Data Lake Analytics Alternatives

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.

From a principal cloud infrastructure analyst’s perspective, my overall experience with Starburst Enterprise has been strong on platform capabilities and acceptable on operational maturity, with most pain points concentrated at the integration/process edges rather than the core query engine behavior. Support Quality: I would characterize support quality as good but somewhat dependent on how prepared we are internally with reproducible evidence. The fastest progress happened when we could provide exact config deltas, command outputs, and clear before/after states. Integration Success: Integration with AWS services is one of the strongest aspects, but it requires disciplined setup. We successfully implemented Glue metastore integration, role-based access patterns, and BIAC role/domain/product provisioning. We also hardened automation by replacing a TLS-fragile Powershell path with a curl-based API call, which improved execution reliability in our environment. Business Impact: The immediate impact has been greater confidence in data management, data access and cleaner operational repeatability. We have completed end-to-end provisioning for owner/consumer role models and aligned writeback paths for Iceberg workflows through Terraform-backed changes. This kind of deterministic runtime verification reduced ambiguity during rollout and improved confidence in production-readiness.

Our overall experience with Dremio Cloud has been very positive. The platform stands out for its fast time to value, intuitive user experience and strong performance on large datasets. Being able to quickly make data available for analytics and combine it with AI has improved how our team works with data. In addition, Dremio's commitment to open standards such as Apache Iceberg provides long-term flexibility and reduces vendor lock-in. That said, the partner ecosystem is still evolving and we would welcome a broader set of native connectors and integrations with additional third-party tools.

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.

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.

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

Basically, you get all the features of Oracle Database AI running on Exadata on premises, but without the extra complexity of installing and managing OS, Cluster or Oracle software. It's very simple to provision, you can start using it in a few minutes.