Fivetran provided that service. Other tools, such as MongoDB, Cassandra, and Elasticsearch, were also developed around this time to provide alternative solutions for managing big data and rounding out the modern data stack.

The ELT revolution: How modern data stacks are shifting from ETL

Understanding the impact of emerging cloud data warehouses like Snowflake and Databricks on today’s modern data stack and analytics requires context on how data has been traditionally moved from sources to processing and analytics tools. 

Let’s begin with a quick definition of the old and new approaches best known as the confusingly similar ETL and ELT. More confusingly, they do the same thing—collecting and preparing data for analysis—but in different ways.

You can learn more in the article “ETL vs. ELT: What’s the Difference?”, but here’s how they differ:

• Extract, Transform, Load (ETL) - Source data is gathered, transformed, and then loaded into a target system. 

• Extract, Load, Transform (ELT) - Source data arrives in a target data warehouse such as Amazon Redshift, Snowflake, or SQL Server and then is transformed or enriched at a later stage to meet specific project requirements. 

In the past, data movement was defined by ETL. Data would be pulled from various sources, organized into, say, a table, and loaded into a data warehouse for mass consumption. This was time-consuming and the growing popularity of cloud data warehouses compelled people to rethink this process. As real-time data collection surged and data volumes grew, so too did the desire to harness the power of real-time analytics across bigger and bigger data stores.

So, experts asked, “What if we could load raw data into a warehouse and then empower people to transform it for their own unique needs?” This would enable enterprises to move more quickly as analysts extracted unique insights faster.

Today, data integration platforms like Rivery do just that. By pushing the T to the last step in the process, such products have revolutionized how data is understood and analyzed. Furthermore, they have radically shifted the way data flows through big organizations. The modern data stack reflects this shift.

Additional differences between ETL and ELT are summarized in the following table:

AI and the modern data stack: Why it’s so critical to AI success

A modern data stack acts as a backbone of AI and machine learning (ML) technologies, since they require so much data to be effective. With the right tools and architecture in place to efficiently handle, process, and analyze data, AI systems will have a robust data stack to function at their best.

A modern data stack has the scalability of the cloud to manage large data volumes quickly and efficiently. Real-time processing capabilities also allow AI-created insights to be output quickly for use cases such as fraud detection and personalization.

High data quality and effective governance are also benefits of a modern data stack for AI applications. This leads to more accurate AI predictions, flexibility to adapt to changing AI technologies, and fast integration of new data sources—all of which are crucial to AI effectiveness and return on investment.

For example, NTT DOCOMO, Japan's largest mobile communications provider, used a modern approach to govern its data and increase trust in data-driven AI outcomes. 

Modern data stack vs. legacy data stack: Key differences explained

Let’s break down some of the key benefits of a modern data stack as compared with a more traditional data stack. 

• A modern data stack is typically more scalable, flexible, and efficient than a legacy data stack. 

• A modern data stack relies on cloud computing, whereas a legacy data stack stores data on servers instead of in the cloud. 

• A modern data stack provides access for more data professionals than a legacy data stack.

A legacy data stack usually refers to the traditional relational database management system (RDBMS), which uses a structured query language (SQL) to store and process data. While an RDBMS can still be used in a modern data stack, it is not as common because it is not as well-suited for managing big data. SQL, however, remains a popular query language for both legacy and modern data stacks.

Unlock business value: The key benefits of a modern data stack

There are many benefits of using a modern data stack, some of which include:

1. Increased data scalability

With a modern data stack, it is easier to scale up or down as needed. Various tools in the stack can be used together or separately, depending on the needs of the company. The elastic capabilities of the cloud help organizations use the needed computing resources on demand for important data tasks. When the jobs finish, the resources can return to a normal state, minimizing compute costs.

2. Increased business flexibility and agility

A modern data stack is also more flexible than a legacy data stack. Various tools can be used in different ways to meet the specific needs of the company. The services within a data stack can be added or removed as needed. Many of the services have consumption-based pricing which allows companies to not have huge software acquisition costs upfront as they begin migrating to the cloud. Data assets are also not fixed to a particular vendor.

3. Enhanced analytics and data operations efficiency

A modern data stack can also be more efficient than a legacy data stack. The tools in the stack are designed to work together in a cloud platform, which can help to save time and resources. Powered by the cloud, more data professionals have access to the data, too. For example, data analysts have access to the data warehouse using BI tools like Tableau and data scientists have access to data science tools, such as Dataiku.

