It's a fact. For many large corporations and data-driven organizations, data is the single most valuable asset, fueling decisions, driving applications, and powering cutting-edge technologies such as artificial intelligence and machine learning.
But behind the scenes of these sophisticated, data-powered processes is a complex architecture known as the data stack, with the modern data stack (MDS) at its core. The modern data stack refers to the combination of cloud-based technologies and tools that are designed to manage and process raw data into actionable insights in a scalable, flexible manner.
Unlike a legacy, traditional data stack—that relies on on-prem hardware and siloed data sources—modern data stacks are built to handle the velocity, variety, and volume of data we encounter today. They are cloud-first, modular, and enable data engineers, data scientists, and business users alike to work with data pipelines, cloud data warehouses, and data lakes to make faster, more informed decisions.
With a modern data stack architecture, data is ingested, stored, transformed, analyzed, and visualized in a seamless workflow. This architecture ensures that businesses can rapidly adapt to the ever-increasing demand for data analysis and data integration, allowing them to extract value from both structured and unstructured data. The shift toward cloud-based data warehouses and data lakes further enhances scalability, enabling organizations to process vast amounts of data stored across multiple locations with minimal manual intervention.
The modern data stack is critical for ensuring data quality and governance as it integrates data transformation tools, data pipelines, and analytical systems into one cohesive ecosystem. Companies that adopt this infrastructure can leverage the full potential of business intelligence tools, empowering both technical teams and business users to work together more efficiently and uncover insights faster.
At its core, the modern data stack embodies flexibility, speed, and automation, making it a key enabler of innovation and data democratization in the enterprise. Whether you're dealing with big data, streaming data, or traditional datasets, the modern data stack provides the necessary tools to ingest, transform, and analyze information in real-time, positioning companies to thrive in today's data-centric landscape.
Table of contents
The key components of a modern data stack
A modern data stack is an integrated architecture that processes raw data into actionable insights. It can be broken down into several critical stages, each playing an essential role in the data transformation process. These stages ensure that data-driven organizations can efficiently manage, process, and extract value from the vast amounts of data they collect daily. The stages include:
- Data pipelines.
- Data storage.
- Data transformation.
- Data analysis.
- Data visualization.
Data pipelines
Data pipelines are the starting point of the entire data transformation process, where data begins its journey from various sources to become useful for analysis and decision-making. These pipelines gather, process, and move data from its original location to a centralized environment, such as a data warehouse or data lake, where it can be stored and later transformed. At this stage, the data is typically in its raw or unedited state—also known as inedible data—meaning it’s not yet suitable for analysis or business use.
The core function of a data pipeline is to automate the flow of data between systems, ensuring that data is continuously ingested, cleaned, and transported for downstream tasks. Tools like Apache Kafka, AWS Glue, Google Cloud Dataflow, and Azure Data Factory play a pivotal role in building efficient and scalable data pipelines. These tools are designed to handle high volumes of real-time data streams and batch processing, making it easier for organizations to move data stored across disparate sources.
A well-architected data pipeline typically includes several stages:
Data ingestion
This is where data from various sources, such as databases, APIs, or IoT devices, is collected and funneled into the pipeline. Tools like Apache Kafka and AWS Kinesis are frequently used for this, as they allow for real-time data ingestion and data streaming between sources.
Data processing
After ingestion, the data may go through preliminary processing to clean and standardize it. This may involve deduplication, validation, or filtering unnecessary data points to ensure only relevant information is passed along.
Data orchestration
Modern data pipelines often include orchestration layers that manage dependencies, scheduling, and execution across various stages of the pipeline. Orchestration tools like Airflow and Dagster automate and monitor the flow of data from source to storage.
Data delivery
The final step in the pipeline is the delivery of data to a data storage layer—such as a cloud data warehouse or data lake—where it becomes available for further transformation and analysis.
Effective data pipelines must be resilient, scalable, and capable of handling the growing volume of data that most modern organizations deal with. Data pipelines are also critical for ensuring data quality, as they can be designed to catch errors, validate data formats, and cleanse data in real-time. This is crucial for businesses that rely on clean, high-quality data to make accurate, data-driven decisions.
