Competitive Advantage
Why customers choose Sparkflows
over Competitors?
Self-Service ISVs
No Native Data Lake Integration |
Not Completely Distributed | Less Scalable
Proprietary Algorithms | Weak Open Integrations |
Not Extensible | Vendor Lock-Ins
Very Expensive
Comprehensive Native Data Lake Integration | Completely Push Down | Much better Distributed Processing | Higher Scalability
Open Technologies | Strong Integrations | End-2-End Self-Service | Fully Extensible | No Vendor Lock-Ins
Competitively Priced
Sparkflows Vs Competitors – Diving into details
Data Lake Integrations
Open Technologies
Machine Learning
Analytical Apps and Reports
Low-Code & Extensibility
Data Quality
Vertical Solutions
Automations
Title
Sparkflows
Competitor
Details
Integration with Data Lakes on cloud or on premise
Support for Open Technologies like Apache Spark, H2O, Airflow, Prophet, ARIMA, MLflow etc.
Support for various ML engines and ML algorithms in workflows
Support for building and executing Analytical Apps and Reports
Ability to code within the workflows, ability to generate open-source code and extend the feature set.
Ability to code within the workflows, ability to generate open-source code and extend the feature set.
Out of the box vertical solutions and ability to build powerful end to end new solutions
Automations like auto data profiling, auto-ml, automated editor etc.
Sparkflows Vs Competitors – Diving into details
There are 100K+ Data Lake Instances and growing. They are on all the clouds and on premise. They hold data from all kinds of sources and are hence the best places to perform Machine Learning. However, they are also complex to operate and to write distributed code. Security and scalability are key pieces of the Data Lakes.
Data Lake Integrations
Data Browsers for HIVE, Glue Catalog, Snowflake etc.
Deep Integration with Data Lakes
Distributed Processing
Title
Sparkflows
Nearest Competitor
This Competitor only supports Snowflake well
This Competitor does not have much support for Data Browsers
This Competitor has light security integrations
This Competitor is not inherently built to run distributed on the Data Lakes
Sparkflows integrates with AWS EMR, Glue, Databricks, Azure HD Insights, Google Data Proc, Cloudera, Kubernetes, Snowflake. Best support for Open Data Lakes.
Sparkflows provides Data Browsers for all of them
Sparkflows has deep security integrations with AWS IAM Roles, Kubernetes, provides Impersonation
In Sparkflows all the workflows and algorithms run distributed
Open Technologies
All Enterprises have a number of Technologies deployed. These include MLflow, Airflow, etc. Its much easier to them to have the Self-Serve system work on these open technologies rather than bring in new technologies.
Airflow
MLflow
AWS Deequ, Great Expectations for Data Quality
All types of Security / SSO / Authn libraries
Title
Sparkflows
Nearest Competitor
This Competitor does not integrate with Airflow
This Competitor does not integrate with MLflow
This Competitor does not integrate with them.
No other competitor has such wider support for security libraries.
Sparkflows integrates deploy with Airflow
Sparkflows integrates with MLflow
Sparkflows integrates with AWS Deequ and Great Expectations
Sparkflows has detailed support for all types of Security libraries (Apache Ranger, kerbores, PingId, Okta, IAM Roles etc.) with extremely strong Governance and Auditing.
Catalog
Competitors lack in such extensive support of catalogs.
Sparkflows supports connectivity with Data Hub, Unity Catalogs and external Data lineage using API.
Machine Learning
There are certain machine learning engines which all the Enterprises use. These include Scikit-learn, Apache Spark ML, H2O, Prophet, ARIMA, Keras, Tensorflow. Enterprises expect the self-serve technologies to deploy support them.
Scikit-learn
H2O
Prophet
Keras / Tensorflow
Title
Sparkflows
Nearest Competitor
This Competitor partially supports Scikit-learn
This Competitor does not support H2O in workflows
This Competitor does not support Prophet in workflows
This Competitor does not support deep learning technologies in workflows
Sparkflows supports Scikit-learn
Sparkflows supports H2O
Sparkflows supports Prophet
Sparkflows supports Deep learning technologies
AutoML
This Competitor supports AutoML
Sparkflows supports AutoML