Data Platform: Core Functions and 6 Tools to Know in 2024

February 14, 2025

What Is a Data Platform?

A data platform is a system to manage, process, store, and analyze data from various sources. It provides an integrated environment where data can flow through different stages—from ingestion to transformation and analysis—offering tools for data management and accessibility.

Data platforms are used by data engineers, data scientists, business analysts, and IT administrators. These users typically aim to simplify data workflows, ensure the quality of data, and derive actionable insights. For example, data engineers use platforms to automate data pipelines, while analysts leverage the platform's analytics capabilities to uncover trends and patterns.

Organizations benefit from data platforms by gaining centralized control over their data, enabling better decision-making, enhancing collaboration, and improving operational efficiency. Additionally, the scalability of these platforms ensures that they can adapt to growing data needs without compromising performance.

The Benefits of Data Platforms

Using a data platform provides the following advantages.

Centralized Data Management

Centralized data management consolidates data from diverse sources into a single, unified platform. This approach simplifies data governance by providing a central location to enforce security policies, ensure compliance, and manage access controls. It also eliminates data silos, making it easier to maintain consistent data quality and prevent duplication.

Scalability

A scalable platform allows organizations to add resources seamlessly, ensuring performance remains consistent without service disruption. This capability is crucial for organizations operating in rapidly changing markets or expanding their data operations. Scalable platforms also offer cost efficiency by aligning resources to current needs, reducing waste.

Improved Data Accessibility

Accessible data enhances collaboration among team members, fostering more informed decision-making processes. Easy access is enabled by interfaces and connectivity, reducing time spent in data retrieval and boosting productivity. Enhancing accessibility also democratizes data usage within an organization, allowing users to generate insights independently.

Analytics and Machine Learning Integration

Integrating analytics and machine learning within data platforms enables organizations to gain deeper insights and drive predictive decision-making. These capabilities allow organizations to process large data sets at speed, identifying patterns and trends that are not visible through traditional analysis methods.

The Core Functions of Modern Data Platforms

A data platform should support the following functions.

Data Storage and Processing

Data storage and processing layers ensure data is accessible, stored securely, and processed in a format suitable for analysis. With technologies like cloud storage and distributed databases, organizations can store data at scale while maintaining rapid access for real-time analysis.

Storage solutions are complemented by processing capabilities that transform raw data into structured insights. Processing layers use parallel computing and in-memory analytics to handle complex computations, enabling companies to keep pace with data velocity.

Data Ingestion

Data ingestion is the process of importing and transferring data from multiple sources into a centralized platform for further processing. An efficient ingestion layer supports batch and real-time data needs, ensuring that no data is overlooked or delayed in being analyzed. Proper data ingestion setups accommodate structured and unstructured data formats.

Data ingestion capabilities are built on scalable architectures that manage changing data loads. They provide data validation mechanisms, ensuring data accuracy and reliability before processing. This pre-processing step is vital for maintaining data quality and integrity.

Data Transformation and Modeling

Data transformation involves converting raw data into a consumable format while modeling defines the shape and structure of data for analysis. This phase ensures that data aligns with business rules and objectives, delivering meaningful insights. Transformation tools automate processes like cleaning, aggregating, and enriching data, making it analysis-ready.

Data modeling techniques establish relationships among data entities, supporting complex queries and analytics tasks. Effective data transformation and modeling improve data usability, allowing users to derive actionable insights.

Business Intelligence (BI) and Analytics

Business intelligence tools present data through dashboards and reports, aiding stakeholders in strategic planning and operational decisions. These tools allow for real-time monitoring of key performance indicators.

Advanced analytics further extends BI capabilities by supporting predictive and prescriptive analysis. This aspect of data platforms enables organizations to identify future opportunities and threats, optimizing strategic initiatives.

Machine Learning and AI

Modern data platforms integrate machine learning and AI to enable advanced analytics and automation. These tools analyze vast datasets to uncover patterns, optimize operations, and generate predictions. For example, ML models can detect anomalies in real-time, supporting fraud detection or predictive maintenance in industrial systems.

