Sold by: WhaleOps
Deployed on AWS
Free Trial
WhaleScheduler is an easy-to-use, low-code data scheduling product. It provides a more powerful scheduling engine, dozens of scheduling job types, a simple IDE editing interface, rich scheduling and operation functions, scheduling lineage analysis
Overview
WhaleScheduler is a commercial version of high-performance distributed, easy-to-use, low-code data scheduling product created by the core team of Apache DolphinScheduler. It provides a more powerful scheduling engine, dozens of scheduling job types, simple IDE editing interface, rich scheduling and running functions, scheduling lineage analysis, operation and maintenance monitoring and enterprise-level permission control. It has been widely used in actual business in many industries, can meet complex scheduling scenarios such as finance, telecommunications, retail, and the Internet, and supports multiple fields such as databases, cloud, big data, AI, MLOps, etc., helping enterprises to quickly improve data development efficiency.
Highlights
- Visual, Drag& Drop Design: A drag and drop interface that enables both engineers and data analysts to build complex ETL pipelines or Task Orchestration.
- Rich Task Ecosystem and Connectors: Supports 30 plus task types(SQL, Shell, Python, java, etc.) and 290 plus data sources (databases, SaaS, data lakes, etc.) out of the box, allowing fast orchestration & integration.
- Deep Observability and Runtime Control: Enhanced monitoring, alerting, and manual intervention capabilities make it easy to ensure pipeline reliability and reduce operational risk.
Details
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Delivery method
Delivery option
64-bit (x86) Amazon Machine Image (AMI)
Latest version
Operating system
AmazonLinux 2023.09.20
Deployed on AWS
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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
Free trial
Try this product free for 15 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 |
|---|---|
c5.2xlarge Recommended | $5.79 |
c4.4xlarge | $1.74 |
c5.xlarge | $3.82 |
Dimensions summary
You bring your own license (BYOL) and pay by the hour for the compute instance running the platform. The three dimensions are AWS EC2 instance sizes: c4.4xlarge, c5.2xlarge, and c5.xlarge. Each is billed hourly, so your cost tracks how long the instance runs. Choose the instance based on the processing power your workflows need. Larger instances offer more CPU and memory capacity for higher concurrency. Because this is BYOL, the software license is handled separately from these hourly infrastructure charges.
Top-of-mind questions for buyers
Each dimension is an AWS EC2 instance size running the platform. They differ by CPU and memory capacity. The c4.4xlarge and c5.2xlarge offer more compute for heavier concurrency; the c5.xlarge is a smaller footprint. You pick the size matching your workflow processing needs.
Hourly software charges track running time. When the instance is fully stopped, no hourly software charge accrues. Note that stopped instances may still incur underlying AWS storage fees for attached volumes. To stop all charges tied to compute, you shut the instance down.
The hourly charge covers the compute instance running the software. You bring your own WhaleScheduler license separately, so the software license is not part of these hourly infrastructure charges. This suits teams that already hold or arrange a license outside AWS Marketplace.
whaleops.io
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Vendor refund policy
We do not currently support refunds, but you can cancel at any time.
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) .
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
Security Fix: Disabled HTTP/2 by default to mitigate CVE-2023-44487 vulnerability.
Additional details
Usage instructions
- Launch the product via CloudFormation
- Access the application via a browser at http://<public_dns>:12345/dolphinscheduler.
- Sign in using the following credentials:
- Default admin username is "user" and password is EC2 instance id.
- After you login success, you can change the password and create new user.
When using CloudFormation to delivery, WhaleScheduler will create below resources:
-
Logical ID(s): S3User
- Type: AWS::IAM::User
- UserName: ${AWS::StackName}-user-s3
- PolicyName: ${AWS::StackName}-s3-policy
- Action:
- 's3:List*'
- 's3:Get*'
- 's3:Put*'
- 's3:Delete*'
- 's3:Abort*'
- 's3:Restore*'
- 's3:Create*'
- 's3:Replicate*'
- 's3:Update*'
- 's3:Describe*'
- Resource: '*' Purpose: WhaleScheduler will use S3 as resource center to store the resource file which uploaded by users.
-
Logical ID(s): S3UserKeys
- Type: AWS::IAM::AccessKey
- UserName: !Sub ${AWS::StackName}-user-s3 Purpose: WhaleScheduler will use use this key to connect s3.
Support
Vendor support
Please allow 24 hours contact service@whaleops.com for support
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 BMC Software
By Dagster Labs
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AI generated from product descriptions
Workflow Orchestration Engine
Distributed scheduling engine supporting complex task orchestration and ETL pipeline execution across multiple job types and data sources
Task Type Support
Supports 30+ task types including SQL, Shell, Python, and Java for diverse workflow requirements
Data Source Connectivity
Integrates with 290+ data sources including databases, SaaS platforms, data lakes, and cloud services
Visual Workflow Design
Drag-and-drop interface for building and designing complex workflows without requiring extensive coding
Monitoring and Observability
Enhanced monitoring, alerting, and manual intervention capabilities with scheduling lineage analysis and operation and maintenance monitoring
Workflow Orchestration Across Hybrid Environments
Orchestrates application workflows across on-premises, private and public clouds, including mainframe to multi-cloud environments
Jobs-as-Code with REST APIs
Supports Jobs-as-Code approach using REST APIs and JSON, enabling workflows to be versionable, testable, maintainable and integrated into CI/CD pipelines
Unified Workflow Management Interface
Provides a single unified view to orchestrate all workflows including file transfers, applications, data sources and infrastructure with a rich library of plug-ins
Intelligent Predictive Analytics
Delivers in-depth workflow observability with intelligent predictive analytics and reports for monitoring production workflows
Mainframe Integration
Integrates with AWS Mainframe Modernization Service to preserve continuity of mission-critical business outcomes during modernization initiatives
Data Asset Management
Orchestration of data assets including tables, datasets, machine learning models, and reports through declarative function definitions
Automated Scheduling and Execution
Automatic execution of functions at appropriate times with capability to keep data assets current and up-to-date
Multi-Environment Support
Support across complete data development lifecycle including local development, unit tests, integration tests, staging environments, and production
Data Pipeline Orchestration
Platform designed for developing and maintaining data pipelines with focus on productivity and scalability
Dependency Management
Tracking and management of dependencies between functions and data assets to ensure correct execution order
Standard contract
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