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Unlock New Possibilities With Automated Data Transformation

Basic
Advanced
Flows
AI Assistant
Custom
Third-party

Now you can use powerful automation to conform data to a variety of AI or analytics use cases or architectures. Reuse transformation components to filter, change, and morph, regardless of role, skill, or data ingestion process.

Basic transformations

Simple point-and-click interface makes transforming metadata and data values easy for both data engineers and non-SQL experts.

Narrow data sets easily
Ensure compliance with standardized metadata
Fill in missing values, mask sensitive information, or change case
Validate and synchronize effortlessly by setting global rules
Generate logical keys to uncover relationships

User interface showing simple standardization data transformations : standardizing metadata, capitalizing names and adjusting values, and building expressions to adjust data values.

Advanced transformations

Automate data warehouse creation, customize data marts, and speed data onboarding.

Automate incremental data processing
Model and automate data warehouse creation
Support multiple data relationships with semantic layering
Optimize join operations with automated denormalization
Customize and automate data marts with star schemas

Diagrams of advanced data transformations using customized tables of data marts with star schemas, featuring options for types, columns, and selection of transaction date columns.

Transformation flows

Create visual transformation flows with a simple drag-and-drop interface. It’s easy for both data engineers and non-SQL experts. You can:

Cleanse and shape your datasets with a palette of data transformation processors
Generate SQL statements, orchestrate flow, and push code to target platform for runtime execution
Support Snowflake, Databricks, Google BigQuery, Azure Synapse Analytics, and Microsoft Fabric

Graphic of a software interface showing a data transformation with nodes for Orders, Aggregate, Join, Employees, and Filter. A 'Join 1' configuration window is open, displaying join type and conditions.

AI assistants and processors

Improve your development productivity by using the latest AI innovations.

Use the AI assistant to generate SQL from natural language prompts
Deliver data to vector stores for RAG apps that could work with Snowflake Cortex, Databricks, Pinecone, Elasticsearch, Neo4J, and ChromaDB
Create embeddings with LLM support from Azure Open AI, Open AI, Snowflake Cortex, and Databricks DBRX
Use the Databricks AI function processor to invoke a Foundation Model for sentiment analysis, classification, and translation

Illustration of an on-screen AI assistant providing answers. A generated SQL query is displayed next to a prompt asking for a list of top selling products, including the average sales price and the number of customers.

Custom transformations

Experienced SQL practitioners can add custom SQL for complex data transformations as part of the transformation pipeline.

Third-party data transformations

Transform and enrich any data in your cloud data platform. Handle diverse data sources with agility and flexibility by mixing and matching data for various use cases, with accommodation for many data ingestion tools and processes.

Learn about transforming data with expanded data onboarding options

Flowchart showing the data integration process. It starts from enterprise and cloud data sources, through data ingestion and cloud data platform, and ends with data consumption for analytics and machine learning.