In part one of this series, we walked through how to use Streamlit, Apache Kafka, and Apache Flink to create a live data-driven user interface for a market data application to select a stock (e.g., SPY) and discussed the structure of the app at a high level. First, data with information on stock bid prices is moved via an Alpaca websocket, then, it’s produced to a Kafka topic in Confluent Cloud where it is also processed with Flink SQL.

Having a dedicated branching strategy is key for teams wanting to speed up their workflows, meet tasks on a deadline, and reduce code errors. But with all the different branching strategies – including release branching, trunk-based branching, feature branching, and more – it can be difficult to determine which one best fits your team’s needs.

While many tools exist to build and deploy your no-code APIs, your options are limited when your solution requires a custom or highly bespoke APIs that execute custom logic.

In the dynamic world of software development, quality is paramount. Every line of code, every feature, every functionality needs to be meticulously evaluated to ensure a flawless user experience. This is where test cases come in – the unsung heroes of the development process.

Two of the main tenets of Zero Trust are encryption between services and managing the connections each service is allowed to use. Achieving this generally falls to running a service mesh in a Kubernetes cluster. Refactoring applications to run properly in Kubernetes takes time and considerable investment. For many organizations, running their applications on virtual machines will be a necessity for years to come. However, this doesn't mean security should fall behind.

Discovering and surfacing telemetry traditionally can be a tedious and challenging process, especially when it comes to pinpointing specific issues for debugging. However, as applications and pipelines grow in complexity, understanding what’s happening beneath the surface becomes increasingly crucial. A lack of visibility hinders the development and maintenance of high-quality applications and pipelines, ultimately impacting customer experience.

Today’s data-driven world requires an agile approach. Modern data teams are constantly under pressure to deliver innovative solutions faster than ever before. Fragmented tooling across data engineering, application development and AI/ML development creates a significant bottleneck, hindering the speed of value delivery required to stay competitive. Disparate tools create a complex landscape for developers and data teams, hindering efficient pipeline development and deployment.