Overview
Sparkflows is a Self-Serve Enterprise product for AI/ML.
Sparkflows provides a workflow editor with 350+ processors for performing the tasks. You can run it easily on a standalone Linux machine or on an EMR cluster.
Few Salient Features:
- Perform AI/ML including Regression, Classification, Clustering in minutes.
- Leverage 350+ processors to build workflows and perform Analytics
- Read various file formats, perform various transformations, Dedup, store results to S3, Hive, ElasticSearch etc..
- Write custom code using SQL, Scala, Python nodes in the middle of a pipeline
- Build & Test Machine Learning models on Big Data Instantly
- Rapidly build & deploy Streaming Applications using Kafka and Spark Streaming
- Submit the Job on EMR Livy
- Submit the Job on AWS Glue
Highlights
- A single platform for your Advanced Analytics needs with 350+ pre-packaged drag and drop processors to simplify your Data Science project execution.
- Minimizes coding effort by providing a visual interface for building data pipelines and machine learning models.
- Sparkflows enables push down analytics, as a result of which the processing happens where the data resides resulting in easy data governance.
Details
Typical total price
$0.954/hour
Pricing
Free trial
Instance type | Product cost/hour | EC2 cost/hour | Total/hour |
|---|---|---|---|
m3.xlarge | $0.35 | $0.266 | $0.616 |
m3.2xlarge | $0.35 | $0.532 | $0.882 |
m4.large | $0.35 | $0.10 | $0.45 |
m4.xlarge | $0.45 | $0.20 | $0.65 |
m4.2xlarge | $0.65 | $0.40 | $1.05 |
m4.4xlarge | $0.65 | $0.80 | $1.45 |
m4.10xlarge | $0.65 | $2.00 | $2.65 |
m4.16xlarge | $0.65 | $3.20 | $3.85 |
r5.xlarge | $0.35 | $0.252 | $0.602 |
r5.2xlarge Recommended | $0.45 | $0.504 | $0.954 |
Additional AWS infrastructure costs
Type | Cost |
|---|---|
EBS General Purpose SSD (gp2) volumes | $0.10/per GB/month of provisioned storage |
Vendor refund policy
Please contact us at support@sparkflows.io if there is need for refund.
Legal
Vendor terms and conditions
Content disclaimer
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 release has the following new features: Rich metadata of ML models, Data Pipelines via Airflow, Livy integration to submit jobs to clusters (EMR, Airflow etc), Enhanced Data Quality and Data Profiling experience, and many new processors.
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 Apache Livy Integration, please see our docs at https://docs.sparkflows.io/en/latest/installation/connection/compute-connection/livy.html
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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