Logentries announced a real-time integration with Google Cloud Platform.
The Logentries log management and analytics service integrates with Google Cloud Logging to offer Google Cloud Platform customers’ an easily configurable choice for log management and advanced analytics including anomaly detection.
The new integration leverages the Google Cloud Publisher-Subscriber (Pub-Sub) API, which provides reliable, many-to-many, asynchronous messaging between Google Cloud Platform and Logentries, for easy communication between services.
“We understand that Log Management and Analytics is a critical customer need and are excited to offer Google customers a choice to easily send logs to a key provider like Logentries,” said Deepak Tiwari, Product Manager, Google Cloud. “Many of Google Compute Engine customers already use Logentries for advanced log analysis. This integration enables customers to use Logentries for Google App Engine and services like Cloud Dataflow as well and makes it even easier to get started. At Google, we are committed to creating an open ecosystem with easy path of integration for partners, and Logentries provides a great example of a leading partner.”
Today’s distributed, cloud-based environments produce billions of machine-generated data, making separate tools for monitoring, alerting, troubleshooting and analyzing data across systems, applications and end users completely unmanageable. Organizations need a single tool to monitor, alert and analyze multiple data sources using one shared data format.
The Logentries and Google Cloud Platform integration provides a meaningful choice for real-time event monitoring, alerting, advanced analytics, and data visualizations to Google customers for a better understanding of their system and application activity and performance. With this visibility, users can monitor and alert on critical activity and exceptions across their apps, including anomaly detection, inactivity alerting, and end user experience metrics.
In addition to data from applications and VMs, logs collected from Google Cloud Platform also contain metadata with every log entry, giving users valuable information, including the exact time any log entry was created, the origins (resource or instance) of each entry along with its security level. With the integration between Logentries and Google Cloud Platform, users can easily:
- Build data visualizations with streaming log data from apps hosted on Google Cloud Platform and VMs hosted on Google Compute Engine.
- Receive real-time alerts on events, inactivity and anomaly detection from Google App Engine and Google Compute Engine.
- Correlate events from Google App Engine with Google Compute Engine to help identify the root cause of issues.
“The Google Cloud Pub/Sub API is a powerful service for routing messages from multiple cloud and third party resources at scale,” explained Trevor Parsons, Co-founder and Chief Scientist of Logentries. “With the new Logentries integration Google customers are now able to generate powerful correlations, visualizations and alerts from disparate sources of application and system-level data – all in real time.”
The Latest
For many B2B and B2C enterprise brands, technology isn't a core strength. Relying on overly complex architectures (like those that follow a pure MACH doctrine) has been flagged by industry leaders as a source of operational slowdown, creating bottlenecks that limit agility in volatile market conditions ...
FinOps champions crucial cross-departmental collaboration, uniting business, finance, technology and engineering leaders to demystify cloud expenses. Yet, too often, critical cost issues are softened into mere "recommendations" or "insights" — easy to ignore. But what if we adopted security's battle-tested strategy and reframed these as the urgent risks they truly are, demanding immediate action? ...
Two in three IT professionals now cite growing complexity as their top challenge — an urgent signal that the modernization curve may be getting too steep, according to the Rising to the Challenge survey from Checkmk ...
While IT leaders are becoming more comfortable and adept at balancing workloads across on-premises, colocation data centers and the public cloud, there's a key component missing: connectivity, according to the 2025 State of the Data Center Report from CoreSite ...
A perfect storm is brewing in cybersecurity — certificate lifespans shrinking to just 47 days while quantum computing threatens today's encryption. Organizations must embrace ephemeral trust and crypto-agility to survive this dual challenge ...
In MEAN TIME TO INSIGHT Episode 14, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud network observability...
While companies adopt AI at a record pace, they also face the challenge of finding a smart and scalable way to manage its rapidly growing costs. This requires balancing the massive possibilities inherent in AI with the need to control cloud costs, aim for long-term profitability and optimize spending ...
Telecommunications is expanding at an unprecedented pace ... But progress brings complexity. As WanAware's 2025 Telecom Observability Benchmark Report reveals, many operators are discovering that modernization requires more than physical build outs and CapEx — it also demands the tools and insights to manage, secure, and optimize this fast-growing infrastructure in real time ...
As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...
Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...