Offerings
Sparkflows offers diverse solutions for AI, Generative AI, and data engineering.
With ready-to-use vertical use cases, businesses can implement these technologies quickly and effectively.
Self-Serve AI and Gen AI
Sparkflows offers diverse solutions for AI, Generative AI, and Data Engineering. Enterprises can quickly derive value from these technologies by leveraging the ready-to-use Industry Solutions.
Self-Serve Data Engineering
Sparkflows provides self-serve capabilities for data engineering, offering various workbenches and studios to seamlessly build complex data engineering pipelines.
AI + Gen AI Vertical Solutions
Sparkflows offers 90+ AI and Gen AI Vertical Solutions out of the box.
Capability to convert Notebooks to Apps
If you have several Jupyter or Databricks Notebooks, they are likely only being used directly by data scientists. Sparkflows provides a low-code, no-code Designer to build a rich interface for your Notebooks, bringing them to life for your business and power users.
Alteryx Alternative
If you are looking to migrate from Alteryx, Sparkflows offers the perfect platform for your users.
With a powerful workflow designer and the ability to run on any cloud or on-premise platform while natively integrating with them, Sparkflows provides a powerful solution.
DataStage
Informatica
SAS
Snowflake
Biq Query
Cloud Migration
If you are migrating to Databricks, Snowflake, BigQuery, or Redshift from on-premise solutions like DataStage,
Informatica, or SAS, Sparkflows makes the process much easier.
Build workflows in Sparkflows for your jobs and convert them to PySpark or Snowpark code with one click.
Additionally, migrate your data from on-premise systems to the cloud using Sparkflow's powerful self-serve Change Data Capture and transformation tools.
Data Quality Rules
Scheduling
Data Remediation
Trends and Dashboards
Collaborate
Alerts and Notifications
Data Quality Assessment
and Remediation
Sparkflows offers comprehensive Data Quality capabilities, allowing users to define rules or build workflows for data quality checks. Results and metrics are displayed on a Dashboard for easy monitoring. It includes features like fuzzy deduplication, outlier detection, and tagging, as well as handling null values through various imputation methods. Users can filter and save records that fail data quality checks, ensuring data integrity.