By automating the movement of data through data pipelines, organizations can free up valuable time for data engineers and data scientists to focus on more complex tasks, like analyzing patterns or building machine learning models. With the rise of cloud-based data platforms like MongoDB Atlas, Snowflake, and Google BigQuery, these pipelines have become even more flexible, allowing companies to handle both batch data processing and real-time data streams with ease.
Data storage
Once data has been ingested through data pipelines, the next critical step in the modern data stack is efficient data storage. Effective data storage not only ensures that data is securely housed but also guarantees that it remains accessible for data transformation, analysis, and future retrieval.
The two primary storage solutions are:
- Data warehouses.
- Data lakes.
Data warehouses
A cloud data warehouse is designed to store structured data, such as transactional information, customer records, or financial data, in a highly organized and easily queryable format. These warehouses are optimized for supporting business intelligence (BI) tools that allow organizations to analyze their data through complex queries and generate reports that drive decision-making.
Cloud-based data warehouses like Amazon Redshift, Google BigQuery, and Snowflake provide virtually unlimited storage capacity, allowing companies to handle increasing volumes of data stored without the need for on-premise infrastructure.
Additionally, cloud data warehouses are designed for fast query performance, ensuring that even the most complex queries return results quickly, enabling data analysts and business users to make timely, data-driven decisions.
One of the key advantages of a cloud data warehouse is its ability to integrate with various business intelligence tools, providing users with intuitive dashboards, visualizations, and reports based on real-time data. The highly structured nature of data warehouses makes them ideal for enterprises that need consistent, reliable data for operational and strategic analysis.
Data lakes
In contrast, a data lake is a more flexible solution that stores unstructured and semi-structured data. This could include a wide range of data types such as text, images, videos, social media content, IoT sensor data, or logs from applications.
Data lakes provide a scalable, cost-effective way to store vast volumes of data in its original format, without the need for immediate data transformation.
The flexibility of a data lake allows organizations to store and manage big data in a way that supports long-term analytics, data mining, and even machine learning applications. This is particularly useful for data scientists and data engineers who need access to large datasets for exploratory analysis or model training. Data lakes can store a mix of both historical data and real-time streaming data, making them a central repository for all kinds of data assets.
Data warehouses vs. data lakes
While both data warehouses and data lakes serve critical roles in modern data storage, they cater to different business needs:
Data warehouses: These are ideal for structured data that requires fast access and supports reporting, business intelligence tools, and operational analysis. These are typically used for real-time business queries, dashboards, and key performance metrics.
Data lakes: These are best suited for unstructured or semi-structured data that can be processed at a later stage. Data lakes are often used for long-term storage of big data, raw data analysis, and machine learning experimentation. They enable organizations to store everything without having to categorize or structure it initially.
By integrating both storage solutions, many organizations adopt a data storage layer that incorporates the best of both worlds. This hybrid approach allows companies to store structured data in a cloud data warehouse for business operations while utilizing a data lake for raw, unstructured data that may later be processed and analyzed for big data analytics.
Learn more about the difference between a data warehouse and a data lake.
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
Extract, 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.
Data analysis
Data analysis is a crucial step in extracting insights and identifying patterns, data flows, and trends that can drive business decisions. By reviewing historical and real-time data, businesses can better understand customer behavior, improve product offerings, and enhance marketing strategies.
To perform data analysis, organizations often rely on advanced tools like Microsoft Power BI or Apache Spark, which allow for the visualization and exploration of large datasets.
MongoDB integrates seamlessly with many of these business intelligence tools, providing powerful connectors to ensure smooth data flow and real-time analysis.
In more advanced scenarios, businesses can leverage machine learning to conduct predictive analytics on their datasets. Tools like Jupyter Notebook—which is also supported by MongoDB—allow data scientists to build and train machine learning models directly on the data, uncovering deeper insights and facilitating data-driven decision-making. In fact, MongoDB has connectors for many analytics tools.
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.
The modern data stack vs legacy data stack
The fundamental difference between a traditional data stack and a 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 a lot of infrastructure
- Time-consuming
Modern data stack
- Cloud configured
- Ease of 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
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.
Speed
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.
Cost
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.
Time
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 increased, new technologies (MongoDB being among them) 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.
Summary
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.
At MongoDB, some of the better-known technology stacks are MEAN and MERN. These stacks do not represent the full 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.
FAQs
What is a data analytics stack?
What is reverse ETL?
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 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.