Logistics industry has to deal with data from multiple sources including bills of lading, customs declaration forms, proofs of delivery, and others. Then they have to ensure the data is prepped, extracted, parsed, converted to the right format and then analyzed. Given how important logistics is in today’s market, it is no wonder that McKinsey Global Supply Chain Leader Survey 2024 reported 74% of respondents were interested in advanced digital and AI-based tools for planning and scheduling.

In part one of this series, we managed distributed state using GenServers. This provided a foundation for understanding some core concepts in distributed Phoenix applications. Now, we turn our focus to deployment and scaling strategies. As your application evolves to meet growing demands, knowing how to scale horizontally, maintain high availability, and monitor distributed components becomes crucial.

At Confluent, we’re committed to building the world's leading data streaming platform that gives you the ability to stream, connect, process, and govern all your data, and makes it available wherever it’s needed, however it’s needed, in real time. Today, we're excited to announce the release of Confluent Platform 7.8. This release builds upon Apache Kafka 3.8, reinforcing our core capabilities as a data streaming platform.

We are seeing businesses across industries engage with large-scale projects needing a talent pool experienced in AI, cloud, IoT, or other emerging technologies. However, the fluctuating nature of these projects requires a flexible workforce, which can only naturally comprise part-time hires. This is why we see a steady investment in the staff augmentation market. The 2023 evaluation for the IT staff augmentation market was approximately USD 300 billion in the recent market reports.

It’s impossible to learn about containerization without hearing about Docker and Kubernetes. These two tools together dominate the world of containers, both being the de facto standard in what they each do. When you’re first getting started learning about containers, it can be quite a challenge to figure out the differences between these two tools.

In today’s data-driven enterprises, the intersection of fiscal responsibility and technical innovation has never been more critical. As data processing costs continue to scale with business growth, building a FinOps culture within your Data Engineering team isn’t just about cost control, it’s about creating a mindset that views cost optimization as an integral part of technical excellence.

For years, companies have operated under the prevailing notion that AI is reserved only for the corporate giants — the ones with the resources to make it work for them. But as technology speeds forward, organizations of all sizes are realizing that generative AI isn’t just aspirational; it’s accessible and applicable now.

Accounts payable (AP) automation is on the rise as finance teams are realizing the benefits of AP automation solutions currently on the market. Teams that achieve partial automation in their AP processes are seeing considerable benefits in terms of time, cost, and efficiency. With vendors now integrating AI technologies into their software solutions, the potential AP automation benefits promise to change the face of accounts payable altogether.

A software update failure led to one of the most infamous outages of the year: the Crowdstrike Outage. The incident is estimated to have cost Fortune 500 companies more than USD 5 billion. Per the congressional testimony by Crowdstrike’s officials, the downtime wasn’t attributed to a malicious attack but a configuration and software update failure. Incidents like these make us question how we deal with our digital ecosystems - the configurations, the monitoring, the testing, etc.