As many countries face another wave of COVID-19 cases, organizations around the world are planning new ways to keep their workforces safe. Leading companies have made huge strides in managing coronavirus while keeping their workplaces open. But despite how far we’ve come, many employees still doubt that their organizations are ready to respond to COVID-19.
Retaining customers is more important for survival than ever. For businesses that rely on very high user volume, like mobile apps, video streaming, social media, e-commerce and gaming, fighting churn is an existential challenge. Data scientists are leading the fight to convert and retain high LTV (lifetime value) users.
With the complexity of data growing across the enterprise and emerging approaches to machine learning and AI use cases, data scientists and machine learning engineers have needed more versatile and efficient ways of enabling data access, faster processing, and better, more customizable resource management across their machine learning projects.
Learn the how and what of analytics and data integration. This is the first in a two-part abridged version of The Essential Guide to Data Integration. Read Part 2 here, and get the full book for free here! You can also watch the webinar. What is data analytics How do you integrate data? Should you build or buy a data analytics solution? What are some business and technical considerations for choosing a data analytics tool, and how can you get started? Let’s start with the first two questions.
Without an overarching company data culture, even the best technology tools won’t get you where you want to go, say the co-founders of Data Culture. Data isn’t just a tech solution. For Gabi Steele and Leah Weiss, founders of the consultancy Data Culture, it’s also a “people” solution. Even within companies that enthusiastically embrace a cloud-based modern data stack, a substantial gap often exists between the business and data sides of the organization.
Leaving movie quotes aside, load testing helps you understand how your application behaves under both normal and peak conditions, and discover what is causing the degradation. But truly, the main goal of load testing is not to lose customers. You don’t do load testing for the sake of doing load testing.
We are happy to announce the release of Kuma 1.0.1 with a few improvements and fixes, and we suggest to upgrade to start using the greatest and latest. This is a minor update on top of Kuma 1.0 that shipped last week with over 70+ features and improvements. For a complete list of features and updates, take a look at the full changelog. Join us on our community channels to learn more about Kuma, including our official Slack chat.
In this guide you’ll learn how to send Moesif behavioral emails with Mailgun. Moesif behavioral emails is a service that sends emails to customers based on their requests to your API. These emails can be used to notify users about technical issues, such as API limits or broken integrations, as well as business-related events such as how many items you’ve shipped. If something can be mapped to an API call, then you can send an email on it.
In another life, I taught the Book of Genesis to high school students, including The Tower of Babel excerpt. It struck me ironic that God’s wrath strikes down the tower, cofounds the universal language and scatters humans around the globe to teach King Nimrod a lesson in hubris; meanwhile, the boys in my class were texting their girlfriends across the country and playing video games with friends in Europe and Asia.
We launched BigQuery ML, an integrated part of Google Cloud’s BigQuery data warehouse, in 2018 as a SQL interface for training and using linear models. Many customers with a large amount of data in BigQuery started using BigQuery ML to remove the need for data ETL, since it brought ML directly to their stored data. Due to ease of explainability, linear models worked quite well for many of our customers.
