At Next ‘23, Exabeam, Dun & Bradstreet, Optimizely and LiveRamp shared how they use BigQuery and Google Data and AI Cloud for data monetization.
Cloud transformation is ranked as the cornerstone of innovation and digitalization. The legacy IT infrastructure to run the business operations—mainly data centers—has a deadline to shift to cloud-based services. Agility, innovation, and time-to-value are the key differentiators cloud service providers (CSP) claim to help organizations speed up digital transformation projects and business objectives.
To ensure a frictionless AI/ML development lifecycle, ClearML recently announced extensive new capabilities for managing, scheduling, and optimizing GPU compute resources. This capability benefits customers regardless of whether their setup is on-premise, in the cloud, or hybrid. Under ClearML’s Orchestration menu, a new Enterprise Cost Management Center enables customers to better visualize and oversee what is happening in their clusters.
The software industry has evolved more quickly in 10 years than most industries do in 1,000, yet we continue to grapple with the basic elements of date and time. Practically all our apps rely on time in some way, and when our users are spread all over the world it can be fiendishly difficult to get the timestamps right. Kotlin language, the dominant Android programming language, provides native classes (or blueprints) to help us deal with date and time.
One year ago, Heroku sunsetted its free tier. Today, we want to reaffirm our commitment to maintaining our free tier, dive into why offering a free tier for compute is complicated (we are looking at you crypto miners), take the time to explain how we intend to sustain it, and explain why we are so committed to providing a free tier. Long story short: we aim to keep a free tier thanks to how we control our costs.
Every business that analyzes their operational (or transactional) data needs to build a custom data pipeline involving several batch or streaming jobs to extract transactional data from relational databases, transform it, and load it into the data warehouse. In this post, we show how you can leverage Amazon Aurora zero-ETL integration with Amazon Redshift and ThoughtSpot for GenAI driven near real-time operational analytics.
