Enterprises see embracing AI as a strategic imperative that will enable them to stay relevant in increasingly competitive markets. However, it remains difficult to quickly build these capabilities given the challenges with finding readily available talent and resources to get started rapidly on the AI journey.

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.

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.

38% of data teams spend between 20% and 40% of their time fixing data pipelines¹. Combating these data failures is a costly and stressful activity for those looking to deliver reliable data to end users. Organizations using Azure Data Factory can now benefit from the integration with Kensu to expedite this process. Their data teams can now observe data within their Azure Data Factory pipelines and receive valuable insights into data lineage, schema changes, and performance metrics.

If you ever tried to go global, you have probably faced a reality check. A whole new set of issues starts to appear when you start to operate a workload over multiple locations across the globe: So it looks like a great idea in theory, but in practice, all of this complexity multiplies the number of failure scenarios to consider!

Learn key strategies for migrating legacy systems to cloud-based platforms to modernize.

We previously explored how we built our own Serverless Engine and a multi-region networking layer based on Nomad, Firecracker, and Kuma. But what about the architecture of the engine that orchestrates these components across the world? This is an interesting topic to work on and we thought it could be useful to share some internals out there. Put on your scuba equipment, this is a deep dive into our architecture and the story of how we built our own global deployment engine.

Finding meaningful cost savings is a high priority for companies running their enterprise architecture in the public cloud. Cloud costs have ballooned in recent years and account for over 30% of IT budgets, reports IDC. Enterprises have sought to shift their cloud workloads from on-premises to public clouds, depending upon pricing and performance, to provide themselves with better leverage and business flexibility.