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Three Practical Use Cases for Databricks on AWS
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Automated data integration: Maximize your data lakehouse
The Databricks Lakehouse platform is a go-to for organizations eager to leverage their data fully. But to reap its full value, one must navigate data integration hurdles. This e-book details how to supercharge your Databricks Lakehouse by:
- Facilitating universal, real-time data ingestion from any source into Databricks
- Streamlining data transformation into analytics-ready forms without manual coding
- And creating a secure, governed data catalog for easy access to reliable data
Read this e-book now to find out how you can automate your entire data pipeline to reduce risk, boost ROI, and free your data engineers.
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The Big Book of Machine Learning Use Cases – 2nd Edition
The world of machine learning is evolving so quickly that it’s challenging to find real-life use cases that are relevant to your day-to-day work.
That’s why Databricks created this comprehensive guide. Get everything you need — use cases, code samples and notebooks — so you can start putting the Databricks Lakehouse Platform to work today.
Plus, you’ll get case studies from leading companies like Comcast, Regeneron and Nationwide.
Learn how to:
- Improve on-shelf availability with out of stock modeling
- Make real-time decisions using quantitative modeling
- Use dynamic time warping and MLflow to detect sales trends series
- Detect financial fraud at scale with decision trees and MLflow on Databricks
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Improving data quality in the age of generative AI
Generative AI can revolutionize your business. But it relies on one crucial element: high-quality data. Inaccurate or biased data can lead to unreliable AI models and costly mistakes.
This playbook from Alteryx and Databricks, Improving Data Quality in the Age of Generative AI, will give you strategies to overcome data quality challenges for your generative AI initiatives.
Inside, you’ll learn:
- Why “garbage in, garbage out” is more relevant than ever in the age of generative AI
- Why a modern data architecture is important for ensuring data quality
- How to bring in the domain experts who understand the data best
- Actionable steps from Alteryx and Databricks to integrate best practices
- And more
