Understanding the Modern Data Stack: Key Components & Benefits

How much value does your organization get from its data? To maximize its potential, you need a modern data stack—a cloud-based collection of tools that enables seamless data integration, storage, and analysis. A modern data stack allows businesses to gather data from multiple sources and process it with greater speed and flexibility compared to traditional systems.

Understanding modern data infrastructure is essential for navigating today’s complex data environments. It ensures that you can scale efficiently, improve data accessibility, and deliver real-time insights that drive decision-making.

Unlike traditional data systems, the modern data stack is built for agility, using scalable cloud platforms and automation to streamline workflows. This guide will help you understand these key differences and guide you in building a modern data stack tailored to your business needs.

Key Takeaways

• A modern data stack is a set of technologies designed to ingest, store, transform, and analyze data from a variety of sources.
• Modern data stacks can ingest both structured and unstructured data in a variety of formats.
• Modern data stacks typically store data in the cloud for centralized access.
• Modern data stacks include robust data quality monitoring capabilities to ensure the highest quality data possible.

What is a Modern Data Stack?

In today’s world, data is everywhere. According to Statista, the amount of data created, consumed, and stored will exceed 120 zettabytes this year and is expected to grow to over 181 zettabytes by 2025. (For perspective, one zettabyte is equal to 1 billion terabytes or 1 trillion gigabytes.)

Despite this massive amount of data, much of it goes unused. In fact, up to 60% of data within the average organization remains unanalyzed, which means many opportunities for valuable insights are missed.

To make the most of their data, businesses need a modern data stack—a collection of cloud-based tools that gather, process, and analyze data in real time, helping companies make better, data-driven decisions.

The modern data stack represents a shift from traditional data systems, which typically rely on on-premises servers and outdated processes. Today’s modern stacks are cloud-native, allowing businesses to store and process large volumes of both structured and unstructured data more efficiently. This move to cloud solutions not only improves data accessibility but also ensures that data is ready to use for timely insights.

With a modern data stack in place, companies can put their data to work, improving both everyday operations and long-term strategies.

Key Components of Modern Data Stack Architecture

A modern data stack typically consists of six key components, each made up of technologies that work together to ensure smooth operation. The Modern Data Stack Architecture varies based on the size and complexity of an organization’s needs, determining whether each component is powered by a single tool or multiple integrated solutions.

• Data sources, both internal and external
• Data integration, which ingests data from different sources
• Data storage, typically in the cloud
• Data transformation, which makes data from all those sources equally accessible and usable
• Data visualization and analytics, which generates actionable insights from the transformed data
• Data governance and monitoring, which keeps track of the entire data stack and all data within

Data Sources

A modern data stack turns raw data into usable data, which means the entire process starts with raw data. Organizations typically have data coming from various sources, including:

• Internal customer and sales databases
• CRM platforms, such as Salesforce and HubSpot
• Social media, such as Facebook, Twitter, and LinkedIn
• Event streams, from website clicks and other real-time activities
• APIs, from software and apps

A modern data stack has to be able to ingest data from all these sources in near-real-time. 

Data Integration

The next part of a modern data stack is data integration. This entails extracting data from its original sources, organizing, and monitoring its quality to prepare it for storage in a centralized location. 

In a traditional data stack, data must first be transformed before it can be stored. This is accomplished through a process known as Extract, Transform, Load, or ETL. In a modern data stack, data does not need to be transformed before it is stored, as the transformation happens later in the process. 

Instead, raw data is typically stored in its original form as part of the Extract, Load, Transform (ELT) process. Unlike the ETL process, which is typically a batch process, ELT can be done in real time as the data is streamed into the stack. 

Data Storage

Ingested data must be stored somewhere. In a modern data stack, that storage is typically somewhere in the cloud. Cloud-based storage is preferred because it is centralized yet easily accessible from other locations, via the Internet. Cloud storage is also easier to maintain, more cost-effective, and easier to scale than traditional on-premises storage. 

Cloud data storage can take many forms, including:

• Data warehouse
• Data lake
• Data mesh

Which architecture you choose depends on your organization’s specific needs and the types of data you typically use.

