What is the modern data stack?

Traditional legacy data stacks (LDS) rely on rigid, on-premises infrastructure that can struggle with scalability, flexibility and real-time processing. In contrast, the MDS offers a cloud-native, modular solution designed to streamline automation, optimize costs and accelerate insights. Perhaps most notably, MDS empowers self-service analytics and artificial intelligence (AI) applications that many companies rely on today.

Data platforms serve as the backbone of modern software development, offering a network of tools and frameworks to build and manage applications efficiently. Think of MDS as the assembly line of digital operations—each component plays a role in moving data seamlessly from collection to analysis. By automating and scaling workflows, MDS ensures that organizations can process, store and use data with precision, driving better decision-making and innovation.

The primary functions of the MDS include:

• Storage: Consolidating data in cloud data warehouses, data lakes or hybrid data lakehouses.
• Ingestion: Moving data from various sources into a data pipeline for analysis.
• Transformation: Converting raw data into structured formats through processes such as cleaning, normalization and aggregation.
• Business intelligence (BI) and analysis: Using analytics tools to generate insights, visualize trends and power machine learning (ML) models.
• Data observability: Monitoring, managing and maintaining data to ensure quality, availability and reliability.

Between 2012 and 2016, a major shift in data workflows reshaped how organizations store and process data. Cloud-based platforms such as Snowflake, Google BigQuery and Amazon Redshift popularized cloud data warehouses, offering unprecedented scalability, compute power and efficiency.

At the same time, organizations shifted from traditional extract, transform, load (ETL) workflows—where data was transformed before storage—to extract, load, transform (ELT), where data is stored first and processed later. This approach increased flexibility and made real-time insights more accessible by using connectors, or extensions, to streamline data movement.

During this period, tools such as Fivetran and Airflow automated data ingestion, while platforms such as Tableau, Power BI and Looker revolutionized BI. Reverse ETL enhanced data flow, pushing insights from warehouses into operational systems such as customer relationship management systems (CRMs)—enabling better automation, decision-making and personalization. These innovations laid the foundation for the MDS, enabling more scalable, automated and flexible data workflows. By streamlining data movement and integration, organizations achieved greater operational agility.

To understand the significance of the MDS, it’s helpful to compare it with the LDS:

Key differences at a glance

• Infrastructure: LDS relies on physical servers; MDS is cloud native.
• Scalability: LDS requires manual scaling; MDS scales dynamically with demand.
• Integration: LDS relies on custom workflows; MDS automates data ingestion.
• Flexibility: LDS is monolithic; MDS is modular, enabling seamless tool integration.
• Analytics: LDS supports batch reporting; MDS offers real-time insights and interactive dashboards.
• Cost: LDS involves significant upfront investment; MDS uses pay-as-you-go models.

Traditional LDS are built on on-premises infrastructure, requiring significant investments in hardware, maintenance and manual scaling. They rely on ETL workflows, meaning data must be cleaned and structured before storage. While effective for static reporting, LDS struggle with real-time processing, scalability and handling unstructured data such as sensor logs, images or audio.

MDS solves these challenges with a cloud-native, modular approach, allowing organizations to store, process and analyze vast amounts of structured and unstructured data more efficiently. ELT workflows provide greater flexibility, often by using Python-based scripting for automation and data processing.

Unlike LDS, which requires costly infrastructure expansions, MDS offers on-demand scalability and its modular nature means that businesses can integrate data stack tools without vendor lock-in. Finally, MDS enables real-time insights and AI-driven analytics and automation, making data more accessible and actionable across an organization.

The MDS consists of several core components including:

• Data storage
• Data ingestion
• Data transformation
• BI and analytics
• Data observability

The data storage layer serves as the foundation of the MDS, providing a centralized environment for managing structured and unstructured data. Organizations can choose from storage solutions from providers such as IBM, AWS or Microsoft based on factors such as cost, performance and scalability needs.

Types of data storage:

• Data warehouses: Structured data from multiple sources aggregates into a single, central data store optimized for analytics, AI and ML. Providers include Snowflake, GoogleBigQuery and Amazon Redshift.
• Data lakes: Data lakes hold raw, structured and unstructured data in various formats. They allow organizations to store vast amounts of data, often reaching petabyte scale and beyond for AI, ML and research applications. Many data lakes were initially built on Hadoop, but most modern architectures now use cloud object storage solutions such as AWS S3 and IBM Cloud® Object Storage (COS).
• Data lakehouses: A hybrid approach that combines the scalability of data lakes with the structured query capabilities of data warehouses. In other words, a seemingly limitless archive with an incredibly precise index. Lakehouses store structured, semistructured and unstructured data while supporting BI, analytics and ML workloads.

