Sold by: Upsolver
Deployed on AWS
Upsolver SQLake is a cloud-native platform for continuous ingestion, transformation and optimization of streaming and batch data delivered to your Iceberg lakehouse, data warehouse and other popular analytics systems.
Overview
Upsolver SQLake is a cloud-native platform for continuous ingestion, transformation and optimization of streaming and batch data delivered to your Iceberg lakehouse, data warehouse and other popular analytics systems.
With only SQL, users develop, test and deploy pipelines in minutes instead of weeks. SQLake automates time-consuming tasks such as job orchestration and scheduling, file system optimization, data retention and the scaling of compute infrastructure.
Key capabilities:
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CONNECT: With only a few clicks or lines of SQL, connect and replicate data from popular streaming, file and database sources like PostgreSQL, SQLServer and Kinesis to Iceberg or warehouse tables.
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ICEBERG LIVE TABLES: Simply define the transformations to perform on the data and Iceberg Live Tables manages task orchestration, scaling, data quality validation, schema evolution and file system optimization.
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ADAPTIVE ICEBERG OPTIMIZER: Connect Upsolver to your existing Iceberg lakehouse and see query performance increase while storage costs decrease, without lifting a finger. Adaptive Optimizer profiles data files and write patterns to determine which files, how frequently and what technique to use to optimize for best possible results.
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DATA OBSERVABILITY: Automation often creates a blind spot, but not with Upsolver. Built-in task monitoring and data observability allows engineers to alert on and pinpoint operational and data related issues like volume changes, data value drift and schema evolution.
Highlights
- Write a query, get a pipeline - Replace complex, code-intensive data engineering with SQL. Using SQL any user can easily create, transform and publish new datasets in minutes.
- Automated - SQLake automates orchestration, file management - compaction, partitioning, data retention, cleanup etc. - and elastic scaling.
- Predictable pricing - pay only for data volume ingested, transformations are free!
Details
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Features and programs
Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing
Pricing is based on the duration and terms of your contract with the vendor, and additional usage. You pay upfront or in installments according to your contract terms with the vendor. This entitles you to a specified quantity of use for the contract duration. Usage-based pricing is in effect for overages or additional usage not covered in the contract. These charges are applied on top of the contract price. If you choose not to renew or replace your contract before the contract end date, access to your entitlements will expire.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/12 months | Overage cost |
|---|---|---|---|
GB ingested | Gigabyte of data ingested, for use in an unlimited number of data pipe | $0.219 | |
Data Under Management | 1 GB of data under management by the Table Optimizer | $0.011 |
Vendor refund policy
Upsolver does not offer refunds. Upsolver reserves the right to do so at their discretion.
Legal
Vendor terms and conditions
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Content disclaimer
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Delivery details
Software as a Service (SaaS)
SaaS delivers cloud-based software applications directly to customers over the internet. You can access these applications through a subscription model. You will pay recurring monthly usage fees through your AWS bill, while AWS handles deployment and infrastructure management, ensuring scalability, reliability, and seamless integration with other AWS services.
