The Modern Data Stack: What It Is and Why You Need It

What is the modern data stack?

We can define the modern data stack as a deliberate combination of different technologies specifically built to support data storage, management, and access. Data stacks are typically created by (but not limited to) organizations striving to leverage their data for strategic decision-making. It is important not to confuse this with the term “modern tech stack,” which is used to describe the sum of an organization’s technological apparatus and is generally focused on multiple use cases (rather than solely data). With technology and data evolving, GoodData views the modern data stack as a necessary asset in an organization’s comprehensive technology stack. But what are the tools that make up the modern data stack?

Modern data stack tools

The modern data stack, much like the modern tech stack, consists of multiple technologies and services. After reviewing the essential components, we’ve identified four main elements:

Public/private cloud

With improvements in security and scalability, as well as a decrease in cost, private and public clouds have become increasingly popular in place of the on-prem alternative. The reduced resource requirements and flexibility provided by cloud storage as opposed to an on-prem storage solution further underline the reason for this shift. Examples of cloud storage solutions include Amazon AWS, Microsoft Azure, and Google Cloud.

Data storage

While a public or private cloud provides the underlying storage for the entire analytics platform as well as a company’s other software and applications, the data itself requires its own storage solution. This could be one of a number of different data storage types, depending on the wider use case, including a modern data warehouse like Snowflake or Redshift, an SQL database like PostgreSQL, a data lakehouse like Dremio, or a combination thereof. Another key component of the modern data stack is dbt, as it enables teams to transform raw data into structured, analysis-ready data models within cloud data warehouses.

Analytics engine

The analytics engine sits at the heart of the modern data stack. One of its key components is the semantic model. The semantic model streamlines data management, translating the complex data structures within your data storage into easy-to-understand, highly reusable abstractions. These abstractions define the relationships between datasets and, importantly, require no prior SQL knowledge from end users. In other words, when multiple end users work with the same data, they get the same consistent outputs, regardless of how they calculate them. You can easily build the semantic model from physical data model fields or pre-built views, with the entities mapped to one or more data sources(e.g., Snowflake, Redshift, Dremio, etc.).

Presentation layer

The final piece of the modern data stack is the plethora of tools that end users use to visualize the data. There are many, many different visualization tools available, and different users will want to use different tools. These could be legacy applications still used within the organization, popular BI tools like Tableau or PowerBI, or more specialist apps used for machine learning or AI.

The value of this new analytics data stack

The modern data stack brings enhanced capabilities and a number of possibilities. Let’s recap its main benefits in comparison to staying with the legacy data stack:

1. Cost-efficiency: Cloud-based storage (and technology) is typically significantly cheaper than on-premise. With cloud-based solutions, you only pay for what you use and can efficiently scale up as needed.
2. Modularity: Modularity translates to flexibility. As your requirements change, you can update the corresponding individual components accordingly, avoiding the need to roll out an entirely new solution.
3. Speed: The modern data stack has become substantially more efficient. It allows you to refresh data in minutes rather than hours and spin up a trial of an entire stack in hours rather than days or weeks.

The ever-evolving data stack is a key piece of an organization’s assets. Gone are the days of one size fits all, with one all-encompassing product. The modern cloud era is geared toward a data stack built up of several interconnecting pieces. For this reason, companies should strive to find flexible solutions in their pursuit of data analysis that not only work in harmony with their wider data stack but offer the flexibility and futureproofing needed for long-term success.

How GoodData fits into the structure of the modern data stack

GoodData is an end-to-end analytics platform with a modular, microservice-based architecture that ensures it fits seamlessly into the wider data stack. Businesses can integrate GoodData alongside existing infrastructure and implement analytics using the exact tools their end users require.

GoodData’s analytics engine, FlexQuery, sits at the center of the modern data stack and can federate data sources or directly query data in real-time. Powered by open-source tooling like Apache Arrow, FlexQuery can process and transfer high volumes of data quickly, enabling, for example, real-time data streaming.

GoodData’s semantic model powers an API-first approach to analytics, enabling seamless integration, flexibility, and scalability by making data accessible and actionable across platforms and applications. Additionally, with headless BI, you can leverage any visualization app of your choice while ensuring reliable results.

Get a modern data analytics platform for today’s data stack

The modern data stack's modular, flexible, and efficient nature brings tremendous value, enabling real-time insights, scalability, and cost-effectiveness. GoodData ranks among the leading BI platforms and plays a pivotal role in today’s stack by providing advanced analytics, embedding capabilities, and seamless integration with your existing infrastructure. Ready to discover how GoodData can transform your business? Get a demo today and experience the future of data analytics firsthand.