For teams operating heavily within the Amazon Web Services ecosystem, the decision to centralize data often begins and ends with Amazon Redshift. As a mature, fully managed data warehouse, it has served as the bedrock for business intelligence and reporting for over a decade. However, as data volumes scale and the variety of data sources increases, the traditional model of moving every byte into a single proprietary warehouse is facing new scrutiny. You must decide whether the benefits of a centralized warehouse outweigh the operational overhead and architectural constraints that come with it.
TL;DR
* Amazon Redshift remains a powerful choice for structured business intelligence and low-latency reporting within the AWS ecosystem.
* Centralization often introduces challenges such as concurrency limits, maintenance burdens like vacuuming, and proprietary storage lock-in.
* An open lakehouse architecture using Apache Iceberg on Amazon S3 provides more flexibility for heterogeneous data environments.
* Starburst offers a federated query layer that allows you to access data across multiple sources and cloud platforms without the need for data centralization.
* The choice is rarely binary. Many organizations find success by using Redshift for core use cases while leveraging Starburst for large-scale lakehouse analytics and cross-source exploration.
The strengths of the Amazon Redshift ecosystem
Amazon Redshift is built on a foundation of MPP architecture, which allows it to handle complex analytical queries by distributing the workload across multiple nodes. For many data professionals, the primary draw is its deep integration with other AWS services. Features like Amazon Redshift Serverless and RA3 instances have modernized the platform by separating compute from storage, allowing for more independent scaling than earlier versions of the service.
The introduction of Redshift Spectrum also expanded the warehouse reach, enabling you to query data directly in Amazon S3. This was a significant step toward a more hybrid model, though Spectrum performance can be uneven compared to data stored locally in the proprietary Redshift format. The zero-ETL initiatives from AWS aim to simplify the data ingestion process from services like Amazon Aurora, making it easier to bring operational data into the warehouse environment for analysis.
For structured datasets where performance and predictable BI tool integration are the top priorities, Redshift is a formidable tool. It provides a familiar SQL interface and supports a vast ecosystem of third-party visualization and reporting applications. When your data is already clean, structured, and primarily resides in AWS, the path of least resistance often leads to centralization in Redshift.
The hidden costs of centralization
While the single source of truth model is conceptually appealing, the practical reality of centralizing all data in a warehouse often leads to significant friction. One of the most common bottlenecks is concurrency. Amazon Redshift typically has a concurrency limit of around 50 parallel queries. While features like Concurrency Scaling can mitigate this by adding transient capacity, this often results in unpredictable costs that can escalate quickly during peak usage periods.
Maintenance is another factor that data platform practitioners must manage. Despite being a managed service, Redshift requires ongoing tuning to maintain optimal performance. This includes managing distribution and sort keys, as well as performing regular vacuum and analyze operations to reclaim space and update statistics after large data modifications. For teams with limited engineering resources, these tasks represent a persistent operational burden.
There is also the issue of proprietary storage. When you load data into Redshift, it is stored in a format that is only accessible through the Redshift engine. This creates a level of lock-in that can make it difficult to use other specialized tools for machine learning or data science without first exporting the data back to an open format like Parquet or Avro on Amazon S3. As you consider whether you should replace your cloud data warehouse, the long-term flexibility of your storage layer becomes a critical consideration.
Data gravity and the burden of movement
Centralizing data in Redshift requires moving data from its point of origin to the warehouse. This movement is not free. It involves egress costs, the development of ETL pipelines, and the inevitable latency that comes with batch processing. As data volumes grow into the petabyte range, the time it takes to move data can become a significant barrier to real-time decision making.
Data gravity suggests that as a dataset grows, it becomes harder to move. By forcing all data into Redshift, you are essentially fighting the natural gravity of your data sources. This is particularly problematic for certain types of data, including logs, clickstream data, and IoT sensor data that are naturally collected in a data lake. Attempting to force these high-volume, semi-structured datasets into a rigid warehouse schema often leads to brittle pipelines and high storage costs.
Furthermore, the engineering effort required to maintain these pipelines is substantial. Every time a source system changes its schema, the ETL pipeline must be updated, and the Redshift table needs to be migrated. This creates a dependency on data engineering teams that can slow down the entire organization. In a decentralized environment, analysts can often access data directly at the source, bypassing these bottlenecks.
The rise of the open lakehouse on Amazon S3
An alternative to the centralized warehouse is the open data lakehouse. This architecture combines the management features of a data warehouse with the low-cost, flexible storage of a data lake. By using open table formats like Apache Iceberg on Amazon S3, you can maintain a single copy of your data that is accessible by multiple different engines, including Starburst, Spark, and even Redshift itself.
A data lakehouse functions by adding a metadata layer over your files in Amazon S3, providing features like ACID transactions, schema evolution, and time travel. This approach avoids the need to force every dataset into a proprietary warehouse. Instead, you can keep large-scale, raw, or semi-structured data in your data lakehouse and only move highly refined subsets into Redshift if specific BI performance requirements demand it.
