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What is a Modern Data Stack?

Data has become one of the most sought after commodities, and to many large corporations, it is the single most valuable resource. Data is so valuable that it has become integral to sustaining our current economy — becoming as necessary as oil, labor, capital, and land. Data drives so much of our day-to-day, but to understand what goes on behind the scenes with this data and the work of data engineers and other data professionals, we need to first ask:

Table of contents

What is a data stack?

A data stack is a collection of various technologies that allow for raw data to be processed before it can be used. A modern data stack (MDS) consists of the specific tools that are used to ingest, organize, store, and transform data. These tools allow for the data to be taken from “inedible data” (data that cannot be worked with) to “edible data” (data that can be worked with).

The use of data and their applications is well known: Prevalent data breaches highlight security concerns, social media platforms extensively leverage personal data, and the evolving field of artificial intelligence relies heavily on diverse data sets, amongst many other examples.

But what about what happens behind the scenes? How do companies take your personal data collected from a website or elsewhere and use it in their system? For those who are not familiar with the intricacies of the data world, the transformation process can be seen as a black box. This article will help break down this process, and we will focus on a crucial term worth learning about: the data stack.

The main functions of data stacks

The process of taking data, such as data assets as your personal data, and turning it into a format that companies can use can be simplified down to five steps:

1. Data pipelines

This is where the data is gathered and moved, or ingested, into a position where it can be analyzed. This is the inedible state.

Well known tools like Apache Kafka are examples of pipeline products for ingesting data into or between sources.

2. Data storage

The data that has been ingested via a pipeline is then stored somewhere, usually in a data warehouse or data lake. This data platform can then provide access to tools for transformation, analysis, and visualization.

A data warehouse is used to store large amounts of structured data, usually for use with business intelligence (BI) tools. On the other hand, a data lake is better suited to storing large amounts of raw, unstructured data. Learn more about data warehouses vs data lakes.

MongoDB is able to store ingested data using the document-model and even has Atlas Data Lake for a scalable, fully managed data lake in the cloud alongside the more general purpose database product.

3. Data transformation

Data transformation is a critical step in the data management and analysis process, involving the conversion of data from one format, structure, or value system to another. This process is essential for preparing raw data for analysis and decision-making.

Types of data transformations

Data transformation comes in a few different types or steps:

  • Normalization: adjusting values in a dataset to a common scale, without distorting differences in ranges of values.
  • Data cleaning: removing or correcting inaccurate records from a dataset.
  • Filtering: removing unnecessary or irrelevant data.
  • Data conversion: changing data from one data type or format to another.
  • Aggregation: summarizing or grouping data, such as calculating sums, averages, or counts.
  • Joining and merging: combining data from different sources into a single dataset.
  • Data encoding: transforming categorical data into a numeric format.

Extraction, transform, and load (ETL) is a type of data transformation technique with the merging of data in a central repository or data warehouse. The data is collected through the enterprise's business rules and then stored and processed by machine learning.

4. Data analysis

Being able to review the data and analyze it for patterns, data flows, and trends that suit your business is important. In the example of your personal data, a company such as one offering a loyalty scheme might analyze your data to view similar products to promote to you, or discounts to encourage you to purchase the same products again.

This data analysis is often done using tools like PowerBI from Microsoft or Apache Spark. In fact, MongoDB has connectors for many analytics tools.

Companies could even do machine learning driven analytics on the data using Jupyter Notebooks, which is also supported by MongoDB.

5. Data visualization

The final step is data visualization. Often businesses, especially stakeholders, will want to see visual representations of the data and any analysis carried out on it. This is where data visualization comes in.

Products like MongoDB Charts make it super easy to visualize data without the need for a lot of configuration. You can select the data source inside your Atlas cluster, select a type of chart you want from a wide range of charting options, and then select the fields you want to include and any aggregations to pre-manipulate the data before displaying, such as sums, averages, or groupings.

Data tools

The tools used in each company are different, but they should be easy to integrate and have distinct uses. Some examples of tools are data warehouses, pipelining, data catalogs, data quality, data archiving, and data lakes. Data stacks originate from technology stacks: Exactly as it sounds, technology stacks are the layers that comprise a product produced by the company. Take a web application, as an example: The necessary layers are the front-end user interface (HTML and CSS, and JS for functionality and style), on top of the back-end software that actually makes the application run, including the data layer, such as MongoDB. A modern data stack is very similar.

Why is a data stack important?

“Time is money.” A cliché but true, especially when it comes to a data-driven corporation. The more efficient a data stack with transforming raw data, the faster your data science teams can monetize it. Having the proper tools in your modern data stack is critical for your company's overall success.

