Modern Data Stack and what we know about it

The modern data stack is faster, efficient and flexible.
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The Modern Data Stack (MDS) is a novel data integration method that may save time while also focusing on high-value tasks. It is the core of analytics architecture. The modern data stack is made up of tools and technology for delivering, managing, and analysing data. Data processes, data management and querying, and analytics will be the foundations of a modern data stack. The article will be focused on understanding the modernization of traditional data stacks. Following are the topics to be covered.

Table of contents

  1. The Data Stack
  2. About Modern Data Stack
  3. Why is it called Modern Data Stack?
  4. How does Modern Data Stack work?
  5. Why should an organization update their Data Stack?

To produce value, data must first be assembled, categorised, cleansed, and used in an analytics project. Let’s start by talking about Data Stack.

The Data Stack

Data is made consumable with the help of a data stack. A data stack is analogous to a data kitchen.


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Consider how you would make a meal. The majority of components aren’t edible on their own, they do contain nutrients, but you wouldn’t want to eat wheat or raw vegetables. However, with the right equipment in the kitchen, such as a mixing bowl, an oven, a kitchen timer, a pan, spoons and spatulas, and a chef who can follow directions, these formerly inedible substances transform into a magnificent meal that everyone will enjoy. 

Bits of information lying about is not appealing. However, after travelling through a data stack, the bits of information have been transformed into meaningful fact and dimension tables with clear field names and types, which are easily digested by various corporate divisions.

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What is inside that data stack? 

It’s much more than a data warehouse. Tools that accomplish four core functions make up data stacks.

  1. The loading process involves moving data from one location to another.
  2. Store everything in one location, generally in the cloud, with warehousing.
  3. Transform it into data that can be utilized.
  4. Serve forth analysis and business intelligence to teams.

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About Modern Data Stack

Over time, organizations would break their data platform into sections specific to certain aspects like applications, analytics, etc. The term “data stack” came into vogue to define the set of components or technologies to support the flow and use of data for analytics.

To be effective, a modern data stack consists of numerous components or technologies that must be combined into a uniform design. Many of these technologies are available as bundled SaaS-based applications. Organizations may opt to develop individual components themselves in some situations, especially if they have unusual requirements or want to save money. Data transformation is a good illustration of this, with some firms deciding to write data operations in Python and SQL. 

An effective modern data stack is essential to driving greater adoption of analytics in your organization and, in general, having greater use of data. Inefficient data stacks can lead to cost overruns on the technology side (higher cloud costs) and higher personnel costs and limit the organizations’ effective use of data.

As the data warehousing and analytics market shifted to the cloud, innovative new approaches and processes were invented to make the data stack and operate the data stack more efficiently. These changes include:

  • Changing the monolithic ETL (extract, transform, and load) data integration process to a more efficient ELT (extract, load, and transform
  • Focusing as much processing as possible in a cloud data warehouse to take advantage of its scalable and cost-efficient computing and storage
  • Taking advantage of newly invented categories of products to help manage the data in the data stack.
  • Using newer cloud-based analytics tools that allow analysts and data scientists greater freedom to find insights. 

Why is it called Modern Data Stack?

The most significant distinction between a modern data stack and a legacy data stack is that the legacy is housed in the cloud and requires minimum technical configuration on the user’s part. These features enhance end-user accessibility as well as scalability, allowing you to easily meet your expanding data demands without incurring the costly and time-consuming downtime that scaling local server instances entails.

The modern data stack, in the end, lowers the technological barrier to data integration. The modern data stack’s components are designed with analysts and business users in mind, ensuring that people of various backgrounds may not only utilise but also administer these tools without requiring extensive technical expertise.

Time, money, and effort are all saved with the modern data stack. When compared to on-premise solutions, the cheap and dropping prices of cloud computing and storage continue to boost the cost savings of a contemporary data stack. Off-the-shelf connections save your analysts, data scientists, and data engineers time that would otherwise be spent creating, building, and managing data connectors, allowing them to focus on higher-value analytics and data science initiatives.

How does Modern Data Stack work?

A Modern Data Stack (MDS) solves the purpose of evaluating the data to find new areas of potential and increase efficiency. The MDS is made up of numerous layers that are piled on top of one another, each with its unique purpose. 

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Data integration

Integrating data from several sources into a single, cohesive view is known as data integration. As part of the integration process, the ingestion phase involves cleaning, ETL mapping, and transformation. Businesses can gain actionable insights from analytics tools through data integration.

There is no one solution that fits all when it comes to data integration. As an alternative, data integration solutions often involve a network of data sources, a master server, and clients who interact with the master server.

In a typical data integration scenario, the client requests data from the master server. The essential data is subsequently gathered by the master server from both internal and external sources. The data is taken from many sources and then merged into a single, coherent data collection. This is returned to the client for their usage.