Additionally, AI capabilities improve decision-making by automating routine tasks, such as data classification or model deployment. This integration allows organizations to scale their data initiatives more easily, providing faster insights and actionable intelligence.

Data Observability

Data observability provides insight into the health and performance of data systems, ensuring reliability and efficiency across data flows. By monitoring data pipelines, platforms can detect errors, anomalies, and bottlenecks, helping to mitigate issues before they impact operations. Observability tools offer dashboards and alerts that give teams visibility into data processes.

This monitoring extends across all layers, contributing to data quality and compliance. Observability is critical for diagnosing issues quickly and maintaining seamless data operations.

Data Orchestration

Data orchestration is the coordination of complex data processes, simplifying the flow of data across platforms and systems. It involves automating workflows, ensuring that data moves seamlessly from ingestion through transformation to analytics. Orchestration tools manage dependencies and scheduling, reducing manual intervention.

Through orchestration, organizations can achieve more precise data management, optimizing resources and minimizing errors. This automation reduces bottlenecks, allowing more agile responses to data processing needs.

Data Platform vs Data Architecture

A data platform refers to the integrated set of tools and infrastructure used to collect, store, process, and manage data, providing an operational framework for handling data. It includes the hardware, software, and systems that allow data to be efficiently ingested, stored, processed, and analyzed.

Data architecture defines the structural framework or blueprint for how data is managed, stored, and moved throughout an organization. It governs the design principles, standards, and processes that ensure data is accessible, secure, and properly organized.

While a data platform provides the tools and operational environment, the architecture outlines the structure and governance needed for effective data management.

Data Platform vs Data Warehouses and Data Lakes

A data platform encompasses a broad set of functions, including data ingestion, storage, processing, transformation, and analysis. It integrates various data technologies to manage data across its lifecycle.

A data warehouse is a specialized component focused on storing (primarily) structured data from multiple sources, optimized for querying and reporting. Data warehouses are suitable for historical data analysis and business intelligence tasks.

A data lake is another type of storage system within a data platform, designed to hold large amounts of raw, unstructured, or semi-structured data. Unlike data warehouses, data lakes store data in its native format until it's needed for processing.

Data Platforms vs ETL Pipelines

An ETL pipeline (Extract, Transform, Load) refers to the processes that move and transform data from one system to another, typically from raw data sources into a more usable format in databases or data warehouses. It focuses on data flow and transformation logic, ensuring data is clean and ready for analysis.

A data platform provides an environment that includes ETL pipelines but also covers data storage, processing, analytics, and governance. ETL pipelines are just one component within the broader scope of a data platform, which supports the entire data lifecycle from ingestion to advanced analytics.

Data Platforms vs Data Analytics

A data platform provides the infrastructure and tools to manage data, enabling various data-related tasks, including analytics. It offers storage, processing power, and integration capabilities for data from multiple sources, enabling a smooth workflow for analysis.

Data analytics refers to the process of analyzing data to extract actionable insights. While a data platform supplies the necessary environment to support analytics, data analytics focuses on the methodologies and tools used to interpret data, such as statistical analysis, machine learning, or business intelligence tools.

Notable Data Platforms

1. Dagster

Dagster is an open-source data orchestration platform for the development, production, and observation of data assets across their development lifecycle, with a declarative programming model, integrated lineage and observability, data validation checks, and best-in-class testability.

Key features of Dagster:

Data asset-centric: Focuses on representing data pipelines in terms of the data assets (e.g., tables, machine learning models, etc.) that they generate, yielding an intuitive, declarative mechanism for building data pipelines.
Observability and monitoring: Aims to be the “single pane of glass” for your data team, with a robust logging system, built-in data catalog, asset lineage, and quality checks.
Cloud-native and cloud-ready: Provides a managed offering with robust, managed infrastructure, elegant CI/CD capability, and multiple deployment options for custom infrastructure.
Integrations: Extensive library of integrations with the most popular data tools, including the leading cloud providers (AWS, GCP, Azure), ETL tools (Fivetran, Airbyte, dlt, Sling), and BI tools (Tableau, Power BI, Looker, and Sigma).
Flexible: As a Python-based data orchestrator, Dagster affords you the full power of Python. Using Pipes, It also lets you execute arbitrary code in other programming languages and on external (remote) environments, while retaining Dagster’s best-in-class observability, lineage, cataloging, and debugging capabilities.
Declarative Automation: This feature lets you go beyond cron-based scheduling and intelligently orchestrate your pipelines using event-driven conditions that consider the overall state of your pipeline and upstream data assets. This reduces redundant computations and ensures your data is always up-to-date based on business requirements and SLAs, instead of arbitrary time triggers.

