Operations | Monitoring | ITSM | DevOps | Cloud

So here we are…the final chapter. In Part 2 of this series, we started to drill down into some of the concepts that make Kafka great. We concluded that although terminology between MQTT and Kafka was similar (for example topics), they behaved quite differently under the hood. We also took a brief overview of Kafka Connect and how we can use some of the enterprise connectors to stream our data to other platforms. Yet we did learn that Kafka does have some shortfalls.

8.3.0 has an issue that could cause creating and accessing snapshots against Azure snapshot repositories to fail authenticating when using SAS tokens. This impacts self-managed customers who have deployed 8.3.0. Elastic Cloud Azure deployments are not currently being upgraded to 8.3.0 and are not impacted as a result. The latest release of Elastic Enterprise Search brings to market enhancements to getting data into Elastic Enterprise Search.

Operating sustainably and promoting green practices can be more complex than you imagine. The benefits of sustainable practices can be significant for businesses, people and the planet. Companies need to ensure that they’re achieving those benefits in a way that complies with established rules and regulations to maximize the impact these initiatives have.

On the heels of several exciting developments about Splunk Cloud Platform announced at.conf22, I am excited to share today that Data Manager now supports onboarding of Microsoft Azure data sources, effective immediately.

It’s the type of nightmare that leaves developers in a cold sweat. Imagine waking up to a message from your team that simply says, “We lost a cluster,” but it’s not a dream at all. InfluxDB Cloud runs on Kubernetes, a cloud application orchestration platform. We use an automated Continuous Delivery (CD) system to deploy code and configuration changes to production. On a typical workday, the engineering team delivers between 5-15 different changes to production.

The term ‘big data’ is used so commonly in research and marketing terms, you would think it’s been here forever. But actually, big data evolved from database marketing (read first efforts of targeted direct mail) in the mid to late 1980’s. This was the first attempt of marketing agencies and large corporations to begin segmenting their customer databases to create different offers based on the demographics or psychographics of their customers.