Data storage

A data warehouse is usually a cloud-based solution for storing all of the data acquired by the data intake tool. It is often called Data Lakes. The cloud data warehouse or data lake is at the heart of the modern data stack. It also serves as the primary query interface for EL tools, data transformation tools, and business intelligence and analytics tools. 

Any data process, whether for data loading or transformation, will rely on the Cloud Data Warehouse’s or data lake’s considerable computational capacity, as well as the underlying storage for loaded or converted data. When querying data, BI and analytics applications will also rely on this processing capacity. The Cloud Data Warehouse or Data Lake will also manage and administer the underlying data security and governance rules and policies. Other data stack technologies like security and governance features should function in tandem with the CDW or Data Lake’s fundamental controls.

There are certain key points that should be focused on before selecting a particular Cloud Data Warehouse or Data Lake.

  • The auto-elastic scalability ensures that when data processes or queries are run, only get the computational resources that are required.
  • The efficiency and granularity of the computation and query resources of the CDW or data lake, so to ensure the costs of the data stack are as low as possible.
  • The security of the CDW or data lake should be strong. The ease of applying and managing the security and governance. 
  • The availability of CDW or data lake for the instances, and where they are available to run.
  • The kinds of data formats needed to work with and does the CDW or data lake support these formats and allow efficient use within the platform.

Data transformation

The ELT (Extract, load, and transform) process includes data transformation and modelling tools. They take the raw data supplied by the Extract and Load tools and turn it into something the analytics teams can use. Raw data will be transformed into numerous different data models by data transformation technologies for usage in various analytics use cases. Data models can be intermediate, allowing many downstream models to utilise them, or they might be final, allowing analytics to use them directly. 

Business intelligence

The data is evaluated here, and dashboards are built so that users may examine the information. Domain specialists may now respond to business inquiries without relying on developers or analysts.

Data governance (DG)

DG refers to the process of making data in corporate systems available, accessible, secure, and inherently conform to internal standards and regulations, as well as governing data consumption. Data governance ensures data is reliable, secure, and consistent. Data governance regulations are forcing businesses to consider new ways to protect their data, as well as relying on data analytics to streamline operations and make better decisions. Essentially, there are two types of data governance.

  • Data catalogs enable businesses to keep track of and make sense of their data, which improves data discoverability, quality, and sharing. Without these technologies, the data lake can quickly deteriorate into a data swamp.
  • When it comes to data protection, data privacy tools assist a company in being legally compliant. Problems such as sensitive data breaches can be addressed.

Why should an organization update their Data Stack?

While there are other advantages to employing a current data stack, three major advantages distinguish a modern data stack from legacy versions.


Data stacks have always been constructed and evolved by the teams who use them. While there is nothing intrinsically wrong with this technique, these data stacks are generally very customised and fragile. These data stacks may soon become troublesome and pose a huge maintenance issue if not supported by data engineers and other technical workers. The same solutions may be accomplished using tools created particularly for each use case by utilising a current data stack.

When limited to the architecture of a typical data stack, scalability can be a significant barrier. Scalability is achieved quickly and is not limited to certain tools when employing a contemporary data stack. MDS technologies are intended to handle as much or as little traffic and processing as is directed at them. A firm encountering a performance challenge with its data warehouse is one example of this. This may be easily fixed by just raising the capacity of the warehouse by easy user interface settings and scaling instantly.

Flexibility and Modularity

The modern data stack is meant to seem like a microservice. This enables the robust creation of tools that address a specific operation in the modern data stack. Furthermore, by structuring tools in this manner, all operations of the contemporary data stack may be loosely connected together, providing freedom of choice when it comes to interchanging stack activities.

The modern data stack solutions structured as modules assist enterprises to reduce the danger of provider lock-in. Because contemporary data stack technologies are constructed as microservices or modules, they essentially solve the same problem, although with subtle differences. Furthermore, because these tools do not rely on the tools surrounding them, they are loosely connected, allowing for simple interchangeability.

Technical Barrier

The technical barrier is eventually lower than the traditional data stack. There are two major benefits for the organization.

  • Building and maintaining data stacks do not require big data teams.
  • The time that data teams may have previously spent developing and managing data stacks may now be reassigned to utilising and comprehending the data, providing for faster time to insight and an agile data team that can grow data request capacity.


The fragile data stacks and wasteful operations make it harder to continue building and scaling the data stack. This also hinders the insight required to make key data-driven choices. The modern data stack spans everything, from extremely modular components of the data stack that make acquiring insight highly accessible to decreasing the technical barrier and offering huge value for enterprises. With this article, we have understood the modern data stack and the evolution of the data stack.


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Sourabh Mehta
Sourabh has worked as a full-time data scientist for an ISP organisation, experienced in analysing patterns and their implementation in product development. He has a keen interest in developing solutions for real-time problems with the help of data both in this universe and metaverse.

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