Data Platform

A data platform is a technology stack or single solution for managing enterprise data. This system ingests and prepares data at scale for operational or analytical use.

Most solutions include analytics tools to run complex queries and support data-intensive machine learning and artificial intelligence projects.

A modern data platform integrates enterprise data sources to support operations, data analysis, and decision-making. An evolution of enterprise data systems, these solutions provide end-to-end data management at scale.

This guide will discuss data platforms, their components, and their benefits before discussing an example of a modern data analytics platform.

Variations of data platform

The modern data platform evolved from traditional enterprise data platforms, systems that unified on-premises databases and storage solutions to support daily business operations. Today’s data platforms continue to serve this role and more.

Cloud data platform: Moving the original on-premises platform to the cloud lets enterprises take advantage of cloud computing’s performance, availability, resiliency, and efficiency.

Data analytics platform: A data analytics platform is optimized for generating insights from any type of enterprise data. Business intelligence analysts get visualization and analytics tools to support decision-makers. Data scientists get performant query tools for big data workloads.

Customer data platform: Customer data platforms focus on the needs of marketing teams by generating insights from hundreds of variables to make customer intent more predictive and enhance the customer experience.

What is the difference between a data platform and a database?

A database is a structured set of data usually accessed through the SQL statements of a database management system.

A data platform unifies multiple databases and other forms of object storage within a single interface.

Key components of a data platform

Data platforms are end-to-end data management solutions. From data collection to exploration and analysis, these systems provide a unified interface for managing data and generating actionable insights.

Data ingestion

Ingestion is the “extract” portion of the extract, load, transform (ELT) or extract, transform, load (ETL) pipelines data engineering teams use to import data from multiple data sources.

These sources may be transactional systems or third-party repositories, in which case the pipelines load data in batches. Other sources, such as e-commerce clickstreams, require real-time streaming pipelines.

Data storage

Data platforms load the ingested data into one or more storage systems: databases or data warehouses for structured data or data lakes for structured, semi-structured, and unstructured data.

These systems could be on-premises, but companies increasingly use cloud data storage systems that offer a better balance of cost and performance. 

From here, a single point of access to all your data would accelerate time to insight. Discover, govern, analyze, and share data from a single data platform.

Data transformation

Data platforms clean, transform, and prepare data to make datasets usable and discoverable. In addition to eliminating duplicate entries, data transformation brings consistency to data from different sources. For example, pipelines transform date and time fields in each dataset to comply with the company’s standard format.

To improve discovery, engineers update data catalogs and make the source’s metadata consistent with the company’s taxonomy.

Data governance

Data governance policies provide a framework for managing data across the enterprise, including the data quality standards and metadata taxonomies touched on earlier. More importantly, governance defines how the company handles data and the conditions allowing users to access data.

Data platforms automate governance enforcement. For instance, the system can ensure user access to data meets local privacy protection regulations.

Data integration

Ingestion pipelines will route data to the most appropriate type of storage anywhere within the organization or in the cloud. Data platforms combine these various storage locations within a single interface, eliminating the organizational data silos that undermine data usability.

Data processing

Data processing — collects, manipulates, and organizes data to produce useful information. It involves a series of operations and transformations (for example,  SQL query engine for big data analytics) that turn raw data into meaningful insights or knowledge.

Data visualization and analysis

A company’s ability to generate data insights is a tremendous competitive advantage that requires robust analytics capabilities. At the same time, they make data more accessible by letting analysts use familiar apps such as Microsoft Excel or Tableau for visualization and analysis.

What are the benefits of a data platform?

An effective data analytics platform’s most valuable feature is the degree of self-service it affords. Letting users securely, appropriately, and directly access data unlocks insights faster and frees the organization from the constraints of centralized data management.

Data-driven decision-making

Data platforms underpin data-driven corporate cultures. Analysis is no longer limited to a core group of experts. Anyone can use data to evaluate the state of the business and develop insights that inform more effective, objective decisions.

Business agility

Data-driven decision-making cultures give executives faster access to better analyses so they can take more effective action as soon as issues and opportunities arise.

Analysts generate insights faster when they can access data directly without waiting for busy data teams to develop custom pipelines.

Further improving business agility, data platforms give dashboards instant access to the latest data so executives always know the state of their business.

Business competitive advantage

The self-service capabilities of a data platform drive agile, effective decision-making. At the same time, their ability to balance access with governance and compliance ensures the business can navigate complex regulatory environments.

The capacity to move fast without breaking things lets companies take disciplined approaches to risk without sacrificing their abilities to outmaneuver competitors and better serve customers.

Why use a data platform? How to evaluate a data platform

Given a data platform’s advantages, businesses must carefully consider potential solution providers. These criteria will help you assess candidate data platforms.

Needs and use cases

First, evaluate your business needs to identify critical use cases within your organization. A marketing-driven initiative may prioritize a customer data platform. However, that would limit its usefulness for the entire company.

Tools and capabilities

Consider how well each solution addresses the key data platform components within the context of your existing tech stack. Does a vendor offer a complete solution? Or will you have to integrate additional technologies?


Your company may not run big data projects now, but your data platform must be scalable. Modern enterprises generate ever-increasing amounts of data. Your data platform must be able to keep up.


Will a data platform require investments in additional infrastructure? For example, a platform that relies on its own centralized data storage will require investments in storage infrastructure. Data platforms can also affect compute expenses if they do not have cost-aware query engines.

Performance and Speed

Cloud-native data platforms offer performance advantages over on-premises apps. Also, consider the platform’s query optimizations. Pushdown queries, for example, run operations on the source data’s storage system to reduce network traffic and speed queries.


Look into the platform’s data security features. Does it control access based on user roles and attributes of each dataset? What APIs does it offer to integrate these controls with applications?

Integration capabilities

Your storage infrastructure will impose unique integration requirements on a data platform. Ask vendors whether they can integrate every data source, whether legacy on-premises systems or modern cloud services.


A data platform’s user experience will determine whether you can maximize the benefits of self-service analytics. Will people have to change their workflows? Or can they keep the SQL tools they’ve been using for years?

Example of a data platform: Building a modern data platform with Starburst

Starburst Galaxy is a modern data lake analytics platform that integrates disparate enterprise data sources and securely democratizes access. Among its benefits:

Enabling data access — Any authorized employee in any department or location can access every data source directly to accelerate time to insight and empower data-driven decision-making.

Uses the tools you know — Your analysts can use their existing SQL tools to run queries through Starburst Galaxy.

Decoupling storage and compute — Starburst leaves data at the source, allowing you to optimize your storage and compute infrastructures to reduce costs and increase performance.

Query optimizations — Starburst’s massively parallel query engine offers dynamic filtering, pushdown queries, and other performance-enhancing features.

Role and attribute-based access controls — Create granular access control rules to secure data and protect personal privacy across regulatory jurisdictions.

Enterprise connectors — Starburst Galaxy offers over 50 connectors to enterprise data solutions, including Amazon Web Services (AWS), Microsoft Azure, Google Cloud, and Snowflake.

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Starburst Galaxy

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  • Query your data lake fast with Starburst's best-in-class MPP SQL query engine
  • Get up and running in less than 5 minutes
  • Easily deploy clusters in AWS, Azure and Google Cloud
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