Artificial Intelligence (AI), machine learning (ML), and big data seem to be the buzzwords of the decade. We’re not just talking robots or autonomous cars — AI and ML’s reach will surely be beyond that. What that really is has been yet to be determined, but the technology will surely stretch across all that SDxCentral covers including 5G, IoT, security, SDN, NFV, and monitoring.

Here are three stories from this week detailing how big data and automation are helping aid in the management and monitoring of enterprise systems and architectures and bringing these words beyond hype to actual services and platforms, particularly in the containerized and microservices world.

Gigaspaces, a vendor that provides software for companies to run transactional and analytical workloads, this week announced that its in-memory real-time analytics platform, InsightEdge, was certified to run on Red Hat’s OpenShift Operator Certification.

Red Hat recently added its Certified Operators toolset, which is part of the its Operator Framework, to OpenShift 4.

With the certification, enterprises can enable more agile development, deployment, and lifecycle management of InsightEdge applications in cloud, on-premises, and in hybrid environments. And when leveraging OpenShift, Gigaspaces users can also accelerate analytics and ML for streaming and historical data for faster and more intelligent insights and actions.

Data is particularly important as companies migrate to cloud and multi-cloud environments. The combination of Red Hat and Gigaspaces, the company says, will optimize bandwidth and reduce data transfer costs between regions. To do this, data is replicated to make the desired changes, custom aggregations, and compression. Data is also encrypted and anonymized to support privacy regulations.

Earlier this year, Gigaspaces added a unified analytics service to its InsightEdge platform. The service accelerates access to data lakes and data warehouses to enable faster and smarter analytics. At that time Karen Krivaa, vice president of marketing at GigaSpaces. told SDxCentral that it stands out with its in-memory platform, InsightEdge, and now its AnalyticsXtreme service simply by offering more in one place.

The certification with Red Hat furthers this ability to operationalize ML to actually obtain insights on data.

Application monitoring company Instana also received Red Hat OpenShift Operator certification for it automatic agent technology. The agent can run on containers, host processes, or microservices to monitor configuration and application root cause analysis on OpenShift.

Instana developed its Application Performance Management (APM) service specifically for microservices. The tool has three core capabilities. First, by building an internal data model it offers continuous discovery and visualization of the entire technology stack to identify performance issues. Second, the APM tool monitors and visualizes this data in real-time. And lastly, it offers AI capabilities, which the company says is a necessity due to the complexity of these environments.

The service has “deep visibility,” Instana says, into a number of languages and over 100 microservice infrastructure technologies. Some of these include Kubernetes, Docker, Spring Boot, NGINX, and Amazon Web Services (AWS).

With this certification these capabilities are extended to OpenShift Kubernetes’ distributions. It will provide visibility into mission-critical apps on OpenShift as well as their dependencies and performance.

Microservices architectures are often more difficult to monitor because these architectures have more application data. But AI is helping to solve this — Instana’s service doesn’t require human configuration or application restarts, but the company claims it can detect changes in the environment in real-time.

InfluxData this week released a beta version of its open source time series database that is hosted on AWS, but fully managed by the company.

InfluxData’s time series database tools are meant to handle massive volumes of time-stamped data. This data is typically produced by IoT devices, applications, networks, containers, and computers. With the tools, enterprises can build monitoring analytics, and IoT applications to scale.

InfluxDB Cloud 2.0, “represents the most significant evolution of the InfluxDB project since it initially launched in 2013,” a spokesperson from the company told SDxCentral. The InfluxDB system as a whole was rewritten to be multi-tenanted and add full programmability of access and control.

The need for this platform, the spokesperson said, has come from gathering feedback from InfluxDB’s open source users and production customers customers “to improve the overall experience and the ability to harvest deeper insights from time series data. Collectively, we found that they need a comprehensive time series platform – ingestion, storage, analytics, dashboarding and alerting in a single unified platform.”

With the latest beta release InfluxData also debuted a rate-limited free tier of the service that will remain free. As more users want to run the service on the cloud, the company says, it was important for them to also only have to pay for what they use across read, write, and compute functions.

There were two other main updates: a new common API that will work across InfluxDB open source, InfluxDB Cloud, or InfluxDB Enterprise and a new language — Flux, which is designed for data scripting, monitoring, and analytics — for users to improve the time to insights and allow them to dig deeper into time series data.