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What is Upsolver?

Upsolver simplifies the creation of a governed data lake while facilitating the management, integration, and preparation of streaming data for analytical purposes. Users can effortlessly build pipelines using SQL with auto-generated schemas on read. The platform includes a visual integrated development environment (IDE) that streamlines the pipeline construction process. It also allows for Upserts in data lake tables, enabling the combination of streaming and large-scale batch data. With automated schema evolution and the ability to reprocess previous states, users experience enhanced flexibility. Furthermore, the orchestration of pipelines is automated, eliminating the need for complex Directed Acyclic Graphs (DAGs). The solution offers fully-managed execution at scale, ensuring a strong consistency guarantee over object storage. There is minimal maintenance overhead, allowing for analytics-ready information to be readily available. Essential hygiene for data lake tables is maintained, with features such as columnar formats, partitioning, compaction, and vacuuming included. The platform supports a low cost with the capability to handle 100,000 events per second, translating to billions of events daily. Additionally, it continuously performs lock-free compaction to solve the "small file" issue. Parquet-based tables enhance the performance of quick queries, making the entire data processing experience efficient and effective. This robust functionality positions Upsolver as a leading choice for organizations looking to optimize their data management strategies.

What is IBM StreamSets?

IBM® StreamSets empowers users to design and manage intelligent streaming data pipelines through a user-friendly graphical interface, making it easier to integrate data seamlessly in both hybrid and multicloud settings. Renowned global organizations leverage IBM StreamSets to manage millions of data pipelines, facilitating modern analytics and the development of smart applications. This platform significantly reduces data staleness while providing real-time information at scale, efficiently processing millions of records across thousands of pipelines within seconds. The drag-and-drop processors are designed to automatically identify and adapt to data drift, ensuring that your data pipelines remain resilient to unexpected changes. Users can create streaming pipelines to ingest structured, semi-structured, or unstructured data, efficiently delivering it to various destinations while maintaining high performance and reliability. Additionally, the system's flexibility allows for rapid adjustments to evolving data needs, making it an invaluable tool for data management in today's dynamic environments.

Media

Media

Integrations Supported

Amazon Redshift
Amazon S3
Amazon Simple Queue Service (SQS)
Apache Cassandra
Azure Data Lake Storage
Azure Industrial IoT
Couchbase
CrateDB
Eco
FileTrail Records Management
HPE Ezmeral Data Fabric
Hadoop
Hive
MongoDB
MySQL
PuppyGraph
Redis
Salesforce
Snowflake
Splunk Cloud Platform

Integrations Supported

Amazon Redshift
Amazon S3
Amazon Simple Queue Service (SQS)
Apache Cassandra
Azure Data Lake Storage
Azure Industrial IoT
Couchbase
CrateDB
Eco
FileTrail Records Management
HPE Ezmeral Data Fabric
Hadoop
Hive
MongoDB
MySQL
PuppyGraph
Redis
Salesforce
Snowflake
Splunk Cloud Platform

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

$1000 per month
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Upsolver

Date Founded

2014

Company Location

Israel

Company Website

www.upsolver.com

Company Facts

Organization Name

IBM

Date Founded

1911

Company Location

United States

Company Website

www.ibm.com/products/streamsets

Categories and Features

Big Data

Collaboration
Data Blends
Data Cleansing
Data Mining
Data Visualization
Data Warehousing
High Volume Processing
No-Code Sandbox
Predictive Analytics
Templates

Data Mining

Data Extraction
Data Visualization
Fraud Detection
Linked Data Management
Machine Learning
Predictive Modeling
Semantic Search
Statistical Analysis
Text Mining

Data Preparation

Collaboration Tools
Data Access
Data Blending
Data Cleansing
Data Governance
Data Mashup
Data Modeling
Data Transformation
Machine Learning
Visual User Interface

Categories and Features

DevOps

Approval Workflow
Dashboard
KPIs
Policy Management
Portfolio Management
Prioritization
Release Management
Timeline Management
Troubleshooting Reports

Streaming Analytics

Data Enrichment
Data Wrangling / Data Prep
Multiple Data Source Support
Process Automation
Real-time Analysis / Reporting
Visualization Dashboards

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