Near Real-Time Anomaly Detection with Delta Live Tables and Databricks Machine Learning
Why is Anomaly Detection Important? Whether in retail, finance, cyber security, or any other industry, spotting anomalous behavior as soon as it happens…
Why is Anomaly Detection Important? Whether in retail, finance, cyber security, or any other industry, spotting anomalous behavior as soon as it happens…
This post is a continuation of the Disaster Recovery Overview, Strategies, and Assessment blog. Introduction A broad ecosystem of tooling exists to implement…
The lakehouse is a new data platform paradigm that combines the best features of data lakes and data warehouses. It is designed as…
There are many different data models that you can use when designing an analytical system, such as industry-specific domain models, Kimball, Inmon, and…
Breaking through the scale barrier (discussing existing challenges) At Databricks, we are hyper-focused on supporting users along their data modernization journeys. A growing…
Data powers scientific discovery and innovation. But data is only as good as its data management strategy, the key factor in ensuring data…
Incremental ETL (Extract, Transform and Load) in a conventional data warehouse has become commonplace with CDC (change data capture) sources, but scale, cost,…
Deep Learning (DL) models are being applied to use cases across all industries — fraud detection in financial services, personalization in media, image…
Behind the growth of every consumer-facing product is the acquisition and retention of an engaged user base. When it comes to customer acquisition,…
One of the questions that we often hear from our customers these days is, “Should I develop my solution in Python or R?”…