We are thrilled to share that we’ve raised $7M in seed funding! At Koyeb, we simplify app deployment with our global serverless platform. We provide an easy way to deploy full-stack applications and databases in production, everywhere, in minutes. We’re focused on allowing developers and businesses to seamlessly build, run, and scale any application globally, with no code rewrite or infrastructure management.
In today's evolving technology landscape, businesses are increasingly recognizing the potential of migrating legacy systems to the cloud. Even though they are the backbone of many organizations, legacy systems and services deployed via on-premises servers often need help to keep up with modern business requirements. Cloud migration offers a transformative opportunity to enhance scalability, flexibility, and efficiency, while enabling access to a wealth of innovative services.
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.