What do you like best about the product?

Starburst provides a single point of access to multiple disparate data sources including data lakes, cloud storage, relational databases, and NoSQL systems. This significantly simplifies the analytics process by allowing users to query data in-place, eliminating the need for complex ETL pipelines.
The Security and Governance features are great which helps in auditing etc.

1. Unified Access to Distributed Data
Starburst provides seamless connectivity to a wide range of data sources—relational databases, data lakes, cloud storage, and more—allowing analysts and data scientists to query across silos using standard SQL.

2. High Performance & Scalability
Built on Trino’s distributed SQL engine, Starburst offers fast query performance at scale. Features like cost-based optimization, dynamic filtering, and query caching significantly enhance performance for large datasets.

3. Enterprise-Ready Security & Governance
Starburst integrates with authentication systems and supports fine-grained access control via tools like Apache Ranger, making it a secure option for highly regulated environments.

4. Flexible Deployment
Supports hybrid, multi-cloud, and on-prem deployments. With Starburst Galaxy, the fully managed SaaS offering, users can get started quickly without infrastructure overhead.

5. Broad Ecosystem Support
It integrates with major cloud platforms (AWS, Azure, GCP) and connects with popular BI and data tools such as Tableau, Power BI, and dbt.

What do you dislike about the product?

While Starburst is a strong platform, there are a few areas that could be enhanced:

1. Catalog Creation and Management
Setting up catalogs, especially at scale, can be complex and sometimes unintuitive. Improvements to the user experience, automation, and management of catalogs—particularly in large, dynamic environments—would greatly benefit data engineering teams.

2. Learning Curve for New Users
Though SQL-based, the platform requires understanding of distributed query execution, connector configurations, and performance tuning. Organizations may need to invest in training or initial consulting support.

3. Monitoring and Troubleshooting
While Starburst provides basic query monitoring tools, more advanced observability features (e.g., deeper lineage tracking, proactive performance insights) could further simplify troubleshooting and operational efficiency.

4. Cost Management in Cloud Environments
Given the platform’s power, it’s easy to incur high compute costs when querying large datasets across multiple cloud sources. Resource management policies need to be carefully implemented.

What problems is the product solving and how is that benefiting you?

Data Silos
Starburst allows querying data in place, eliminating the need for traditional ETL pipelines that move data into a centralized repository. This reduces latency and complexity.

Performance Bottlenecks in Distributed Queries
Leveraging Trino’s high-performance distributed SQL engine, Starburst enables interactive and scalable analytics over large and diverse datasets.

Lack of Unified Access Across Cloud and On-Prem
With Starburst, we can connect to multiple data sources (across AWS, Azure, GCP, on-prem DBs) through a single SQL interface, simplifying analytics and reducing tool sprawl.

Data Governance and Security Challenges
Starburst supports fine-grained access control, auditability, and role-based permissions—essential for compliance and enterprise data governance.

Tool Fragmentation and Analyst Productivity
Analysts and data scientists can use standard BI tools like Tableau, Power BI, or even Jupyter with Starburst to access all relevant data without switching contexts or learning new interfaces.