Upsolver has successfully eliminated the engineering complexity from data lake management, so that companies can quickly solve their modern data challenges in real time, at any scale, and without having to create big data engineering silos. Its Cloud-native data lake extract, transform, load (ETL) platform enables the performance of complex ETL with significant lower capital expenditure. The Upsolver solution can be implemented in on-premise and Cloud environments.
Solution
Upsolver functionalities include:
- Streaming – connects to Kinesis/Kafka streams and prepares data for Analytics
- Realtime analytics – perfect for ad-hoc real-time analytics. Look-up tables enable real-time responses to data
- Building Data lake – tremendously improve time-to-value of building a data lake and preparing data for Analytics
Benefits
- Implementation with no need to recruit data engineers.
- Basic SQL knowledge or analysts able to perform ETL
- No need for open-code exposure (Spark, Presto, Hive)
- Can replace AEROSPIKE and in-memory solutions currently used for ETL
- Provide enhanced solutions for lookup tables
- UI/SQL Replace ETL Coding & DevOps
- Presents additional lake Features
- Performing 100x faster lake queries
- No lock-in (open formats and metadata)
- Upsolver’s solutions can be deployed on Azure and AWS
Upsolver and CloudZone
Integrating Upsolver’s unique solution enables us at CloudZone to achieve a short time-to-market, delivering great value to those of our customers wishing to perform ETL on the Cloud.
See our case study about how CloudZone implemented Upsolver to help migrate Gamoshi from Google Cloud Platform to AWS, enabling the customer to achieve extended ETL capabilities.
User Profile
Upsolver is relevant for companies performing ETL, with a data lake.
