We have explored how we can deploy Machine Learning models using AWS Lambda. Deploying ML models with AWS Lambda is suitable for early-stage projects as there are certain limitations in using Lambda function. However, this is not a reason to worry if you need to utilize AWS Lambda to its full potential for your Machine Learning project. When working with Lambda functions its a constant worry about the size of deployment packages for a developer.
Whether you are planning to use Graylog for security and threat hunting, IT Operations analysis and reporting, or any other use case, getting your logs into Graylog is essential. The process of log collection is sometimes a daunting task, especially if you are planning to collect massive amounts of data. But if you take a minute to answer some key questions before you begin, you can transform the log collection task from daunting to smooth sailing. Here we go with the questions...
Keeping track of what’s going on in Kubernetes isn’t easy. It’s an environment where things move quickly, individual containers come and go, and a large number of independent processes involving separate users may all be happening at the same time. Container-based systems are by their nature optimized for rapid, efficient response to a heavy load of requests from multiple users in a highly abstracted environment and not for high-visibility, real-time monitoring.
If you’re here, it means that your application is a hit, coming through a long way of development and deployments. Your application is finally in a stage where you or your team need to set up more servers than you can handle manually, and you have to provision them fast. There’s also the need to make sure that all of them have the same configuration, packages, and versions in order for your application to have the same behavior in all of them.
Increasing online traffic is the goal for any website managers, developers, or e-commerce website builders. More traffic equals more customers and, therefore, sales. However, it can be too much of a good thing. If you don't take steps to address website or app performance during traffic peaks, it can cause those assets to slow down and ultimately crash. Making you a victim of your own success! Website crashes are a catastrophe for e-commerce stores.
InfluxDB clustering is available as part of InfluxData’s commercial product offered in both managed and self-managed capacities.
This tutorial will walk you through sending StatsD metrics to Telegraf. StatsD is a simple protocol for sending application metrics via UDP. These metrics can be sent to a Telegraf instance, where they are aggregated and periodically flushed to InfluxDB or other output sinks that you have configured. At the time of writing, we have 37 different output plugins supported.
Cloud application requirements have pushed beyond the limitations of relational database management systems. This rising demand for managing unstructured data, along with the growing need for data analytics, has caused significant market growth in the NoSQL database sector. MongoDB, as one of the classical NoSQL databases, is a powerful tool helping companies align with new cloud-based business strategies. With great power comes great responsibility.
Microservices and distributed architectures have become the norm for building modern day applications. While the cloud has made it easier to deploy and scale microservices, it has opened up a complex set of problems for DevOps by virtue of the sheer volume of interactions between those services.
