Operations | Monitoring | ITSM | DevOps | Cloud

January 2023

Using AIOps for automation and efficiency in observability and IT operations

Artificial intelligence for IT Operations (or AIOps) has been playing an expanding role in helping SREs, DevOps, and developers effectively navigate the challenges around application and infrastructure complexity, pace of change, and data volume that characterize the operations landscape.

How to create a document schema for product variants and SKUs with Elastic Enterprise Search

Learn more about product variants and SKUs and how to display a product hierarchy in search results using Elastic Enterprise Search. This technical demo shows how to index products for search, group variants, and add an in-stock filter. Additional Resources.

How to use search analytics in Elastic

Learn how to use search analytics in Elastic Enterprise Search and Kibana. This demo shows how to use search data in Elastic to improve the search experience. Get a quick tutorial on analyzing out-of-the-box search analytics like top search queries, queries with no results, and queries with no clicks. See how to enrich, visualize, and share analytics in Elastic.

Elastic Enterprise Search for ecommerce demo

In this introductory demo, learn how to use Elastic Enterprise Search for ecommerce use cases to improve user experience across the buyer journey. See autocomplete, scoped search suggestions, filtering & faceting, conditional faceting, product variations, and in-store pickup and store locator capabilities. Additional Resources.

Easily analyze AWS VPC Flow Logs with Elastic Observability

Elastic Observability provides a full-stack observability solution, by supporting metrics, traces, and logs for applications and infrastructure. In a previous blog, I showed you how to monitor your AWS infrastructure running a three-tier application. Specifically we reviewed metrics ingest and analysis on Elastic Observability for EC2, VPC, ELB, and RDS.

Getting started with unified observability for Azure in less than 10 minutes using terraform

This video provides a step-by-step guide on how to observe Microsoft Azure environments. This will only take about 10 minutes of working time for you to get a fully configured Elastic Cluster that is actively collecting the data of your Azure environment. Chapters: Additional Resources.

Detect data exfiltration activity with Kibana's new integration

Does your organization’s data include sensitive information, like intellectual property or personally identifiable information (PII)? Do you want to protect your data from being stolen and sent (i.e., exfiltrated) to external web services? If the answer to these questions is yes, then Elastic’s Data Exfiltration Detection package can help you identify when critical enterprise data is being stolen and exfiltrated.

Why metrics, logs, and traces aren't enough

Unlock the full potential of your observability stack with continuous profiling Identifying performance bottlenecks and wasteful computations can be a complex and challenging task, particularly in modern cloud-native environments. As the complexity of cloud-native environments increases, so does the need for effective observability solutions.

AIOps Essentials: Automating actions from AIOps analysis | AIOps Use Cases (5/5)

Artificial intelligence for IT operations (AIOps) is a way to automate tasks that are typically carried out by site reliability engineers (SREs). It aims to make the lives of SREs easier by helping them reduce the amount of noise coming from systems, surface issues more easily, and perform root cause analysis by correlating data from different systems.

Parsing and enriching log data for troubleshooting in Elastic Observability

In an earlier blog post, Log monitoring and unstructured log data, moving beyond tail -f, we talked about collecting and working with unstructured log data. We learned that it’s very easy to add data to the Elastic Stack. So far the only parsing we did was to extract the timestamp from this data, so older data gets backfilled correctly. We also talked about searching this unstructured data toward the end of the blog.

AIOps Essentials: What is AIOps? | AIOps Use Cases with Elastic Observability (1/5)

Artificial intelligence for IT operations (AIOps) is a way to automate tasks that are typically carried out by site reliability engineers (SREs). It aims to make the lives of SREs easier by helping them reduce the amount of noise coming from systems, surface issues more easily, and perform root cause analysis by correlating data from different systems. AIOps can also automate actions based on identified problems using machine learning. In this video series, we demonstrate how to use Elastic to implement AIOps.

AIOps Essentials: How to Reduce Noise in Ingested Telemetry on Elastic | AIOps Use Cases (2/5)

Artificial intelligence for IT operations (AIOps) is a way to automate tasks that are typically carried out by site reliability engineers (SREs). It aims to make the lives of SREs easier by helping them reduce the amount of noise coming from systems, surface issues more easily, and perform root cause analysis by correlating data from different systems.

AIOps Essentials: Issue Detection using Anomaly Detection on top of APM | AIOps Use Cases (3/5)

Artificial intelligence for IT operations (AIOps) is a way to automate tasks that are typically carried out by site reliability engineers (SREs). It aims to make the lives of SREs easier by helping them reduce the amount of noise coming from systems, surface issues more easily, and perform root cause analysis by correlating data from different systems

AIOps Essentials: How to use Distributed Tracing for Root Cause Analysis | AIOps Use Cases (4/5)

Artificial intelligence for IT operations (AIOps) is a way to automate tasks that are typically carried out by site reliability engineers (SREs). It aims to make the lives of SREs easier by helping them reduce the amount of noise coming from systems, surface issues more easily, and perform root cause analysis by correlating data from different systems.

Elastic Observability 8.6: Maximizing operational efficiencies with improved application analysis and workflow integrations

Elastic Observability 8.6 introduces a set of capabilities improving production operations through the introduction of host (EC2/GCP compute/Azure compute) observability, application dependency operations views (insights into databases, caches, etc), and a new connector for Opsgenie. These new features allow customers to: Elastic Observability 8.6 is available now on Elastic Cloud — the only hosted Elasticsearch offering to include all of the new features in this latest release.

Elastic Enterprise Search 8.6: Reduce time to relevant search results - for file systems, MongoDB, and Amazon S3

Elastic Enterprise Search 8.6 enables customers to index searchable content on file systems, network drives, MongoDB, and Amazon S3. With new connectors for network drives and Amazon S3, content indexed can easily be transformed for natural language processing (NLP) use cases with intuitive tooling to test and tune your search experience with the trained model of your choice.