4. Improved and expanded data culture

A modern data stack can help to create a better data culture within an organization. The various tools are designed with usability in mind. This makes it easier for employees to access and use data, regardless of their technical expertise. Furthermore, the flexibility of a modern data stack means employees are not restricted to using a particular tool. They can choose the tool that best meets their needs.

Employees can benefit from an enhanced data culture in many ways including:

• Data search and discovery — Employees can find relevant data for faster decision-making.

• Data literacy — Employees can interpret and analyze data to draw logical conclusions; they can also identify subject matter experts best equipped to educate on specific data assets.

• Data governance — Data is appropriately managed, PII (personally identifiable information) is masked, and regulations governing certain kinds of data are visible within workflows, so employees use the right data in the right ways.

Is the modern data stack right for you? Who can benefit from it?

The modern data stack is well-suited for companies with large amounts of data—big data, as they say. In the past, this was restricted to enterprise-sized organizations, but increasingly, even small and mid-sized businesses manage large data landscapes and will benefit from a modern data stack.

For any organization with multiple data teams and functions, a modern data stack is the best option, as it can facilitate collaboration, reduce costs, and improve data-driven outcomes.

A modern data stack can also streamline data management and IT bottlenecks, accelerating access to various teams that require data. These teams include:

• Data analysts

• Business analysts

• Data scientists

• Software engineers

• Web developers

• Digital analysts

• Cloud engineers

• Data engineers

• Business leaders

Smaller companies that want to improve scalability, flexibility, and efficiency are also embracing the modern data stack. Really, a modern data stack can be adopted by any company that wants to improve its data management efforts.

Essential features of the modern data stack: A component-by-component guide

If you’re looking to modernize your data stack, there are a few things to keep in mind:

1. Determine which services and tools you need and how they will work together.

2. Find a data platform that can support your modern data stack.

3. Consider how you will migrate data from your legacy system to your new modern data stack.

4. Plan to train your team on how to use the new tools and services in your modern data stack.

While it may seem like a lot of work, modernizing your data stack can be a great way to improve your company’s data management. 

As you embark on your journey with step #1 above, here’s what to look for in the different components required to assemble a modern data stack.

Data warehouses:

A data warehouse, data lake, or data lakehouse is a central repository for all your company’s data. You should look for a data warehouse that is scalable, flexible, and efficient. Popular cloud data warehouses today include Snowflake, Databricks, and BigQuery. If your organization is large, you definitely need to look for robustness. Good data warehouses should be reliable.

Data science:

Data science platforms like Dataiku are used to analyze and understand your data. You should look for data science tools that are easy to use and that offer a variety of features. Great data science tools will assist data scientists and citizen data scientists in testing and training datasets for developing models, and ultimately for deploying them. 

Business intelligence:

Business intelligence (BI) tools are used to visualize your data. You should pick those that allow for easy integration and can create beautiful data visualizations. Examples of BI tools include Looker, Power BI, and Tableau. These help data analysts visualize key insights that can help you make better data-backed decisions.

ELT data transformation:

ELT data transformation tools are used to extract, load, and transform data. You should choose those that are easy to use and offer a variety of features. A great ELT tool will help you automate your data pipelines and make it easier to manage your data and serve well in producing regular reports that measure key performance indicators in your organization. Examples of data transformation tools include dbt and dataform.

Data ingestion:

Data ingestion tools are used to collect your data and incorporate them into your data warehouse. You should choose those that are easy to use and offer a variety of connections to multiple sources at once and can automatically parse and cleanse your data so that it’s ready for analysis. A great data ingestion tool will help you collect your data from a variety of sources and then automatically cleanse and prepare it for analysis. An example of a data ingestion tool is Fivetran.

Reverse ETL:

Reverse ETL tools are used to send data back into third-party applications. It’s always helpful if the reverse ETL tool can automatically map your data from your new modern data stack to your SaaS app. Examples of reverse ETL tools include Weld, Census, and Hightouch.

Data orchestration:

Data orchestration tools are used to manage and monitor your data pipelines. You should look for those that offer a variety of features that assist in using Python scripts that run automatically and process data. Examples of data orchestration tools are Prefect or Apache Airflow.

How a data mesh works with a modern data stack

While a modern data stack provides flexibility and scalability, a data mesh approach can add a level of decentralization to democratize data ownership and management. By using tools like cloud data warehouses and APIs in a data mesh, organizations can give teams power over data pipelines and data products to accelerate innovation. 

The benefits of a data mesh are proven because they push data outcomes closer to data consumers’ needs, such as improving the customer experience, enhancing patient care, and enhancing risk management. Implementing a data mesh within a modern data stack brings self-service capabilities on top of a shared infrastructure of a data catalog, data discovery platform, and other tools.

