Amazon S3 has become the backbone of modern data storage, but storage alone does not create value. The real challenge is turning raw files into accessible, governed data that can power analytics and AI without months of engineering effort. Traditional approaches rely on custom, user-defined code and complex data pipelines that are slow to implement, difficult to maintain, and often compromise governance.
Together, Starburst and AWS take a different path. By pairing the durability of Amazon S3 with Starburst Galaxy’s no-code data ingestion, organizations can quickly transform their data into live, governed datasets. This enables real-time insights while preserving the flexibility required to scale analytics and AI across the business.
Let’s explore how this might work in practice.
Why data ingestion is the starting point for analytics and AI
Before any analytics or AI workflow can begin, data must be ingested and made usable. Raw files in Amazon S3 are great for storage, but they are not immediately accessible or governed. Without a reliable ingestion process, teams face brittle pipelines, delayed time-to-insight, and difficulty scaling as data volumes grow.
Why Apache Iceberg is the perfect foundation for all your data
Apache Iceberg is the perfect foundation for Amazon S3 data. Using Starburst, you can transform raw files into structured, governed tables that behave like a database but scale like a data lake. Iceberg brings features such as schema evolution, time travel, and ACID transactions. This not only makes it the perfect foundation of modern lakehouse architectures, it also makes Iceberg an excellent destination for Amazon S3 data.
Starburst Galaxy makes using Iceberg easy
How do you get there? Starburst Galaxy makes using Iceberg easy. Our platform allows for fully managed ingestion pipelines for Amazon S3. Instead of stitching together Spark jobs, custom scripts, or multiple tools, data engineers can continuously ingest files into Iceberg tables through a no-code interface. Once ingested, data is automatically optimized, governed with access controls, and queryable in seconds.
Starburst and Amazon S3, working together
The result is an architecture that combines scalability, governance, and interoperability. By eliminating ingestion complexity, Starburst Galaxy and AWS enable organizations to quickly and consistently hydrate their Iceberg architecture on AWS, ensuring that data is always ready for analytics and AI. As part of this, Starburst is a Financial Services Technology Competency Partner, as well as a data and analytics partner, and a Graviton EC2 Spot service-ready partner.
Let’s look in more detail at how that might take shape in your organization.
How to move from raw data to governed tables with no-code file ingestion
One of the most common challenges in building a data lakehouse is turning raw files into governed, accessible tables. Traditionally, this required custom pipelines, orchestration tools, and manual coding.
With Starburst Galaxy on AWS working together, that workflow becomes simple and fully managed.
Data formats and data models
In practice, the process begins with raw Parquet files stored in Amazon S3. Using Galaxy’s no-code interface, you can ingest these files directly into Apache Iceberg tables without writing a single line of code.
Instead, the ingestion process runs continuously as new files arrive, automatically hydrating Iceberg tables so that data is immediately available for query.
Why no-code file ingestion improves efficiency
This approach reduces operational overhead by removing the need for separate batch jobs or manual refreshes. It also accelerates analytics pipelines, since fresh data is consistently loaded into governed tables that support both SQL-based analytics and AI workloads.
The result is a faster path from raw S3 storage to live insights, all while preserving governance and reliability.
Real-Time analytics with Starburst and Amazon QuickSight
Once data is ingested into Iceberg tables, it becomes immediately usable in Starburst Galaxy for analytics and AI workflows. This means that there is no need to wait for downstream pipelines or additional preparation.
From there, data engineers and analysts can begin working with fresh datasets as soon as they land.
Starburst and Amazon QuickSight
One effective way to implement this workflow is by utilizing Amazon QuickSight. Starburst Galaxy integrates seamlessly with QuickSight, enabling you to easily transform ingested data into live dashboards. With direct integration, queries run in Galaxy can be fed directly into QuickSight visualizations, providing an interactive layer for business users without requiring additional infrastructure.
This connectivity enables organizations to transition from raw data in S3 to governed tables in Galaxy, and then to live dashboards in QuickSight, all within minutes. The result is a streamlined workflow where fresh data flows continuously into analytics, supporting real-time decision-making across the business.
How organizations use Starburst and AWS to create a foundation for all their data
Best practices and real-world customer use cases showing how Starburst ingestion accelerates analytics pipelines, reduces operational complexity, and enables faster business insights.
For more information on this, check out this blog outlining how Amazon S3 and Starburst work together.
Accelerating Your AWS deployment with Starburst
Starburst Galaxy and AWS work seamlessly together, making your data ingestion journey easier than ever before.
Turning raw data in Amazon S3 into governed insights no longer requires complex pipelines or months of engineering. By combining the durability of S3 with Starburst Galaxy’s no-code ingestion and the open standards of Apache Iceberg, organizations can onboard faster, maintain governance, and enable real-time analytics.
Using Starburst and AWS, users ensure that fresh data moves smoothly from storage to query to visualization, without bottlenecks or added infrastructure. This means analytics teams can deliver insights more quickly, and AI initiatives can build on trusted, production-ready data from the outset.
Together, Starburst and AWS provide a foundation that is both powerful and flexible, giving data professionals a practical path to modernizing their lakehouse on the cloud.
Want to know more about how AWS and Starburst work together? Check out Starburst Galaxy on AWS Marketplace.
