Modern Data Stack

This guide provides definitions, examples and practical advice to help you understand the topic of modern data stacks.

What is a Modern Data Stack?

A modern data stack (MDS) is a collection of tools and technologies used to gather, store, process, and analyze data in a scalable, efficient, and cost-effective way. They encompass various functions, including data pipelines for ingestion, cloud-based data storage, data transformation, data analysis and visualization, and data science and machine learning.

Main Functions

A modern data stack is a set of technologies used to collect, store, manage, and analyze data in modern, scalable ways.

Let’s delve into the 6 key components of the modern data stack and their functions, along with examples of top tools for each.

  1. Data Sources

    • Function: Data sources are the origins of data, including databases, APIs, logs, and external services. They provide raw data that needs to be aggregated, ingested and processed.

    • What to Look For: Reliability, scalability, and compatibility with your existing infrastructure.

    • Top Tools:

      • SAP: Popular ERP software for businesses.

      • Google Analytics: A website tracking tool that provides user interaction insights.

      • Apache Kafka: A distributed event streaming platform.

  2. Data Pipelines

    • Function: Data pipelines move and transform data from sources to destinations such as data warehouses or databases. They handle data ingestion, transformation, and loading processes (and reverse ETL).

    • What to Look For: Ease of use, fault tolerance, and support for various data formats.

    • Top Tools:

      • Fivetran: cloud-based automated data movement platform.

      • Qlik (which now includes Talend): Comprehensive ELT and ETL tools with visual interfaces.

      • Stitch: A simple cloud ETL service.

  3. Data Storage

    • Function: Data storage solutions store processed data for querying and analysis. They include cloud data warehouses, data lakes, and databases.

    • What to Look For: Scalability, performance, and cost-effectiveness.

    • Top Tools:

      • Snowflake: A cloud data platform known for its elasticity and ease of use.

      • AWS Redshift: A powerful cloud data warehousing solution.

      • Databricks: A unified cloud platform for data, AI, and machine learning at scale.

  4. Data Transformation

    • Function: Data transformation tools clean, enrich, and reshape data. They prepare it for analysis and reporting.

    • What to Look For: Flexibility, support for custom transformations, and integration capabilities.

    • Top Tools:

      • Trifacta (now Alteryx Designer Cloud): A user-friendly data wrangling platform.

      • dbt (Data Build Tool): An open-source, SQL-based transformation tool for ETL or ELT.

      • Matillion: An ETL platform designed for cloud data warehouses.

  5. Data Analytics / Visualization

    • Function: Analytics tools allow users to explore and visualize big data, create business dashboards, and generate insights.

    • What to Look For: Intuitive interfaces, robust visualization options, and collaboration features.

    • Top Tools:

      • Qlik: An analytics platform with AI-powered insights and predictions.

      • Tableau: A powerful data visualization platform.

      • Power BI: Microsoft’s business intelligence tool.

  6. Data Science/ML

    • Function: Data science and machine learning tools enable predictive modeling, classification, and anomaly detection.

    • What to Look For: Support for algorithms, scalability, and integration with other components.

    • Top Tools:

      • Python (with libraries like Pandas, NumPy, and Scikit-learn): Widely used programming language for data science and ML.

      • TensorFlow: An open-source ML framework by Google.

      • PyTorch: A popular deep learning library.

Remember that the modern data stack is dynamic, and new tools emerge regularly. Choose components that align with your data strategy, organizational needs, scalability requirements, and long-term goals.

Data Integration Challenges & Solutions

Learn how to overcome the top 14 challenges you face.

Modern Data Stack vs Legacy Data Stack

Modern data stacks offer you agility, scalability, and accessibility, while legacy stacks require more manual effort and struggle to keep up with today’s dynamic data landscape. Let’s compare them side by side with examples:




Cloud-Based. MDS components are provisioned in the cloud, eliminating the need for physical infrastructure setup.

Example: Amazon S3 and Google BigQuery are instantly available cloud services.

On-Premises. Legacy stacks require manual provisioning of servers and hardware.

Example: Setting up an on-premise Teradata data warehouse involves significant effort.


Elastic. MDS tools can scale horizontally or vertically based on demand.

