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What is Lakeflow Designer?

Lakeflow Designer provides a no-code data preparation solution that is ready for production, making it an excellent choice for teams aiming to optimize their workflows. It allows users to leverage AI-driven authoring directly within Databricks for data preparation and transformation. This functionality enables modern data teams to perform no-code tasks effortlessly on Databricks, facilitating data management through natural language while circumventing the challenges of adopting new tools, excessive rework, and governance hurdles. By integrating Lakeflow's capabilities with an intuitive interface, Lakeflow Designer offers visual data preparation that preserves the essential power, scalability, and governance aspects of a centralized data platform. The inclusion of Genie Code further enhances the platform by allowing teams to develop and refine transformations based on the data's schema, lineage, and pertinent business context, which leads to greater accuracy and reduced manual effort. Additionally, it eases the transition to production by allowing users to prepare data directly at its origin, ensuring that transformations can be executed in production with Lakeflow Jobs without needing to replicate or adapt logic across various tools. This integrated methodology not only simplifies workflows but also significantly boosts overall productivity for data teams, ultimately fostering a more efficient data management environment.

What is Auraa?

Auraa represents Covasant's cutting-edge, agent-driven data platform tailored exclusively for Databricks, enabling rapid transformation of data into formats suitable for AI applications. Utilizing conversational AI capabilities that function in natural language, organizations can harness agents to autonomously locate diverse data sources, build pipelines, ensure data integrity, and register all elements in Unity Catalog from the outset. This innovative approach entirely eliminates the requirement for conventional pipeline coding, substantially decreases engineering backlogs, and eradicates months of manual setup. Generally, creating a data lake on Databricks may take 18 to 24 months; however, with Auraa, onboarding the initial data source can be achieved in under 15 minutes, the first use case can be operational in mere hours, and the complete deployment timeline can shrink to around 8 to 10 weeks, leading to potential cost savings of up to 70%. By redefining data engineering processes, Auraa manages these as structured, versioned, and governed metadata, moving away from unreliable, hand-coded pipelines. Furthermore, the platform ensures that the Databricks lakehouse remains reproducible and auditable, while continuously improving its functionalities through the deployment of agents, ultimately driving efficiency and innovation in the realm of data management. This transformative capability positions Auraa as an essential tool for organizations aiming to streamline their data operations and accelerate their journey toward AI readiness.

Media

Media

No images available

Integrations Supported

Databricks

Integrations Supported

Databricks

API Availability

API Availability

Pricing Information

Pricing not provided

Pricing Information

Pricing not provided
Free Version
Free Trial Offered?

Supported Platforms

SaaS

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Databricks

Date Founded

2013

Company Location

United States

Company Website

www.databricks.com/product/data-engineering/lakeflow-designer

Company Facts

Organization Name

Covasant Technologies Private Limited

Company Location

India

Company Website

www.covasant.com

Categories and Features

Data Preparation

Not specified

Categories and Features

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