Modern data stacks vs legacy data stacks

The fundamental difference between an old data stack and the modern one lies in the difference between on-premise and cloud-based tools. Legacy data storage is hosted on a single premises; this means hardware will need individual provisioning, management, and scaling as business needs evolve. The data stacks are hosted entirely in the cloud, allowing all the necessary hardware maintenance for managing the equipment to be performed automatically. Cloud-based and SaaS-based data transformation tools eliminate significant costs and enable users to focus primarily on business objectives.

A legacy data stack is what came before the modern data stack. It's an infrastructure-heavy method of preparing data for analytical use. Even though the move toward modern data stacks is gaining popularity, legacy data stacks are still vital for businesses. They hold essential company information and need to be integrated properly into your MDS. The key differences between the two are outlined below:

Legacy data stack:

  • Technically heavy
  • Requires lots of infrastructure
  • Time-consuming

Modern data stack:

  • Cloud configured
  • Easy to use
  • Suite of tools is designed with non-technical employers in mind
  • Saves time

Advantages of a modern data stack

The four main advantages of switching from an outdated stack to a modern data stack are:


Modularity is a term to describe a way to create various products that can be separated into standalone but integrable components. In a data stack, this would be seen as building your stack layer by layer, including various technologies and tools that are perfect for your organization.


The modern data stack is a cloud-based solution, meaning the speed of processing data has increased exponentially. The same amount of work that took hours with a legacy data stack can now take minutes. The automation involved with cloud data warehouses has also made this a faster option.


Hardware and complicated infrastructure are no longer needed in a modern data stack. This cuts costs down drastically, while allowing more authority over your data processing methods.


Setting up a modern cloud data stack can take as little as 30 minutes. Modern data stacks are also automated, meaning fewer working hours need to be involved in the data process.

Data stack use cases

As the requirement for more data storage space has increased, new technologies (MongoDB being among them) have found more efficient ways of dealing with data. Cloud technology skipped to the forefront of modern engineering in the early 2010s and dramatically changed big data forever. Amazon's Redshift in 2012 pushed forward the modern data lake, and this truly paved the way for data optimization and transformation as we know it today. Cloud computing and storage allowed for data to now be loaded prior to being transformed (a process known as ELT: extract, load, transform) instead of its sister method (ETL: extract, transform, load), due to storage space being readily available because of cloud computing technology. Some examples of well known companies that provide proper data stack tools are Snowflake, Google BigQuery, and AWS Redshift. These organizations help provide companies with data storage, data transformation, data ingestion tools, and the various business intelligence tools necessary to conduct data manipulation.

timeline of big data


Technology stacks are crucial for developers in every sort of corporation. These are not a new concept, and the modern data stack is an addition to what should be going on in the background of your organization. Almost every application produced in a company is born through some sort of stack pipeline.

Here at MongoDB, some of the more better known technology stacks are MEAN and MERN. These stacks are not the extent of what MongoDB can do: MongoDB even allows integration with Apache Hadoop, so complex data analytics can be conducted on data stored in your MongoDB clusters. This combination, along with important business intelligence tools, allows for a deeper analysis of your raw data.

Every data-driven organization needs a personalized modern data stack. There are a multitude of companies offering competing services with pay-as-you-go methods, so integrating an efficient and elegant data pipeline into your organization is now easier than ever before.


What is a data analytics stack?

A data analytics stack refers to the combination of technologies and tools used to gather, process, store, analyze, and visualize data to derive insights and make informed decisions.

What is reverse ETL?

ETL — or extraction, transform, and load — is a type of data transformation tool. Reverse ETL has become more popular in modern data stacks in recent years and instead of collecting data from various sources for analysis, it sends data from the centralized repository back into business applications.

What is a data warehouse used for?

A data warehouse is a central repository for all of an organization's data. It is designed to bring together data from multiple sources and make it available to businesses for analysis and reporting. Data warehouses are used by organizations to gain insights and make better decisions.

This data is typically stored in a structured format and is used for reporting and analysis. The data in a data warehouse is often sourced from multiple sources, such as transactional systems, log files, and external data sources. The data is then transformed and loaded into a warehouse for analysis.

What is a data lake?

A data lake is a repository of data from different sources that is stored in its original, raw format. Like data warehouses, data lakes store large amounts of historic and current data. What sets data lakes apart is their ability to store data in a variety of formats, including JSON, BSON, CSV, TSV, Avro, ORC, and Parquet.