What is the modern data stack?
The modern data stack (MDS) is a cloud-native modular tool kit that smoothly handles each data processing stage. Multiple tools work together in this stack, each specializing in specific aspects of data processing — from initial collection to final analysis.
Since this stack uses a cloud infrastructure, it automatically scales with data volume and user demands. Rather than maintaining complex on-premises systems, you can deploy and modify tools as needed.
The modular approach is more agile and cost-effective because if your team wants to add new data sources or change analysis methods, they can swap or add components without disrupting the entire system.
Core components of the modern data stack
A modern data stack architecture is made up of several core components that have individually important roles in the whole journey from raw data collection to turning them into actionable insights:
Data ingestion: Collects and imports data from databases, applications, and third-party platforms into the data stack.
Data storage: Provides a centralized area to house large volumes of structured and unstructured data. Some widely-used data storage providers include Google BigQuery, Snowflake, and Amazon — they support dynamic scalability to store and retrieve data efficiently based on workload needs.
Data processing: Prepares raw data for analysis by cleaning and structuring it through data management and governance tools. This prepares large datasets for downstream use regardless of complexity or volume.
Data transformation: Transforms processed data into an analysis-ready format by applying business logic and calculations. This ensures that the data is standardized and aligned with business requirements and is ready for visualization.
Data visualization: Converts processed data into visual formats like charts and dashboards to make it easier for business users to interpret data insights.
Role of metadata and data lineage
Metadata and data lineage are major components of any data governance strategy because they provide transparency to build trust in a modern data stack. Metadata describes data attributes, such as origin, structure, and format, while lineage traces the journey of data from its source through various transformations.
However, for better metadata management and lineage tracking, you should opt for knowledge-graph-powered data cataloging solutions. They link metadata and lineage with data assets and their transformations within a single and accessible framework. This provides full business context which improves data governance, and maintains system compliance.
Integration and interoperability
Modern data stacks need cataloging tools to support integration and interoperability — for this they use open APIs and extensible architectures. With these APIs, data cataloging tools can easily pull data from different databases, SaaS applications, and real-time streams. This extensibility helps organizations collect structured and unstructured data into centralized platforms like data lakes or warehouses.
Benefits of a modern data stack
A modern data stack allows organizations to use the full potential of their data assets, which comes with many benefits:
Improves scalability and adaptability: MDS scales smoothly with organizational needs because of its cloud-native and modular nature. You can add or upgrade tools to the stack as your data ecosystem evolves. This way it can manage large volumes of data and keep up with expanding workloads without requiring major reconfigurations.
Processes data in real-time: Organizations can analyze data as soon as it is generated to provide faster and more relevant insights. This way your teams can make quicker decisions to respond to market changes and operational needs.
Promotes collaborative environment: It breaks down silos and makes data accessible to different types of users, like analysts and business teams.
Increases transparency: It creates a centralized data environment where changes are tracked automatically and rules are enforced. Traditional silos dissolve between departments — so all teams can work with the same data sets without any issues.
Key tools and technologies in the modern data stack
With data assets increasing everywhere, smart organizations now consider modern data stacks as engines that power efficient decisions. They mix and match tools to create the perfect stack for their organization’s unique needs. Here are some widely-used tools in each layer.
Data ingestion
In the first stage, data ingestion tools collect and transport information from databases, SaaS platforms, APIs, IoT devices, and more into your centralized data environment. They automate this raw data extraction and prepare data for the next stage.
Some of the most popular tools used at this stage are:
Fivetran and Stitch for fast automated ingestion.
Apache Kafka for handling real-time data streams.
Data storage
Once the data is ingested, it needs a place to be processed. That’s why modern data stacks rely on cloud-native storage solutions that are scalable and cost-effective. These storage tools store raw and processed data securely while allowing easy access for analysis.
Some of the most popular tools used at this stage are:
Snowflake, Google BigQuery, and Amazon Redshift for structured data storage.
Databricks and Delta Lake for hybrid lakehouse solutions.
Data transformation
Raw data usually comes with flaws and redundancies. That’s why we use data transformation tools to clean and prepare data for analysis. With these tools, you can remove duplicates and any irrelevant information.
Some of the most popular tools used at this stage are:
dbt (Data Build Tool) for modular and scalable transformations.
Dataform for managing ELT workflows.
Data visualization
Once the data is transformed, visualization tools convert it into easy-to-understand dashboards and reports to present data in a readable format that is accessible for everyone. This helps teams understand that data and draw insights from it to make decisions with clarity and confidence.
Some of the most popular tools used at this stage are:
Tableau, Looker, and Power BI for user-friendly dashboards.
Mode Analytics for advanced and interactive visualizations.
