Systems | Development | Analytics | API | Testing

In part one of this series, we successfully built a TypeScript Lambda on the AWS cloud. But we left a lot of room for improvement in terms of the developer experience. For starters, the Lambda didn’t run on a local machine, which is cumbersome. The code we wrote is also not testable, which makes refactoring hard or, at least, dangerous. In this take, let’s focus on improving the developer experience. The goal is to make the code more robust and easier to work with. Ready?

Live experiences are at the heart of the modern web. And delivering them to small audiences is relatively easy, thanks to protocols such as WebSockets. But there is a challenge. The difficulty involved in scaling WebSockets is non-linear. In other words, there comes a point where serving more clients demands significantly more complex architecture.

Billions of people rely on WhatsApp each day to communicate in realtime. Friends exchange memes, expats catch up with their families, businesses take bookings and run customer support, and teams ranging from emergency services to on-call engineers at tech companies even use WhatsApp as their primary communication tool. So when WhatsApp had an hours-long global outage on 25 October 2022, the world noticed.

SQL is the universal language of data modeling. While it is not what everyone uses, it is what most analytics engineers use. SQL is found all across the most popular modern data stack tools; ThoughtSpot’s SearchIQ query engine translates natural language query into complex SQL commands on the fly, dbt built the entire premise of their tool around SQL and Jinja. And within your Snowflake data platform, it’s used to query all your tables.

The PHP ErrorException class is meant to be thrown explicitly to catch and handle errors that would otherwise be ignored, such as Notices or Warnings.

It’s no secret that advancements like AI and machine learning (ML) can have a major impact on business operations. In Cloudera’s recent report Limitless: The Positive Power of AI, we found that 87% of business decision makers are achieving success through existing ML programs. Among the top benefits of ML, 59% of decision makers cite time savings, 54% cite cost savings, and 42% believe ML enables employees to focus on innovation as opposed to manual tasks.