DataMorph does the Extraction, Loading, Transformation (ELT) and orchestration in a single platform, greatly simplifying your data product creation process. DataMorph provides you a visual canvas of operators and SQL snippets to create complex data pipelines and workflows. DataMorph translates these pipelines to run in Spark, the most efficient transformation engine, to get the most out of your cloud data platform and without the data team needing to learn advanced dependencies and scaling techniques.
How DataMorph speeds up reliable Data Product creation:
DataMorph provides a common foundation and framework for both data engineers and data scientists to collaborate and bring ML projects to production faster. By bringing together data pipeline creation, visual + SQL data transformations, and workflow orchestration into a single platform, DataMorph breaks down the silos of data engineers and data scientists so they can release business impacting solutions faster.
How DataMorph enables faster data modeling and ML projects:
DataMorph platform gives you the flexibility to adopt the latest data management best practices at the speed right for your business. DataMorph helps you modernize and migrate to newer platforms while having a stable front-end for data engineers, data scientists, and business analysts to continue delivering insight for stakeholders.
How DataMorph helps you adopt a modern data architecture and orchestration:
DataMorph users interact with our web-based UI for developing and deploying data projects, and running pipelines and workflows.
The control plane, hosted by DataMorph, manages and supports
The data plane sits in your cloud environment, and DataMorph never has direct access to the data. The DataMorph engine and Spark and Orchestration runtimes are provisioned in your secure environment and manage the provisioning, scaling, and execution of data workflows and pipelines, as created in the control plane.
Data pipelines can be used to move data between systems, to read, transform and output data into formats that make data analysis easy. Data pipelines can also perform a variety of other important data-related tasks like data quality checks and schema evolution.
Workflow orchestration is the process of managing and coordinating the execution of data pipelines which involves scheduling and automating the various tasks and processes involved in data pipelines, as well as error handling, alerting, and monitoring.