Learn more about the Dagster platform

2. Snowflake

Snowflake is a fully managed data platform supporting the modern AI Data Cloud. It enables organizations to securely manage data at scale while offering tools to productize AI, analytics, and applications.

Key features of Snowflake:

Elastic compute: Supports diverse workloads, including streaming pipelines, AI, and analytics, with flexible compute options like GPUs.
Unified data access: Consolidates structured, semi-structured, and unstructured data with nearly infinite scalability and interoperability.
Cortex AI: Enables serverless large language models (LLMs) for natural language processing and custom summaries at scale.
Fully managed cloud services: Automates complex operations and applies performance improvements, reducing overhead.
Cross-cloud collaboration: Supports data sharing, governance, and business continuity across different cloud environments.

3. Databricks

Databricks is a unified data platform that simplifies data and AI usage across an organization. Built on a lakehouse architecture, it combines data management and governance with AI capabilities, making it easier to understand enterprise data.

Key features of Databricks:

Data Intelligence Engine: Utilizes generative AI to automatically optimize performance, infrastructure, and workflows based on your data’s specific needs.
Unified lakehouse architecture: Provides a single platform for managing all data types with integrated governance, ensuring easy access and collaboration.
Natural language interface: Simplifies data discovery and application development with large language models (LLMs), allowing users to ask questions or write code using natural language queries.
End-to-end AI and MLOps: Supports the entire AI lifecycle, from building models with APIs like OpenAI to developing custom solutions, while maintaining strict data privacy and intellectual property control.
Security: Ensures secure, private AI development with a unified approach to governance and data protection.

4. Google BigQuery

Google BigQuery is a fully managed, serverless data platform that enables organizations to analyze massive datasets with built-in machine learning, geospatial analysis, and business intelligence features. With its distributed architecture, BigQuery separates compute and storage layers, allowing management and real-time analysis of structured and unstructured data.

Key features of Google BigQuery:

Serverless architecture: Automatically manages infrastructure, enabling users to run queries without provisioning or scaling resources.
Separation of compute and storage: Ensures independent, dynamic resource allocation for optimal performance without downtime.
Built-in machine learning and geospatial analysis: Provides integrated tools for predictive analytics and geographic information systems (GIS), simplifying complex data analyses.
Multi-language support: Enables data analysis with SQL, Python, Java, and other popular languages.
Scalable streaming and batch ingestion: Handles continuous data ingestion and queries terabytes of data in seconds or petabytes in minutes.

5. Microsoft Fabric

Microsoft Fabric is a unified data platform that helps accelerate AI innovation, providing teams with integrated tools for analytics, data management, and artificial intelligence. By combining AI-powered capabilities with mission-critical infrastructure, Fabric enables organizations to harness their data and drive smarter decision-making.

Key features of Microsoft Fabric:

AI-powered tools: Provides tailored solutions for a range of data projects, enabling teams to build AI-ready workflows and applications more easily.
Unified data integration: Offers tools to unify data estates, simplifying complex ETL scenarios and ensuring seamless data flow across the organization.
Data engineering: Supports the design of systems capable of managing and analyzing large-scale datasets.
Data science workflows: Includes AI tools to create end-to-end workflows, enrich data, and deliver actionable insights.
Scalable data warehousing: Features independently scalable compute and storage with industry-leading SQL performance for optimized data management.

6. Apache Kafka

Apache Kafka is an open-source distributed event streaming platform for high-performance data pipelines, real-time analytics, data integration, and mission-critical applications across various industries. It is offered under the Apache-2.0 license. It enables organizations to handle vast amounts of data while ensuring high availability and fault tolerance.