Data ingestion is the process of collecting and moving data from various sources into a centralized storage system for processing and analysis. The effectiveness of a data pipeline depends on how well big data is ingested and integrated. Data engineers play a crucial role as errors at this stage can cause issues downstream in analytics and AI models.

Types of data ingestion:

• Batch processing: The most common ingestion method, batch processing collects data in groups (or batches) and sends it to storage at scheduled intervals. This approach is cost-effective and is ideal when real-time updates are not required.
• Real-time processing: Also called “stream processing,” this method continuously ingests and processes data as it is generated. It is critical for AI applications, fraud detection and real-time analytics. However, it requires higher computational resources.

Automated ingestion tools such as Apache Airflow, Stitch and Fivetran help organizations seamlessly move data between systems, reducing manual effort and improving data integration efficiency.

Raw data is often inconsistent or unstructured when ingested, making it difficult to analyze in its native format. The data transformation layer ensures that data is clean, structured and optimized for tasks such as analytics, reporting and ML.

Common data transformation tasks:

• Data cleaning: Removing errors, null values and duplicates.
• Normalization: Standardizing data formats for consistency.
• Aggregation: Summarizing large datasets for reporting.
• Merging: Combining data from multiple sources into a unified dataset.

Historically, data transformation followed the ETL workflow. However, with the rise of cloud-based storage solutions, most organizations now use ELT processes. Data transformation tools such as dbt and Dataform automate workflows and help ensure that data is accurate, consistent and ready for analytics.

The BI and analytics layer converts raw data into actionable insights through data analytics, visualization, trend analysis, structured query language (SQL) querying and AI-driven automation. Tools such as Tableau, Power BI and Looker provide interactive dashboards and real-time data analysis, helping organizations track performance and refine strategies.

Beyond data visualization, AI-powered analytics and data science enhance decision-making by detecting anomalies, predicting trends and automating workflows—all of which depend on strong data management practices. Whether used for customer behavior analysis, financial forecasting or supply chain optimization, BI tools ensure that businesses can use data for both strategic and operational environments.

Data observability ensures data quality, availability and reliability by continuously monitoring data health. This layer helps data teams detect broken pipelines, missing records or slow processing before they affect analytics.

Observability tools such as Monte Carlo and Datadog provide insight into data flows, allowing engineers to diagnose and enhance workflows in real time. By resolving issues proactively, organizations can maintain data integrity and improve data-driven decision-making. Strong observability practices support a well-structured data model and ensure that stakeholders can trust insights throughout the data lifecycle.

Beyond the five fundamental layers, MDS often include other components to enhance accessibility and functionality. These components can include: 

• Data discovery: Data discovery helps organizations surface and assess hidden or siloed data sources, ensuring data teams can extract valuable information and put it to work effectively.
• Data governance: Establishing policies and safeguards can help ensure security, regulatory compliance and data consistency. By managing data flows and enforcing a structured data model, governance supports efficient workflows and helps ensure real-time compliance monitoring.
• Data cataloging: Teams can use metadata to create a structured inventory of data assets across data warehouses, data lakes and other storage environments. A well-maintained catalog supports the entire data lifecycle and allows stakeholders to quickly access and use essential information.
• ML and AI: Some data platforms integrate ML and AI to refine data processing, improve predictive modeling, automate insights and enhance anomaly detection. ML models also optimize workflows by identifying inefficiencies and suggesting real-time improvements for data teams.

Companies can deploy their own MDS to improve AI-powered personalization, customer insights, logistics and fraud detection.

Businesses that rely on real-time decision-making, automation and AI-driven insights use MDS to enhance data accessibility and streamline operations. Industries such as technology, finance, healthcare, e-commerce and logistics often use MDS to integrate large-scale data sources, improve analytics capabilities and support more efficient decision-making and orchestration.

However, in a world where data informs nearly every aspect of business operations, the real question isn’t which industries benefit from MDS, but how it can help organizations boost efficiency and adaptability. As AI adoption, open-source tools and real-time data processing continue to evolve, MDS is becoming a more common approach for organizations to modernize their data architecture.