An enthral tool for providing analytics ready data pipelines.
Overall Satisfaction with Upsolver
Define pipelines using only SQL on the auto-generated schema on reading. Add upserts and deletes to data lake tables. Blend streaming and large-scale batch data. Automate schema evolution and reprocessing from the previous state. Allows for automatic orchestration of pipelines. Capacity to fully manage execution at scale. Strong consistency is guaranteed over object storage.
Pros
- Data lake table management.
- High performance at scale on complex data.
- Capacity to parquet based data for fast queries.
Cons
- Enables low latency dimension tables using streaming upsets.
- Continuous lock free compaction.
- Automatic schema on read and data profiling.
- Lower cloud compute and data engineering cost.
- Free for small workloads.
- Connects faster to a solution guru whenever a problem arise.
- Integrations and connectors.
- Effective stream processing engines.
- Ability to write to Amazon.
Great in streamlining workload. Continuously serve data to lakes, warehouses, databases, and streaming systems. Near-zero maintenance overhead for analytics ready data. Blend streaming and large-scale batch data. Low code, SQL-based data transformation. UI-driven ingestion connections with auto-generated schema on reading. Automated pipeline orchestration with built-in data lake best practices.
Do you think Upsolver delivers good value for the price?
Yes
Are you happy with Upsolver's feature set?
Yes
Did Upsolver live up to sales and marketing promises?
Yes
Did implementation of Upsolver go as expected?
Yes
Would you buy Upsolver again?
Yes
Comments
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