Example: Cloud-based data warehouses like Snowflake automatically adjust resources.

Limited. Legacy systems often struggle to handle sudden growth.

Example: Expanding an on-premise Oracle database requires careful planning.


Flexible. MDS allows your data teams seamless addition or replacement of tools as needs evolve.

Example: Switching from one SaaS data warehouse to another is straightforward.

Rigid. Legacy stacks are less adaptable due to customizations and dependencies.

Example: Migrating from a legacy ETL tool to a new one is complex.


Standardized. MDS tools follow industry best practices, reducing extensive customization.

Example: dbt provides predefined transformations for SQL-based ETL.

Highly Customized. Legacy tools are tailored to specific business requirements.

Example: Custom-built ETL scripts for legacy data pipelines.


Highly Available. MDS components are designed for redundancy and failover.

Example: Cloud services like Google Cloud Storage ensure data availability.

Risk of Downtime. Legacy systems may face downtime during maintenance or failures.

Example: On-premise databases require scheduled downtime for updates.

Ease of Integration

Seamless. MDS tools often have APIs and connectors for smooth data flow.

Example. Integrating Apache Airflow with cloud services is straightforward.

Complex. Legacy tools may lack standardized APIs, making integration challenging.

Example. Connecting a legacy CRM system to a modern analytics platform can be cumbersome.


There are many benefits of using a modern data stack, which include:

Increased Scalability: A modern data stack allows for easy scaling up or down based on company needs, with various cloud computing tools that can be used together or separately.

Adaptability to Changing Needs: A modern data stack is inherently flexible, allowing organizations to adjust their data infrastructure as requirements evolve. Whether it’s accommodating new data sources, modifying pipelines, or integrating additional tools, the flexibility ensures agility in responding to business changes.

Tool Interoperability: Modern data stacks often consist of modular components that seamlessly integrate with one another. This interoperability enables users to mix and match tools based on specific use cases, without being locked into a rigid framework. For example, combining a cloud-based data warehouse with a specialized visualization tool provides flexibility in data exploration and reporting.

Centralized Management: A modern data stack allows for centralized management of data pipelines, making it easier to monitor, troubleshoot, and maintain.

Cost-Effective: By leveraging cloud-based services, organizations can avoid the upfront costs of hardware and infrastructure, paying only for what they use.

Real-Time Data Processing: Modern data stacks enable real-time data processing, allowing data scientists and businesses to make informed decisions based on up-to-date information.

Top 4 Strategies for Automating Your Data Pipeline


While MDS offers many benefits, here are some of the key challenges you should be aware of. 

  • Complexity. The number of choices in the market for ETL tools and destinations can be overwhelming. It can be time-consuming and tricky for you to choose and integrate your MDS due to the abundance of tools and services in the ever-evolving ecosystem. And all of the different tools may still not meet your requirements.

  • Data Silos and Integration Challenges. As data is collected from multiple sources and stored in various systems, integrating disparate datasets while ensuring accuracy can be difficult. The modern data stack embraces flexibility, but this can lead to complexity when it comes to ETL code. Unlike legacy systems with rigid frameworks, modern data stacks often lack a one-size-fits-all solution for ETL. And even with convenient out-of-the-box solutions, there are scenarios where custom pipelines are necessary, especially when specific requirements cannot be met by existing connectors. Your data engineers and analysts may need to build custom ETL pipelines using tools like Airflow, Luigi, or Dagster. These frameworks allow fine-grained control over data workflows but require more technical expertise.

  • Service and Support.  You may experience frustration with resolving an integration problem in a timely way. MDS tool vendors may blame your issue on other vendors. And upgrading one tool may break the integration with other parts of the MDS.

Ideally you can find a comprehensive data foundation that performs most of the MDS functions. This way, you can avoid the time selecting and integrating multiple tools.

Data Integration Challenges & Solutions

Learn how to overcome the top 14 challenges you face.

Accelerate Analytics-Ready Data and Insights With DataOps

A modern DataOps approach to data integration speeds up the discovery and availability of real-time, analytics-ready data to cloud repositories by automating data streaming (CDC), refinement, cataloging, and publishing. Some platforms also support data warehouse automation and data lake creation.

Learn More About Data Integration With Qlik