Online cloud storage has taken over the world. Anyone working with computers is familiar with at least one cloud service, and the field is packed with competitors, all offering virtually the same thing. Cloud storage lets users save files from anywhere and access them anywhere. Internxt doesn’t bring any new revolutionary features to the cloud storage world, but it revolutionizes how it works. With Internxt, it’s less what it does and more how it does it.
A look at what Parquet is, how it works and some of the companies using its optimization techniques as a critical component in their architecture. As the amount of data being generated and stored for analysis grows at an increasing rate, developers are looking to optimize performance and reduce costs at every angle possible. At the petabyte scale, even marginal gains and optimizations can save companies millions of dollars in hardware costs when it comes to storing and processing their data.
s3gw is an S3-compatible service, focused on deployments in a Kubernetes environment backed by any PVC, including Longhorn (https://longhorn.io). Since its inception, the primary focus has been on Cloud Native deployments, however, the s3gw can be deployed in a myriad of scenarios, provided there is some form of storage attached.
Last week InfluxData announced IOx, the new time series engine for InfluxDB. We’ve revisited the core of our database to achieve big things with the underlying technology. Users can expect higher performance and more options for querying data. Here’s a quick intro to some of the most exciting things coming with InfluxDB IOx.
This post is part 1 of a 3-part series about monitoring MongoDB performance with the WiredTiger storage engine. Part 2 explains the different ways to collect MongoDB metrics, and Part 3 details how to monitor its performance with Datadog. If you are using the MMAPv1 storage engine, visit the companion article “Monitoring MongoDB performance metrics (MMAP)”.