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.
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 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.
One tool for all your Python performance tracking needs We're building this neat service in Python to ingest data in Elasticsearch from various sources (MySQL, Network Drive, AWS, etc.) for Enterprise Search. Sucking data from a third-party service to Elasticsearch is usually an I/O-bound activity. Your code sits on opened sockets and passes data from one end to the other. That's a great use case for an asynchronous application in Python, but it needs to be carefully crafted.
Log files and system logs have been a treasure trove of information for administrators and developers for decades. But with more moving parts and ever more options on where to run modern cloud applications, keeping an eye on logs and troubleshooting problems have become increasingly difficult.
The structured nature of Kubernetes enables a repeatable and scalable means of deploying and managing services and applications. This has led to widespread adoption across market verticals for both on-premises and cloud deployment models. The autonomous nature of Kubernetes operation, however, demands comprehensive, fully-converged observability and security. This is uniquely possible today using the Elastic platform.
We’re pleased to announce that Elastic has been named a Leader in the 2022 Gartner® Magic Quadrant™ for Insight Engines. This is our second year of inclusion in the Gartner Magic Quadrant for this category, and this year’s evaluation places Elastic as the furthest entry on the "Completeness of Vision" axis.
We are excited to announce that Elastic has been recognized as a Strong Performer in The Forrester Wave™: Artificial Intelligence for IT Operations (AIOps), Q4 2022 in our first year participating! As organizations modernize their infrastructure and applications, operations and development teams are faced with an exponential growth in data.
Elastic Universal Profiling is based on technology that came into Elastic as part of the acquisition of optimyze.cloud — a startup that had developed Prodfiler.com, the world’s first frictionless fleet-wide in-production multi-runtime profiler that was launched in August 2021. In order to bring the vision of frictionless deployability, low performance overhead, “just run it everywhere” magic to the broader market, a number of technical innovations were necessary.
The transition to distributed applications is in full swing, driven mainly by our need to be “always-on” as consumers and fast-paced businesses. That need is driving deployments to have more complex requirements along with the ability to be globally diverse and rapidly innovate.