Data governance and lineage
Governance tools are used throughout the data management process to ensure the data complies with laws and is accessible to the right people. In addition, lineage tools map the entire data journey across the organization. This gives full transparency into how data flows and transforms which makes it easier to promote a culture of accountability and collaboration across teams.
Some of the most popular tools used at this stage are:
data.world with its Eureka Explorer™ Lineage for automated column-level lineage.
Informatica and Collibra for strong governance frameworks.
Since a variety of tools are available for each stage, you should select those that align with your organization’s needs and can provide more flexibility for larger data volumes.
Building and implementing a modern data stack
Creating a modern data stack is like assembling a toolkit by following a clear roadmap and adopting the best data stack management practices. So here’s a step-by-step approach to get you started:
Assess business needs: Analyze your current data system and organizational requirements. Identify where data is stored and the challenges in managing it. Then, consult with stakeholders across teams to understand their specific data needs and priorities. This will make sure that the modern data stack addresses real challenges and aligns with business objectives.
Define data goals: Plan what you want to achieve with your modern data stack. Are you aiming for real-time analytics, improved compliance, or better collaboration? This helps define success metrics, such as reduced processing times or improved data accuracy.
Choose compatible tools: Select tools that fit your immediate needs while providing room for growth. For example, cloud-native solutions are best for scalability, along with open APIs for smoother integration. Also, look for user-friendliness to make it accessible to both technical teams and business users. Remember that your choices should align across the stack’s layers to create a cohesive and efficient ecosystem.
Integrate tools: Next, use APIs and automation to connect tools and allow data to flow easily between systems. Test these integrations on a small scale to identify and resolve potential issues early.
Train teams: Provide necessary skills and training to your teams so they can use the modern data stack properly. As part of training, encourage technical and non-technical teams to coordinate — this will set a strong foundation for a collaborative environment.
Check out our podcast on Why are we so bad at this modern data stack?
Best practices for building a modern data stack
While having a roadmap will set you on the right path, you must follow some of the best practices to ace this process:
Start with an iterative approach: Start your journey with the core components, such as data ingestion, storage, and basic visualization tools that address your most pressing needs. Pilot the stack on a specific use case to test functionality and refine workflows accordingly.
Scale over time: Focus on data stack scalability from the start to avoid overbuilding your stack. Add tools to your stack that can handle your current workload but also accommodate future growth. This way, your stack will evolve with increasing organizational priorities and data complexity.
Prioritize agile data governance: Agile governance emphasizes adaptability so your policies and processes can evolve as new regulations emerge or business needs change. Use tools like data.world with knowledge graphs-based architecture to get visibility into data lineage.
Data quality and transparency: Data quality is the backbone of a reliable modern data stack — so create automated data validation and cleansing processes. Transparency is equally important so you must use lineage tracking tools like data.world’s lineage tracking to map data flows.
See how data.world helped WPP manage its modern data stack with full-scale democratization.
Modern data stack trends
The modern data stack is progressing beyond basic storage and processing to address core challenges in data governance. Here are some of the latest MDS trends:
Knowledge graph-driven data governance
Knowledge graph-backed governance tools provide end-to-end transparency to organizations by showing how data flows across systems from ingestion to transformation and final usage.
Metadata management enhances this process by providing detailed context about data assets which makes troubleshooting issues easier. That’s why it has become simple for businesses to empower teams to make informed decisions confidently.
AI and automation
Automation is changing how organizations manage data workflows within the modern data stack. Now, AI-powered tools handle routine tasks like data ingestion or quality checks so teams can focus on strategic analysis and insights. This multiplies the efficiency of data stacks and scales business data operations without overburdening teams.
For example, automated anomaly detection flags issues before they grow through systems to protect data integrity.
Open and integrated systems
Gone are the days of siloed and proprietary data systems. Modern data stacks embrace open standards and interoperability so multiple tools can work together without hindrance. This shift creates a more connected ecosystem where data flows freely across different stages and visualization layers.
Open APIs and extensible architectures are also becoming popular. They help organizations integrate the best tools with their data ecosystems and avoid vendor lock-in. As a result, businesses can build flexible stacks that grow with their needs, ensuring their data infrastructure remains future-proof and accessible.
data.world’s role in the modern data stack
Modern data stacks are a necessity for any data-driven organization and luckily there are tools like data.world which make this process hassle-free. data.world is a leading solution in the modern data stack ecosystem that simplifies data discovery and governance.
It is backed by a knowledge graph technology that can connect data, people, and insights across your organization. The knowledge graph architecture presents a complete view of your data assets by mapping relationships between datasets and business context. This means you can:
Track data lineage from source to insight.
Make decisions with verified, trusted data.
Maintain data quality through automated monitoring.
In addition, data.world's agile governance approach balances flexibility with control. Your teams can collaborate freely while automated policies ensure compliance and security. This means faster time to insight without compromising standards.
Book a demo today and see how data.world can help.