By combining the decentralized data mesh approach with the structure of a modern data stack, organizations can wring more value from data while keeping pace with modern innovations like AI. 

For a real-world example of how organizations use a data mesh, learn how Fifth Third Bank used a data mesh to decentralize its approach to data ownership.

Building your modern data stack: A step-by-step implementation guide

Building a modern data stack isn’t as difficult as it sounds. However, it will take time to understand how all the data comes together. 

Let’s go through this step-by-step guide to building a modern data stack:

1) Deploy a data warehouse

There are many data warehouses available on the market. Do your research and pick one that will fit your company’s needs. For example, if you have a lot of data, you’ll need a data warehouse of the appropriate size to store it.

To integrate data easily, choose a data warehouse that offers secured connectors that encrypt your data when in transit. This is especially important when moving data from on-premise sources to the cloud.

Cloud-based data warehouses are usually cheaper and easier to set up. However, on-premises data warehouses offer more control over your data. Most organizations today choose a cloud data warehouse partner like Snowflake as part of their modern data stack. However, some firms in heavily regulated industries, such as healthcare or banking, may still need to leverage on-premises data storage for compliance reasons.

Popular cloud-based data warehouses include Amazon Redshift, Google BigQuery, Snowflake, and Databricks.These cloud data warehouses work well with a good metadata management strategy, which includes implementing a data catalog like Alation.

2) Use a data ingestion tool to connect your data sources

Now that you have a data warehouse, you need to get data into it. The best way to do this is with a data ingestion tool. There are many data ingestion tools available on the market. Depending on your data sources, connecting with them can be done with an API or a connector. Some data sources will require you to write code to connect them to your data ingestion tool. Stitch, Airbyte, or Fivetran are popular solutions for data ingestion.

Database replication can also help you move on-premise data to the cloud. Database replication uses change-data-capture techniques to move data changes as they occur, providing a highly performant means of synching changes to a cloud warehouse. Examples of database replication tools include Fivetran and QlikReplicate.

3) Use a data transformation tool to clean and prepare your data

After your data is in your data warehouse, you need to clean and prepare it for analysis. The best way to do this is with a data transformation tool.

There are many data transformation tools available on the market such as dbt, Dataform, and Dataiku.

4) Visualize data using business intelligence tools

After your data is clean and prepared, you can start visualizing it using BI tools to provide interactive visualizations that multiple stakeholders can use. Tableau has very good permissions and Tableau Server is a good cloud-based platform for stakeholders to easily access dashboards. Common BI tools in the modern data stack include Looker and Google Data Studio.

5) Use reverse ETL tools to send data to third-party apps

After you’ve been using your modern data stack for a while, it’s not unlikely that you’ll need to send data to third-party apps, such as HubSpot or Zendesk. The best way to do this is with reverse ETL tools. These tools will help you map your data back so you won’t lose track of it in your third-party SaaS apps. Examples of reverse ETL tools are Hightouch and Census.

6) Teach your organization the tools in the modern data stack

The modern data stack can be a lot to take in at first. But don’t worry, once you get the hang of it, it’ll be a breeze. To help your organization get started, hold training sessions or workshops. You can also create documentation or video tutorials. Courses and certifications in Power BI, Tableau, and cloud-based data warehouses can be really helpful in this.

Building a modern data stack is essential for any company that wants to make data-driven decisions. By following these steps, you’ll be well on your way to putting together a modern data stack that works for you.

The future of the modern data stack: Trends, innovations, and predictions

The modern data stack is here to stay. As more and more companies adopt it, the modern data stack will only continue to evolve and become even more powerful in the years to come.

In the future, we can expect to see even more innovation in the modern data stack. This will help companies to better scale, manage, and analyze their data.

We can also expect to see the modern data stack becoming more and more accessible to businesses of all sizes. This will happen when the data skills of professionals rise and the cost of analyzing data falls drastically with cheaper cloud computing and Artificial Intelligence services.

As the modern data stack evolves, so will the way we use data to make decisions in our businesses. The modern data stack is here to stay and it’s only going to get better with time.

Conclusion

The modern data stack is a powerful tool that can help companies make better data-driven decisions. If you’re not already using one, now is the time to start putting together a modern data stack that works for you.

If you’re still using a legacy data stack with on-premises components, consider adopting a modern data stack. It is not merely a rising trend; there are multiple benefits to using it, as discussed above.

In the future, we can expect to see even more innovation in the modern data stack. This will help companies to better scale, manage, and analyze their data.

Curious to learn how a data catalog can support your modern data stack? Book a demo to see for yourself.