Resources
Vendor resources
Support
Vendor support
Standard and Enterprise support available in app https://app.upsolver.com or via email support@upsolver.com
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
Updated weekly
By Upsolver
By Dremio
Top
10
In Streaming solutions, ELT/ETL
Top
10
In Streaming solutions
Top
10
In Data Warehouses
Sentiment is AI generated from actual customer reviews on AWS and G2
Functionality
Ease of use
Customer service
Cost effectiveness
Positive reviews
Mixed reviews
Negative reviews
AI generated from product descriptions
Data Ingestion
Supports continuous ingestion of streaming and batch data from multiple sources including PostgreSQL, SQLServer, and Kinesis
Data Transformation
Enables data transformations using SQL with automated task orchestration, scaling, and data quality validation
Table Management
Provides Iceberg Live Tables with native schema evolution and file system optimization capabilities
Performance Optimization
Implements adaptive Iceberg data file optimizer that profiles data files and write patterns to improve query performance and reduce storage costs
Operational Monitoring
Offers built-in task monitoring and data observability features to track volume changes, data value drift, and schema evolution
Stream Processing Framework
Native integration of Apache Flink for real-time data filtering, joining, and enrichment without operational overhead
Data Streaming Architecture
Cloud-native platform built on Apache Kafka, Apache Flink, and Apache Iceberg for event streaming and data processing
Connector Ecosystem
Supports 120+ pre-built connectors for seamless data integration with AWS services like S3, Redshift, DynamoDB, and Lambda
High Availability Infrastructure
Enterprise-grade service with 99.99% uptime SLA and autoscaling capabilities for resilient data streaming
Real-Time Analytics Transformation
Capability to convert Kafka topics to Apache Iceberg tables for downstream analytics across multiple AWS data services
Data Lake Query Performance
Provides sub-second query response times using SQL query service on data lake platforms
Open Standards Support
Utilizes community-driven standards like Apache Iceberg and Apache Arrow for processing engines
Multi-Source Data Integration
Enables joining data from data lakes and external databases without data movement
Compute Engine Management
Automatically handles compute engine lifecycle including provisioning, scaling, pausing, and decommissioning
VPC-Based Data Processing
Deploys compute engines within customer's Amazon Virtual Private Cloud for secure data processing
Validated by AWS Marketplace
FedRAMP
GDPR
HIPAA
ISO/IEC 27001
PCI DSS
SOC 2 Type 2
No security profile
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Standard contract
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Customer reviews
0 ratings
0 AWS reviews
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15 external reviews
Star ratings include only reviews from verified AWS customers. External reviews can also include a star rating, but star ratings from external reviews are not averaged in with the AWS customer star ratings.
Snehasish Das
Allows for data to be moved across platforms and different data technologies
Reviewed on Dec 23, 2024
Review provided by PeerSpot
What is our primary use case?
I am working as a consultant and currently have my own consultancy services. I provide services to companies that are data-heavy and looking for data engineering solutions for their business needs.
We primarily serve financial service customers in India and around the globe. We use Upsolver as an ETL tool to move data from different sources into one destination quickly and at scale.
What is most valuable?
The most prominent feature of Upsolver is its function as an ETL tool, allowing data to be moved across platforms and different data technologies. Upsolver does this in a quick time, unlike traditional processes which are time-consuming.
Additionally, it offers scalability for large volumes of data, with performance and ease of cloud-native integration.
What needs improvement?
There is room for improvement in query tuning. Upsolver could do a more in-depth analysis in employing machine power, such as CPU and memory, to enhance query performance.
Furthermore, allocating CPU and memory resources for complex queries could improve efficiency.
For how long have I used the solution?
I started using Upsolver from the end of 2023 or the beginning of 2024.
What do I think about the stability of the solution?
On the stability side, I would rate it seven out of ten. Using multiple cloud providers and data engineering technologies creates complexity, and managing different plugins is not always easy, but they are working on it.
What do I think about the scalability of the solution?
Upsolver's scalability is rated around eight out of ten. It performs well with large volumes of data and is designed for cloud-native applications.
How are customer service and support?
Customer service is excellent, and I would rate it between eight point five to nine out of ten. They offer quick support in the Asia region and have multiple support centers globally. They maintain regular communication with customers through webinars and are very connected.
How would you rate customer service and support?
Positive
Which solution did I use previously and why did I switch?
In Fidelity, I used different data products like Snowflake. We used other ETL tools to move data across platforms. I did not use Upsolver there since it was not mature enough at that time.
How was the initial setup?
The initial setup is relatively easy, rated nine out of ten. It is cloud-based, eliminating the need for installation on a local system.
What's my experience with pricing, setup cost, and licensing?
Upsolver is affordable at approximately $225 per terabyte per year. Compared to what I know from others, it's cheaper than many other products.
What other advice do I have?
I rate Upsolver nine out of ten overall.
It works well for medium to enterprise-level customers. If a company is managing a heterogeneous data environment, Upsolver could be beneficial, but smaller companies with simple needs might not receive as much value.
