Databricks Genie CodeDatabricks
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Related Products
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About
Genie Code is an AI agent built for data teams that analyzes, builds, and maintains complex data workflows inside the Databricks workspace. It autonomously plans and executes multistep tasks while adapting to an organization’s data and governance model, with specialized capabilities across data engineering, data science, machine learning, and business intelligence. Grounded in Unity Catalog metadata, semantics, and governance, it can identify authoritative tables, metrics, and assets, understand dependencies across data and AI systems, and respect existing access controls. For data science, Genie Code can find and clean data, explore datasets, test hypotheses, and generate shareable reports. Machine learning workflows include feature engineering, model training and evaluation, deployment, endpoint configuration, and performance tuning. Data engineers can use natural language to automate ETL workloads, optimize queries, and build Spark Declarative Pipelines.
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About
Hackolade Studio is a powerful data modeling platform that supports a wide range of technologies including relational SQL and NoSQL databases, cloud data warehouses, APIs, streaming platforms, and data exchange formats.
Designed for modern data architecture, it enables users to visually design, document, and evolve schemas across systems like Oracle, PostgreSQL, Databricks, Snowflake, MongoDB, Cassandra, DynamoDB, Neo4j, Kafka (with Confluent Schema Registry), OpenAPI, GraphQL, and more.
Hackolade Studio offers forward and reverse engineering, schema versioning, model validation, and integration with metadata catalogs such as Unity Catalog and Collibra.
It empowers data architects, engineers, and governance teams to collaborate on consistent, governed, and scalable data models. Whether building data products, managing API contracts, or ensuring regulatory compliance, Hackolade Studio streamlines the process in one unified interface.
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Platforms Supported
Windows
Not Supported
Mac
Not Supported
Linux
Not Supported
Cloud
Supported
On-Premises
Not Supported
iPhone
Not Supported
iPad
Not Supported
Android
Not Supported
Chromebook
Not Supported
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Platforms Supported
Windows
Supported
Mac
Supported
Linux
Supported
Cloud
Supported
On-Premises
Not Supported
iPhone
Not Supported
iPad
Not Supported
Android
Not Supported
Chromebook
Not Supported
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Audience
Data engineers, data scientists, ML engineers, and analytics teams seeking to automate complex data, machine learning, pipeline, and BI workflows
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Audience
Data architects, data modelers, data engineers - across all industries and organization sizes
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Support
Phone Support
Supported
24/7 Live Support
Not Supported
Online
Supported
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Support
Phone Support
Not Supported
24/7 Live Support
Supported
Online
Supported
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API
Offers API
Not Supported
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API
Offers API
Supported
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Screenshots and Videos |
Screenshots and Videos |
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Pricing
No information available.
Free Version
Not Supported
Free Trial
Supported
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Pricing
€175 per month
Annual subscriptions possible
Volume discount possible Possibility to purchase consurrent (floating) licenses via purchase order
Free Version
Supported
Free Trial
Supported
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Reviews/
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Reviews/
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Training
Documentation
Supported
Webinars
Supported
Live Online
Supported
In Person
Not Supported
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Training
Documentation
Supported
Webinars
Supported
Live Online
Supported
In Person
Not Supported
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Company InformationDatabricks
Founded: 2013
United States
www.databricks.com/product/genie/code
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Company InformationHackolade
Founded: 2016
Belgium
hackolade.com
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Alternatives |
Alternatives |
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Categories |
Categories |
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Integrations
Databricks
Supported
Apache Atlas
Not Supported
Apache Avro
Not Supported
Apache Kafka
Not Supported
BigID
Not Supported
Bitbucket
Not Supported
Collibra
Not Supported
Firebase
Not Supported
Google Cloud BigQuery
Not Supported
GraphDB
Not Supported
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Integrations
Databricks
Supported
Apache Atlas
Supported
Apache Avro
Supported
Apache Kafka
Supported
BigID
Supported
Bitbucket
Supported
Collibra
Supported
Firebase
Supported
Google Cloud BigQuery
Supported
GraphDB
Supported
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