Sold by: Sparkflows.ioÂ
Deployed on AWS
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Sparkflows empowers enterprises with a unified, AI-driven platform for end-to-end Machine Learning project lifecycles, featuring enhanced self-service analytics, robust data pipeline management with extensive integrations (Databricks, EMR, Snowflake), and advanced AI/automation capabilities including sophisticated chatbot interactions.
Overview
Sparkflows delivers a comprehensive, self-serve Enterprise platform meticulously designed for the modern AI/ML landscape. It empowers data scientists, analysts, and engineers to manage the entire lifecycle of analytics and machine learning projects with unparalleled ease and efficiency. Leveraging a powerful visual workflow editor equipped with over 350 processors, users can rapidly build, test, and deploy sophisticated data pipelines and ML models, minimizing coding efforts and accelerating time-to-value.
The platform seamlessly integrates with diverse enterprise environments, whether on-premise, cloud (including AWS EMR, Azure, GCP), or standalone machines. Recent releases (up to v3.3.1) significantly enhance this robust foundation with cutting-edge features. Experience advanced AI and automation through enhanced chatbots capable of natural language querying on structured databases (PostgreSQL, MySQL) and streamlined chatbot import/export. Security is fortified with SSO via PingID and Snowflake Key-Pair Authentication.
Workflow orchestration is more powerful than ever, featuring extensive Databricks integration (cluster management, job/notebook/workflow execution via Airflow operators), enhanced EMR pipeline creation with dynamic sizing and bootstrap arguments, and new nodes for SFTP and Email notifications. Data management capabilities are expanded with inline Data Quality checks, support for new storage connections like Confluence, ServiceNow, and SharePoint, Pinecone Vector DB integration for advanced search, and flexible variable management (global, group, project levels). Performance and usability are boosted by upgrades to Java 17 and Spring Boot 3, server-side pagination, improved UI layouts, enhanced code editor features (auto-completion), and a centralized Code Library for SQL/Scala components. Sparkflows ensures data governance by enabling push-down analytics, processing data where it resides, and offers robust monitoring with enhanced execution visibility and resource utilization alerts.
Highlights
- Accelerate your AI/ML projects with an AI-powered, self-service analytics platform featuring enhanced Databricks, EMR, and Snowflake integration, plus new AI automation like natural language database querying.
- Streamline end-to-end data science workflows using a visual editor with 350+ processors, now upgraded with inline Data Quality checks, extensive pipeline operators (including SFTP/Email), and a centralized Code Library.
- Boost productivity with improved UI/UX (Java 17/Spring Boot 3 upgrades, new layouts), robust security features (SSO, Key-Pair Auth), enhanced variable management, and seamless integration with tools like Confluence, ServiceNow, and SharePoint.
Details
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Delivery method
Delivery option
64-bit (x86) Amazon Machine Image (AMI)
Latest version
Operating system
AmazonLinux 4.x
Deployed on AWS
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Financing for AWS Marketplace purchases
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Pricing
Free trial
Try this product free for 21 days according to the free trial terms set by the vendor. Usage-based pricing is in effect for usage beyond the free trial terms. Your free trial gets automatically converted to a paid subscription when the trial ends, but may be canceled any time before that.
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time. Alternatively, you can pay upfront for a contract, which typically covers your anticipated usage for the contract duration. Any usage beyond contract will incur additional usage-based costs.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Cost/hour |
|---|---|
r5.2xlarge Recommended | $0.45 |
m4.large | $0.35 |
m4.4xlarge | $0.65 |
r5.24xlarge | $0.55 |
r5.8xlarge | $0.55 |
r5.16xlarge | $0.55 |
r5.metal | $0.55 |
m3.2xlarge | $0.35 |
r5.4xlarge | $0.45 |
r5.12xlarge | $0.55 |
Vendor refund policy
Please contact us at support@sparkflows.io if there is need for refund.
Legal
Vendor terms and conditions
Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA)Â .
Content disclaimer
Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
Delivery details
64-bit (x86) Amazon Machine Image (AMI)
Amazon Machine Image (AMI)
An AMI is a virtual image that provides the information required to launch an instance. Amazon EC2 (Elastic Compute Cloud) instances are virtual servers on which you can run your applications and workloads, offering varying combinations of CPU, memory, storage, and networking resources. You can launch as many instances from as many different AMIs as you need.
Version release notes
The latest Sparkflows releases (up to version 3.3.1) introduce a wealth of powerful features and enhancements designed to streamline data workflows, bolster security, and integrate cutting-edge AI capabilities. Significant advancements include enhanced AI and automation features like chatbot export/import functionality and natural language querying on structured databases (PostgreSQL, MySQL) for intuitive data analysis. Security is strengthened with SSO support via PingID using persistent NameID formats and Snowflake Key-Pair Authentication. Workflow management sees improvements with options to disable SSL validation for MWAA, dynamic EMR cluster sizing, bootstrap script arguments, and new pipeline nodes for SFTP and Email Notifications. Data handling is more robust with inline Data Quality checks, support for new storage connections (Confluence, ServiceNow, SharePoint), Pinecone Vector DB integration, and enhanced variable management across global, group, and project levels, including import/export capabilities and support for curly braces. The platform now supports Java 17 and Spring Boot 3 for improved performance, alongside UI/UX upgrades like enhanced node/project list layouts, dashboard card views, and better execution monitoring visibility. Databricks integration is significantly expanded with new pipeline operators for cluster management, job execution, and notebook/workflow runs, plus support for Azure Service Principals and ADLS in PySpark. Additional features include a centralized Code Library (SQL/Scala), enhanced log search, project sharing during creation, alerts for resource utilization, and automated /tmp directory cleanup.
Additional details
Usage instructions
Sparkflows is running on port "8080 for http & 8443 for https" when instance is launched. Access it in your browser by going to:
- http://INSTANCE_PUBLIC_ADDRESS:8080
- https://INSTANCE_PUBLIC_ADDRESS:8443
- For HTTPS URL to work, Port HTTPS(443) & 8443 Should be open Login with below to get started:
- Username : admin
- Password : instance id of your machine Administrative (command-line) access can be obtained through ssh ec2-user@INSTANCE_PUBLIC_ADDRESS. Sparkflows runs under linux user account "ec2-user". For additional information, or any issue, please see our docs at https://docs.sparkflows.io/en/latest/user-guide/index.html For Compute Connection Integration, please see our docs at https://docs.sparkflows.io/en/latest/user-guide/connection/compute-connection/index.htmlÂ
Support
Vendor support
The Customer Support team is available to assist customers on the installation, getting started or troubleshooting assistance that they might require. Please reach out to support@sparkflows.io for any assistance or clarifications.
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.
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