Oracle Autonomous AI Lakehouse Alternatives

Our overall experience with Dremio has been excellent. It has significantly simplified our data architecture by removing extremely process heavy ETL. The platform overall is relaible, the UI is intuitive and it scales as data demand grows. Being early adopters of the Data Lakehouse also proved to be a signifcant step in a stable, scalable solution. Dremio bridges the gap between our raw data and business insights in a fast yet reliable manner.

As a Business Analyst in a mid-market organization, we were dealing with scattered data tools - separate pipelines, reporting layers, and storage solutions that didn't talk to each other well. We moved to Microsoft Fabric roughly eight months ago hoping to bring some coherence to that landscape, and for the most part, it has delivered on that promise. Having data integration, warehousing, and reporting accessible within one environment has reduced the back-and-forth between teams and shortened the time it takes to go from raw data to an insight I can actually act on.

I had a good overall experience using Amazon SageMaker. The platform provides a strong set of capabilities for building, training and deploying machine learning models in a scalable cloud environment. I especially found the integration with other AWS services, the managed infrastructure and the flexibility across different stages of ML lifecycle to be valuable.

By integrating data from transactional data silos and modernizing our architecture, we have moved beyond our simple IT upgrade to build a robust data platform with streaming data ingesting for fraud detection, great data management and data cataloging for quick response using Private AI, with Ai and data workload readiness for our data science team, GIS with great Query engine to merge combinational data, resolve Growth & Scalable Challenges, issues and huge reduction in infrastructure management time, TCO reduction, Cost optimization. Supports Private cloud infrastructure and data stored on-prem due to sensitivity, and lastly no platform sprawls.

We've been using Google Cloud Lakehouse as our central platform for data analytics and reporting. It has helped us bring data from multiple sources into a single environment and has reduced the effort of data ingestion, reducing the overall effort required to manage separate data warehouses and data lakes. Query performance has improved through an optimised Query engine, making the life of Data Science teams easier by streamlining data management.

The biggest feature that directly impacted my evaluation of Starburst was how it revolutionized data ingestion. Starburst fulfilled a need to ingest and provide query engine capabilities against stream security event data stored in our data lake. Schema discovery provided just that!

It's a good architecture fit governanced data platform solution.

It's a amazing data lake product, it support various data sources such as oss, mysql, sqlserver, oracle, postgresql, MongoDB, Redis, ElasticeSearch, ADB for MySQL, ADB for PosrgreSQL, PolarDB... And it offers standard SQL interface, we can use it conveniently and compatibility with Business Intelligence (BI) tools, easy to turn big data into consumable data insights and visualizations. At the last, due to DLA basis serverless architecture, it's provider a very very higher cost performance.