This shift toward openness is particularly relevant for modern AI and machine learning workloads. All of this underscores the importance of data access for modern analytics and AI workflows. An open lakehouse ensures that the data is ready for both SQL-based analytics and Python-based data science workflows simultaneously.
Leveraging federation to bridge data silos
In many organizations, data does not just live in AWS, and if some data does exist there, it is not all of the data in every department. Instead, it is often spread across multiple cloud platforms, on-premises databases, and various SaaS applications. Centralizing all of this into Redshift requires building and maintaining complex pipelines for every single source. This is where data federation becomes a strategic advantage.
Starburst, building on the success of Trino, allows you to query data without copying or moving it. Whether the data is in a PostgreSQL database on-premises, a Snowflake instance in another region, or a massive collection of files in Amazon S3, Starburst provides a single point of access. This federated approach means you can join data across these disparate sources using standard SQL.
For example, Rippling used this to provide a unified view of their business without the delay of moving everything into a central repository. By using Starburst Galaxy or Starburst Enterprise, you can empower your analysts to explore data immediately, rather than waiting weeks for a new pipeline to be built.
Performance and cost considerations
When comparing Redshift to an open lakehouse powered by Starburst, performance and cost are usually the deciding factors. Redshift is highly optimized for its internal storage format, but Starburst has made significant strides in closing the performance gap on open formats. Starburst claims that its engine is approximately 54% faster than Redshift when querying Apache Iceberg tables. This figure is vendor-stated and not independently verified.
From a cost perspective, the differences can be even more pronounced. Centralizing data in a warehouse involves not just the cost of the compute to run queries, but also the storage costs and the engineering hours required to manage the ingestion pipelines. Starburst states that its platform can be roughly 65% cheaper than Redshift for certain AWS workloads, though this is also a vendor-stated claim that has not been independently verified.
One way Starburst achieves this performance is through advanced query optimization techniques. For instance, predicate pushdown is a method where the query engine pushes the filtering logic down to the underlying data source. This reduces the amount of data that needs to be transferred over the network, significantly speeding up queries on federated sources.
Operational complexity and the vacuuming problem
One of the most cited frustrations with Amazon Redshift is the need for manual maintenance. While AWS has introduced automated vacuuming and analyzing, these processes still consume cluster resources. In a high-concurrency environment, these background tasks can compete with user queries for CPU and I/O, leading to performance degradation.
In contrast, an open lakehouse architecture built on Amazon S3 and Apache Iceberg handles metadata and data organization differently. While Iceberg tables do require occasional maintenance like manifest compaction and snapshot expiration, these tasks are typically decoupled from the query engine. This means you can maintain your data lakehouse without impacting the performance of your active analytical queries.
The distribution of data in Redshift also requires careful planning. Choosing the wrong distribution style (KEY, EVEN, or ALL) can lead to massive data shuffles during joins, which kills performance. In a Starburst-led architecture, the engine is designed to handle data where it lives, using sophisticated cost-based optimizers to determine the most efficient way to execute a join across different systems or storage formats.
Choosing the right path for your platform
The decision to centralize in Redshift or adopt a more federated, lakehouse-centric approach depends on your specific use cases. If your primary goal is to support a stable set of BI dashboards with very high performance requirements on structured data, Redshift is a logical choice. Its managed nature and AWS ecosystem integration provide a clear path for many teams.
However, if you are dealing with rapidly growing data volumes, a high variety of data sources, or a need for more flexible, open storage, the lakehouse model is often superior. When choosing the best data lakehouse platform for AI, you should look for tools that provide both the performance of a warehouse and the flexibility of a federated engine.
The Icehouse architecture from Starburst is designed specifically for this purpose. It combines the Trino engine with built-in support for Apache Iceberg, providing a warehouse-like experience directly on your Amazon S3 data lake. This allows you to scale your storage and compute independently while maintaining the open standards that prevent long-term vendor lock-in.
A strategy of coexistence
For most large enterprises, the answer is not to choose one technology over the other, but to implement a strategy of coexistence. You can continue to use Redshift for the high-value, highly structured workloads where it excels, while using Starburst to provide a unified query layer across the rest of your data estate.
This hybrid approach allows you to keep your most demanding BI workloads in Redshift for maximum performance while using Starburst to query large-scale historical data in Amazon S3 without moving it. It also provides direct access to raw data for AI and machine learning. This reduces the overall maintenance burden on your data engineering team by minimizing the number of required ingestion pipelines.
By moving away from the idea that every piece of data needs to be centralized in a single warehouse, you create a more resilient and adaptable data platform. You can respond more quickly to new business requirements and take advantage of new technologies as they emerge, all while keeping your costs under control and your data accessible in open formats. The focus shifts from managing the infrastructure of a central warehouse to delivering the insights that the business needs to grow.
Ready to modernize your data federation strategy? Download The Data Engineer’s Guide to Reducing Your Cloud Data Warehouse Spending to discover how combining open table formats with Starburst delivers warehouse-grade query performance at a fraction of the cost.