Key features of Apache Kafka:

High throughput: Capable of delivering messages at network-limited speeds with latencies as low as 2 milliseconds, supporting trillions of messages daily.
Scalable architecture: Easily scales up to thousands of brokers, petabytes of data, and hundreds of thousands of partitions, while elastically managing storage and processing.
Durable storage: Stores streams of data safely in a fault-tolerant, distributed cluster that ensures data integrity and availability.
Built-in stream processing: Offers exactly-once processing and supports complex stream transformations, joins, and aggregations using event-time semantics.
Extensive connectivity: Integrates with hundreds of event sources and sinks, including databases, cloud services, and messaging systems via the Kafka Connect interface.

Best Practices to Build an Effective Data Platform

Here are some of the measures organizations should consider to provide an effective platform for their data.

Modular Architecture

A modular architecture ensures flexibility, scalability, and ease of maintenance. In a modular setup, different components such as data ingestion, storage, processing, and analytics are decoupled, allowing each module to be developed, updated, or replaced independently without disrupting the entire system.

This approach also enables integration with new technologies and tools, allowing organizations to adapt quickly to changing business needs or innovations in the data space. A modular architecture promotes reusability and reduces development time, as existing modules can be leveraged across multiple use cases.

Batch and Streaming Support

The data platform must support both batch and streaming data processing to handle diverse data needs. Batch processing is suitable for large volumes of data that can be processed at scheduled intervals, such as overnight data jobs or monthly reports. It is efficient for tasks that do not require real-time results.

Streaming support is important for use cases requiring real-time data processing, such as fraud detection, monitoring systems, or dynamic pricing models. Streaming enables immediate data analysis and decision-making, providing organizations with timely insights.

Automated ETL/ELT Pipelines

Automated ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) pipelines are critical for managing the continuous flow of data from various sources into the data platform. Automation ensures that data is consistently extracted, cleaned, and transformed without manual intervention, reducing errors and saving time.

ETL pipelines are typically used when data transformation is needed before loading it into a target system, such as a data warehouse. ELT is beneficial when data is loaded in its raw form and transformed later, often using the processing power of the data platform itself. Both approaches should be automated to streamline data ingestion and improve reliability.

Data Storage Optimization

Optimization techniques such as data partitioning, compression, and tiered storage (storing frequently accessed data in faster, more expensive storage, while archiving less-used data in cheaper, slower storage) can significantly reduce storage costs and improve performance.

Effective storage optimization also includes managing data redundancy and ensuring that storage systems are scalable to handle future data growth. Leveraging cloud storage solutions with built-in optimization capabilities, such as auto-scaling and distributed storage, ensures that the platform can meet data demands without bottlenecks.

Data Catalogs

A data catalog provides a comprehensive inventory of data assets, including metadata, data lineage, and data ownership, making it easier for users to find and understand available datasets. Implementing a data catalog promotes data discoverability and governance within an organization.

Data catalogs help ensure that data is well-documented and easily discovered, reducing duplication of effort and increasing efficiency in data analysis. They also support compliance with regulatory requirements by tracking data usage and ensuring proper access controls are in place.

Performance Optimization

Performance optimization is crucial for ensuring that a data platform can process and analyze large datasets quickly and efficiently. This can be achieved through techniques such as query optimization, caching frequently used data, and using distributed computing to parallelize tasks across multiple nodes.

Additionally, monitoring tools should be used to identify performance bottlenecks, such as slow-running queries or overloaded resources. Optimizing the platform's performance ensures that data pipelines run smoothly, minimizing delays and enabling faster insights.

Conclusion

Building an effective data platform requires careful consideration of modularity, scalability, and automation to ensure seamless data processing and analytics. By integrating both batch and streaming capabilities, optimizing data storage, and utilizing automated pipelines, organizations can streamline their data workflows and unlock valuable insights. Adopting performance optimization techniques and proper governance through data catalogs further ensures that the platform remains efficient, compliant, and ready to scale with evolving business needs.

Dagster enables these best practices and more by serving as the unified control plane for your data platform. You can use it to define and orchestrate your workflows in a declarative manner and improve data governance and developer productivity with its native support for data lineage and observability, data validation checks, and best-in-class testability.