We’re happy to announce that Oracle Cloud Infrastructure Logging Analytics is now available.
Enterprise log management is a key enabler to ensure that applications meet their business goals and service level objectives. DevOps personnel, such as DBAs, IT and data analysts, developers, and business analysts all need to be able to quickly and easily gain insights from the important information exposed in application, database, infrastructure, and network logs.
Everything generates logs, from internet-of-things (IoT) devices to software applications and containers, to database, infrastructure, and network components. The many terabytes of logs generated by these components every day contain extremely high-value data, but that data is voluminous, non-standard and arrives at a high velocity. Oracle Cloud Infrastructure (OCI) Logging Analytics, which is now generally available, helps customers maximize the value of log and machine data by providing rapid insights about what that data means. It uses smart analytics, applied machine learning, out-of-the-box knowledge content, and advanced visualizations without requiring data scientists against logs from any technology, deployed anywhere.
Figure 1. Oracle Cloud Infrastructure Logging Analytics helps customers maximize the value of log and machine data by providing rapid insights about what that data means, using applied machine learning and advanced visualizations without requiring data scientists.
Customers can use OCI Logging Analytics to debug performance problems caused by various issues, such as poorly written code, business logic issues, misconfigurations, network contention, or databases that need tuning. Logging Analytics is an integrated part of the Oracle Cloud Observability and Management platform and is designed to work hand-in-hand with other platform services, such as Monitoring, Logging, Notifications, Events, Application Performance Monitoring, and Database Management.
Current industry approaches to log management focus primarily on storage and indexing, and when it’s time to ask questions of the data, those approaches assume that operators apply their own external domain knowledge about content and context of those logs to write complex queries and do data science. This approach is inefficient and error-prone, and it results in longer mean time-to-detect and time-to-repair performance and availability issues, since operators are forced to spend more time formulating questions instead of acting.
On the other hand, Oracle Cloud Infrastructure Logging Analytics makes it easy to turn all that raw data into insight. So, operators can spend their effort fixing problems, instead of investigating them. It provides out-of-the-box log enrichment that reveals the meaning of the log data, not just the data itself. It also makes asking questions easy by providing pretuned, operations-optimized machine learning and visualizations that scale across tens of millions of log entries—all available at the touch of a button.
Figure 2. Rich, out-of-the-box visualizations and applied machine learning make it easy to derive insights out of voluminous log data.
Oracle Cloud Infrastructure Logging Analytics also helps operators perform impromptu exploration and forensics about questions that they don’t know they should be asking. All the out-of-the-box visualizations and machine learning can easily be refactored, using an integrated drag-and-drop interface that requires no knowledge of the data structure.
OCI Logging Analytics is topology-aware, meaning that logs are automatically placed in context of application topology. This automation makes it easy to find information about other related components and to zoom in or out on dependencies as necessary.
Finally, Logging Analytics also includes a powerful query language with over 70 primitives for fine-grained control of data analysis.
Figure 3. Topology-aware log enrichment makes impromptu exploration fast and painless.
Oracle understands that applications today are built with wildly heterogeneous technology and often span multiple clouds and on-premises. Logging Analytics provides hundreds of out-of-the-box log parsers that support a wide array of popular commercial and open source technology. The parsers can collect those logs through various API-based and agent-best methods, depending on the technology in question. Logging Analytics also provides an easy-to-use, inline extensibility mechanism for custom logs, ensuring that the full range of machine data can be ingested, enriched, analyzed, and turned into insight.
But don’t take our word for it! Independent analyst firm Omdia recently named Oracle a leader in the Omdia Universe: Selecting a Hybrid and Multicloud Management Solution, 2020-21
Oracle Cloud Infrastructure Logging Analytics is architected to minimize any extra administrative overhead, ensuring that operators can spend their time using the insights they’ve gleaned rather than just configuring the solution. It includes the following low-overhead features:
Fast, easy install and auto-upgrade of log collection agents
Unified agent management from a single place
Seamless user onboarding, roles-based access control, and policy management ensure that the right people have access to right data all the time (and only the right data!).
Full visibility and control on usage, metering, and billing to manage cost
With Oracle Cloud Logging Analytics, DevOps, site reliability engineers, IT operations, and development experts can all use the insight derived from machine data to reduce troubleshooting times, improve user experience, and exceed service level objectives for even their most complex applications, without requiring an army of data scientists.