Chances are, you’ve heard of the term “modern data stack” before.
But you may not actually know what it really meant because many people use it as a buzzword!
In actual fact, it isn’t all that confusing at all, and understanding what it means can have huge benefits for your organization.
In this article, I will explain the modern data stack in detail, list some benefits, and discuss what the future holds.
What Is the Modern Data Stack?
The modern data stack is a combination of various software tools used to collect, process, and store data on a well-integrated cloud-based data platform. It is known to have benefits in handling data due to its robustness, speed, and scalability.
A typical modern data stack consists of the following:
- A data warehouse
- Extract, load, Transform (ELT) tools
- Data ingestion/integration services
- Reverse ETL tools
- Data orchestration tools
- Business intelligence (BI) platforms
These tools are used to manage big data, which is defined as data that is too large or complex to be processed by traditional means.
How Did the Modern Data Stack Get Started?
The rise of cloud computing and cloud data warehousing has catalyzed the growth of the modern data stack. The modern data stack is also the consequence of a shift in analysis workflow, fromextract, 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 data stack.
This shift addresses a growing demand for data access, which the modern data stack enables with cloud-based services and integration.
There has also been a paradigm shift toward agile analytics and flexible options, where data assets can be moved around more quickly and easily, and not locked into 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.
Most importantly, cloud computing has gotten much faster and cheaper, emerging as the core technology driving the development of the modern data stack.
It began when some of the popular cloud data warehouses — such as BigQuery, Redshift, and Snowflake — started to appear in the early 2010s.
Later, BI tools such as Chartio, Looker, and Tableau arrived on the data scene.
Realizing that many 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.
A Note on the Shift from ETL to ELT
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 moved. In the past, data movement was defined by ETL: extract, transform, and load. Data would be pulled from various sources, organized into, say, a table, and loaded into a data warehouse for mass consumption. This was not only time-consuming, but the growing popularity of cloud data warehouses compelled people to rethink this process.
As real-time data collection surged, so too did the desire to harness the power of real-time analytics.
What if, experts asked, you could load raw data into a warehouse, and then empower people to transform it for their own unique needs?
You’d move more quickly, and enable analysts to extract unique insights.
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.
The need for better insights, faster, has contributed to the rise of the modern data stack. (TDWI)
What Separates a Modern Data Stack from a Legacy 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. Modern data stacks provide 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.
What Are the Benefits of a Modern Data Stack?
There are many benefits of using a modern data stack, which include:
1. Increased 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. Improved Flexibility
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 not fixed to a particular vendor.
3. Enhanced 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 cloud computing, more data professionals have access to the data, too.
Data analysts have access to the data warehouse using BI tools like Tableau; data scientists have access to data science tools, such as Dataiku.
4. Better 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 a good data culture in these ways:
- Data search and discovery — Employees can find relevant data for just-in-time 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.
Data governance is a key use case of the modern data stack. (TDWI)
Who Can Adopt the Modern Data Stack?
The modern data stack is well-suited for companies with large amounts of data. In the past, this was restricted to enterprise-sized organizations, but increasingly, even smaller businesses face large data landscapes and will benefit from a modern data stack.
More particularly, if you have multiple functions of data teams across your organization, a modern data stack is the way to go as it can facilitate collaboration.
A modern data stack can streamline IT bottlenecks, accelerating access to various teams that require data:
- Data analysts
- Business analysts
- Data scientists
- Software engineers
- Web developers
- Digital analysts
- Cloud engineers
- Data engineers
- Business leaders
Smaller companies that want to improve their scalability, flexibility, and efficiency are also embracing the modern data stack.
Basically, a modern data stack can be adopted by any company that wants to improve its data management.
If you’re looking to modernize your data stack, there are a few things to keep in mind. First, you’ll need to determine which services and tools you need and how they will work together.
Second, you’ll need to find a data platform that can support your modern data stack.
Third, you’ll need to consider how you will migrate your data from your legacy system to your new modern data stack.
And finally, you’ll need 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.
What Should I Look For in Each Component of the Modern Data Stack?
A data warehouse 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 Tools:
Data science tools 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.
As data science is growing in popularity and importance, if your organization uses data science, you’ll need to pay more attention to picking the right tools for this.
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. An example of a data science tool is Dataiku.
Business Intelligence Tools:
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 Tools:
ELT data transformation tools are used to extract, load, and transform your data. You should choose those that are easy to use and offer a variety of features.
It’s always frustrating to not have high flexibility in transforming data of various types.
A great ELT tool will help you automate your data pipelines and make it easier to manage your data.
These automated pipelines will 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 Tools:
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.
It’s always helpful if the data ingestion tool 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 Tools:
Reverse ETL tools are used to send data back into 3rd party SaaS applications that you use. It’s always helpful if the reverse ETL tool can automatically map your data from your new modern data stack to your SaaS app.
Getting a good reverse ETL tool can help save costs and scale the process of mapping back to 3rd party apps.
Examples of reverse ETL tools include Weld or Census, or Hightouch.
Data Orchestration Tools:
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.
Better yet if you learn how to use GitHub to do version control so you can always revert back to older versions of code.
If you don’t already have data transformation tools, you need one that works to automate data analyses and keep them robust.
Examples of data orchestration tools are Prefect or Apache Airflow.
How Can I Build a Modern Data Stack?
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:
1) Get 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.
If you want to be able to integrate your data easily, you’ll need 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..
There are two types of data warehouses: on-premises and cloud-based. On-premises data warehouses are installed on your company’s servers. Cloud-based data warehouses are hosted on the cloud and can be accessed from anywhere.
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 will pick 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, or Databricks.These cloud data warehouses work well with a good metadata management strategy, which includes implementing a data catalog, which Alation can provide.
2) Get a data ingestion tool and 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. Do your research and pick one that will fit your company’s needs.
Once you have a data ingestion tool, connect your data sources to it. Depending on your data sources, this 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 will do the work of 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. Do your research and pick one that will fit your company’s needs. For example, if you want to be able to automatically clean your data, you’ll need to find a tool that offers that feature.
Common data transformation tools in the modern data stack include dbt, Dataform, and Dataiku.
4) Start visualizing data using business intelligence tools
After your data is clean and prepared, you can start visualizing it using business intelligence (BI) tools.
There are many BI tools available on the market. If you want to be able to share data easily, you’ll need to find a tool that offers that feature.
Some BI tools will also allow you to create dashboards and reports.
These 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, Tableau, 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.
If you need help teaching people in your organization about the modern data stack, there are plenty of resources out there.
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.
What Does the Future Hold for the Modern Data Stack?
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 too 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.
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, 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.
Justin Chia is the founder of Justjooz. He seeks to educate everyday people about crypto, analytics, and home tech.