Data Transformation

In a typical modern data stack using ELT methodology, data is stored in its original format and not transformed until it is ready to be accessed and used. This enables the use of data in various formats, including both structured and unstructured data. It is transformed within the storage medium into whatever format is required for consumption. 

The transformation process is also where data quality comes into play. For data to be useful it must be accurate, complete, consistent, timely, unique, and valid. Poor-quality data must be identified, separated from higher-quality data, and either cleaned or deleted. This requires data transformation tools that include robust data quality monitoring capabilities. 

Data Visualization and Analytics

Once data is transformed, it can be consumed by users within the organization. This requires visualizing the data, in the form of reports and dashboards, as well as analyzing the data. Today’s modern business intelligence (BI) and analysis tools let users slice and dice the data in myriad combinations, as well as combine data from different sources to identify current and future trends. Detailed analysis, when performed on the right data, results in actionable insights that help drive business decisions. 

Data Governance and Monitoring

The final component in the modern data stack is one that resides apart from the normal data flow. To keep track of the data in the stack and ensure that the process is working efficiently requires constant monitoring and governance. You need to know where your data is at all times, the state of that data, and how discoverable it is, otherwise the data you gather could become unusable and ultimately unused. Data governance is also necessary for testing and monitoring the data flow, monitoring data quality, generating detailed audit trails, and ensuring compliance with privacy and security regulations.

Benefits of Using Modern Data Stack Tools

There are several key advantages to moving from a traditional to a modern data stack, including:

• Faster data processing and analysis speeds
• More detailed analytics
• More robust data tracking and auditing
• Ability to ingest data from disparate sources
• More automated processes
• Higher quality data
• Lower costs 

A modern data stack can transform the data culture within an organization by making data more accessible and user-friendly for employees at all skill levels. 

The modern data stack tools are designed with usability in mind, enabling everyone—from data novices to experienced analysts—to leverage data effectively.

Furthermore, the flexibility of a modern data stack allows users to select the tools that best fit their individual needs and expertise, rather than being confined to a single solution. This adaptability not only enhances productivity but also fosters a more data-driven mindset throughout the organization.

Modern Data Stack vs. Traditional Data Stack

Traditional data stacks are on-premises solutions that rely on internally managed hardware and software, often featuring complex monolithic architectures. These legacy systems require significant investments in IT infrastructure and personnel, making them less flexible and scalable compared to modern alternatives.

6 key characteristics define and set a modern data stack apart from traditional (legacy) data stacks.

1. Cloud-First: Modern data stack tools are cloud-based for greater scalability, while traditional solutions are on-premises, limiting flexibility.
2. Data Warehouse Integration: Modern stacks seamlessly integrate with cloud data warehouses like Redshift and BigQuery, unlike legacy systems that struggle with integration.
3. Modular Design: Modern stacks allow easy swapping of components, whereas traditional stacks require complex adjustments for changes.
4. Open-Source Components: Many modern components are open-source and community-supported, while traditional stacks often rely on costly proprietary software.
5. SaaS and Managed Services: Modern tools often offer low-code/no-code options for accessibility, while traditional systems need higher technical expertise.
6. Data Democratization: Modern solutions enable all employees to access data, promoting collaboration, unlike traditional systems that limit access to specialists.

Ensure a High-Quality Modern Data Analytics Stack with First Eigen’s DataBuck

A high-quality modern data analytics stack is only as effective as the data it processes. First Eigen’s DataBuck is a data quality monitoring solution that enhances your data stack by automatically identifying and correcting low-quality data. With its advanced AI & ML capabilities, DataBuck improves data processing speed, reduces errors, and lowers operational costs, all with minimal manual intervention. Ensure your analytics stack delivers accurate and actionable insights by transforming poor-quality data into a valuable resource for your organization.

Contact FirstEigen today to learn more about data quality in the modern data stack.

Check out these articles on Data Trustability, Observability & Data Quality Management-

• 6 Key Data Quality Metrics You Should Be Tracking
• How to Scale Your Data Quality Operations with AI and ML?
• 12 Things You Can Do to Improve Data Quality
• How to Ensure Data Integrity During Cloud Migrations?
• Date Mesh Architecture
• Data Mesh vs Data Lake
• Modern Analytics Stack
• Traditional Data Warehouse vs Cloud Data Warehouse