Which deployment model are you using for this solution?
Public Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Other
Kireet Kokala
Provides ETL tools with stability at a competitive price
Reviewed on Aug 13, 2024
Review provided by PeerSpot
What is our primary use case?
When I test-drove Upsolver for a consulting company, I used it in POC to stream and ingest data. The goal was to move data from a source, possibly SQL Server, into a destination like Snowflake or Redshift. The POC aimed to evaluate Upsolver against StreamSets, the competition for ETL tasks. The use case involved data aggregation, ingestion rules, landing data into a data lake, and handling ETL processes for a data warehouse.
How has it helped my organization?
It has simplified prices, time to set up, and ease of use.
What is most valuable?
It was easy to use and set up, with a nearly no-code interface that relied mostly on drag-and-drop functionality. Some customization was available, and I attended developer training that allowed me to explore it in more depth. At the time of my evaluation, features like iceberg tables were not present on the platform but are now included based on your current website.
It was easier to use than the competition. I liked the real-time ingestion and transformation features. The wizard-based guidance was particularly notable.
What needs improvement?
Upsolver excels in ETL and data aggregation, while ThoughtSpot is strong in natural language processing for querying datasets. Combining these tools can be very effective: Upsolver handles aggregation and ETL, and ThoughtSpot allows for natural language queries. There’s potential for highlighting these integrations in the future.
For how long have I used the solution?
I have been using Upsolver since 2020.
What do I think about the stability of the solution?
There were no stability issues.
What do I think about the scalability of the solution?
It is very scalable. We didn't foresee any issues.
How was the initial setup?
Some prior knowledge is helpful. A novice user might still encounter confusion, especially if they skip steps during tenant setup and run into permissions errors.
What's my experience with pricing, setup cost, and licensing?
It was competitively priced and within the customer's budget.
What other advice do I have?
I recommend test-driving it for ease of use and peace of mind. If you’re exploring ETL tools and want to guide your curiosity and market survey, it's a good idea to leap for a test drive.
Overall, I rate the solution an eight out of ten.
Computer Software
Great Tool to Setup Data Ingestion pipeline
Reviewed on May 21, 2023
Review provided by G2
What do you like best about the product?
Upsolver enables its users to build Data Ingestion pipelines without setting up additional infrastructure. In the latest version, one can build he entire pipeline and schedule it with an SQL Like framework.
What do you dislike about the product?
Upsolver can look to integrate more Data stores for ingestion as well as data dumps.
What problems is the product solving and how is that benefiting you?
Upsolver is a Big Data Ingestion tool that provides a one-stop solution for building and orchestrating data pipelines. It manages all the infrastructure requirements under the hood.
Nir E.
An ETL tool which facilitates data ingestion
Reviewed on Feb 28, 2022
Review provided by G2
What do you like best about the product?
* Ease of use
* UI simplicity
* Support team availability & professionalism
* UI simplicity
* Support team availability & professionalism
What do you dislike about the product?
* Sometimes, it takes time for the UI to get updated
* Couldn't find a simple login page;)
* Couldn't find a simple login page;)
What problems is the product solving and how is that benefiting you?
Preparing semi-structured log files, to be later queried by BI analytics tools
Ido A.
As a startup - upsolver offers a great platform to transfer , manage and transform data at ease!
Reviewed on Feb 24, 2022
Review provided by G2
What do you like best about the product?
With the platform's simplicity, it is effortless to set up a source connector, transform the data using a simple SQL editor and send it wherever I want.
The best feature, In my opinion, is the fact that I can duplicate a "Flow" and send it to another destination.
The best feature, In my opinion, is the fact that I can duplicate a "Flow" and send it to another destination.
What do you dislike about the product?
The UI is a little bit unpleasant to the human eye, but it is a small thing compared to the system's functionality and simplicity
What problems is the product solving and how is that benefiting you?
Transferring raw data from streams, modeling the data into an aggregated data model (enriching it with Lookup Tables), and sending it to multiple resources using.