Find out in this report how the two Data Science Platforms solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.
| Product | Market Share (%) |
|---|---|
| Dataiku | 10.5% |
| Starburst Galaxy | 0.8% |
| Other | 88.7% |
| Company Size | Count |
|---|---|
| Small Business | 4 |
| Midsize Enterprise | 1 |
| Large Enterprise | 8 |
| Company Size | Count |
|---|---|
| Small Business | 4 |
| Midsize Enterprise | 2 |
| Large Enterprise | 1 |
Dataiku Data Science Studio is acclaimed for its versatile capabilities in advanced analytics, data preparation, machine learning, and visualization. It streamlines complex data tasks with an intuitive visual interface, supports multiple languages like Python, R, SQL, and scales efficiently for large dataset handling, boosting organizational efficiency and collaboration.
Starburst Galaxy offers rapid query speeds and robust cluster management, enhancing data engineering efficiency while supporting AWS integrations and cross-database functionality. Users benefit from its advanced data integration and federated querying capabilities.
Starburst Galaxy stands out with a compute-focused architecture that excels in facilitating seamless data integration. Technological innovations like autoscaling clusters and automated metadata management optimize operations in multi-tenant environments. With a keen emphasis on compatibility, the platform provides support for AWS Glue and enables federated querying across S3, Snowflake, and Redshift. This adaptability ensures comprehensive ETL processes and enhances analytics through querying SQL Server, Google Sheets, and blob stores. While noted for its robust capabilities, users seek improvements in cluster startup times, Tableau and AI support, and desire infrastructure-as-code enhancements.
What are Starburst Galaxy's key features?In industries focusing on large-scale data efforts, Starburst Galaxy plays an essential role in connecting data sources like Amazon S3 and RDS, streamlining tasks in data engineering and ad-hoc analysis across complex environments. Teams leverage its cross-database querying to boost AWS analytics, with features tailored for sectors needing agile data solutions, from ETL pipelines to secure data federation.
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