Running your own Kafka is starting to feel like wading through oatmeal. We’re not the only ones thinking that. The majority of organizations we speak to have or are in the process of moving their Kafka to a managed service. If you’re already an AWS-shop, Managed Streaming for Apache Kafka (MSK) is a no-brainer. It is the same Kafka that we know and love and integrated with other AWS services such as IAM, Cloudwatch, Cloudtrail, KMS, VPC and more.
Understand the impact of data transfer and egress costs across Azure, Amazon Web Services, and Google Cloud platform in data integration One of the most frequent questions asked by cloud-savvy, price-aware customers is something like: Ok, so we like that your tool makes it easy to integrate our cloud database and storage in our centralized data warehouse, but I know our budget will be scrutinized for Total Cost of Ownership (TCO), including our data egress costs.
With 2.1 billion smartphone users and 10,000+ mobile apps released daily, the mobile application field seems lucrative yet more competitive than ever, especially the testing landscape. To account for such demands, entering cloud mobile testing: transparency and mobility allow businesses and developers teams to optimize their mobile app testing pipeline while ensuring the application quality.
This post is about Kafka and the process I have been through recently writing a performance test for an application that subscribes to messages from this technology. The test I ended up with was in the end very straightforward but there were several hurdles that took a while to resolve. I hope that reading this post will hopefully help you avoid them.
Kotlin Multiplatform apps are a great option for big projects. They allow us to unify the business logic under a Kotlin module while using the latest perks of each platform’s individual UI, significantly reducing development costs. Debugging in Kotlin Multiplatform is easy, as you can use typical tools like adding breakpoints and printing logs in the console – and in Bugfender. Continue reading for a full tutorial. KMM stands for Kotlin Multiplatform Mobile.
The push to embrace cloud-based technologies has undoubtedly transformed IT infrastructures at every level of government. Federal, state, and local agencies have made significant strides in modernizing how data is collected, stored, and analyzed, all in service of their mission and in fulfillment of strategic IT mandates.
The infamous data science workflow with interconnected circles of data acquisition, wrangling, analysis, and reporting understates the multi-connectivity and non-linearity of these components. The same is true for machine learning and deep learning workflows. I understand the need for oversimplification is expedient in presentations and executive summaries. However, it may paint unrealistic pictures, hide the intricacies of ML development and conceal the realities of the mess.
The Internet of Things (IoT) is a term for the ever growing number of internet connected devices that fall beyond the realm of your typical laptop, desktop computer or smartphone. Many of us already own and use IoT devices on a daily basis, these could be anything from "smart" versions of appliances like refrigerators, thermostats and coffee machines through to your expected IoT devices such as Amazon’s Alexa & Google’s home speakers.
