Analyzing Customer Attrition in Subscription Models
Download the notebooks to demo the solution covered below The subscription model is experiencing a renaissance. Gone are the days of the penny music CD clubs, replaced by an ever-increasing assortment of digital streaming services delivering music, videos and more directly to consumers’ devices in exchange for a modest recurring fee. Today, 70% of US...
How to Extract Market Drivers at Scale Using Alternative Data
Watch the on-demand webinar Alternative Data Analytics with Python for a demonstration of the solution discussed in this blog and/or download the following notebooks to try it yourself. Stock Analysis - Plant-based Meat Historical Data GDELT News Source In Lakehouse Text Analytics on GDELT Alternative Data Time Series Foot Traffic Forecasting Introduction Why Alternative data...
A data-driven approach to Environmental, Social and Governance
The future of finance goes hand in hand with social responsibility, environmental stewardship and corporate ethics. In order to stay competitive, Financial Services Institutions (FSI) are increasingly disclosing more information about their environmental, social and governance (ESG) performance. By better understanding and quantifying the sustainability and societal impact of any investment in a company or...
Allow Simple Cluster Creation with Full Admin Control Using Cluster Policies
What is a Databricks cluster policy? A Databricks cluster policy is a template that restricts the way users interact with cluster configuration. Today, any user with cluster creation permissions is able to launch an Apache Spark™ cluster with any configuration. This leads to a few issues: Administrators are forced to choose between control and flexibility....
Announcing GPU-aware scheduling and enhanced deep learning capabilities
Databricks is pleased to announce the release of Databricks Runtime 7.0 for Machine Learning (Runtime 7.0 ML) which provides preconfigured GPU-aware scheduling and adds enhanced deep learning capabilities for training and inference workloads. Preconfigured GPU-aware scheduling Project Hydrogen is a major Apache Spark™ initiative to bring state-of-the-art artificial intelligence (AI) and Big Data solutions together....
Announcing MLflow Model Serving on Databricks
Databricks MLflow Model Serving provides a turnkey solution to host machine learning (ML) models as REST endpoints that are updated automatically, enabling data science teams to own the end-to-end lifecycle of a real-time machine learning model from training to production. When it comes to deploying ML models, data scientists have to make a choice based...
Introducing Delta Engine
Today, we announced Delta Engine, which ties together a 100% Apache Spark-compatible vectorized query engine to take advantage of modern CPU architecture with optimizations to Spark 3.0’s query optimizer and caching capabilities that were launched as part of Databricks Runtime 7.0. Together, these features significantly accelerate query performance on data lakes, especially those enabled by...
Time Traveling with Delta Lake: A Retrospective of the Last Year
Try out Delta Lake 0.7.0 with Spark 3.0 today! It has been a little more than a year since Delta Lake became an open-source project as a Linux Foundation project. While a lot has changed over the last year, the challenges for most data lakes remain stubbornly the same - the inherent unreliability of data...
Customer Lifetime Value Part 2: Estimating Future Spend
Download the Customer Lifetimes Part 2 notebook to demo the solution covered below, and watch the on-demand virtual workshop to learn more. You can also go to Part 1 to learn how to estimate customer lifetime duration. 20% of your customers account for 200% of your profits. You read that correctly. This seems a mathematical...
Simplify Python environment management on Databricks Runtime for Machine Learning using %pip and %conda
Today we announce the release of %pip and %conda notebook magic commands to significantly simplify python environment management in Databricks Runtime for Machine Learning. With the new magic commands, you can manage Python package dependencies within a notebook scope using familiar pip and conda syntax. For example, you can run %pip install -U koalas in...
