As the shift to cloud, modern app architectures and technology stacks continue to accelerate, the demand for real-time analytics to monitor, troubleshoot, secure and speed new innovations to these environments is also accelerating. So, we're not surprised to see demand for continuous intelligence—what we define as: real-time analytics from a cloud-native platform, supporting multiple use cases—is also accelerating.

Energy storage and sustainability are big issues in Hawaii, where oil tankers are shipped in everyday, just to keep the lights on. Resiliency in the energy grid, due to threat of natural disaster, is critical. Energy storage is needed to manage how much solar energy is coming into the market or into the grid. Properly managing the power entering the grid is vital for grid stability since Hawaii has intermittent energy that floods the grid.

Elastic is prepping for Elasticsearch 8.0, but in the meantime is rolling out upgrades and features with Elasticsearch 7.7. The new version introduces asynchronous search as well as changes with Elasticsearch clusters, mapping, SQL enhancements, snapshots and machine learning. This post will cover just a few of the highlights of the new release. Besides asynchronous search, ES 7.7 also introduces multi-class classification, reduced heap usage, inference time features, and better password security.

At STRABAG, we are using Elastic Cloud Enterprise (ECE) for two main use cases within our on-premises web applications. One to power different kinds of search and a second for operations where we ship more than 25,000 log entries per minute to Elastic from our load balancers. The ECE platform runs in an air-gapped environment, and we would still like to be able to use our corporate logins for the ECE platform.

At Elastic, we love data. It’s the backbone of what we do: search. And that data takes many forms. A knowledge base article showing how to reset your cable box — data. Logs from your network — data. IP addresses accessing your secure network — data. A video tutorial on how adults can use TikTok — data. The list goes on. But behind each piece of data is a story. And a person. Or a customer.

Data ingestion and data analysis are the yin and yang of a time series platform. There are many resources to help you ingest data. Typical ingestions are agent-based, imports via CSVs, using client libraries, or via third-party technologies. Once your time series data arrives, analysis completes the circle and often leads to additional data collection, and so on and so forth.