Operations | Monitoring | ITSM | DevOps | Cloud

Many organizations use Amazon CloudWatch to analyze log data, but find that restrictive CloudWatch log retention issues hold them back from effective troubleshooting and root-cause analysis. As a result, many companies may be looking for effective ways to export CloudWatch logs to S3 automatically. Let's look at some of the reasons why you might want to export CloudWatch logs to S3 in the first place, along with some Amazon-native and open-source tools to help you with the process.

In the rapidly shifting world of financial technology (FinTech), acquiring and retaining new customers to achieve long-term business growth requires a proactive approach to user experience and application performance optimization. As FinTech companies compete against rivals to grow a user base and revolutionize how consumers manage their finances, they increasingly depend on data-driven insights to optimize their mobile applications and deliver exceptional user experiences. This is where application event analytics comes into play.

Many people wonder what the difference is between monitoring vs. observability. While monitoring is simply watching a system, observability means truly understanding a system's state. DevOps teams leverage observability to debug their applications, or troubleshoot the root cause of system issues. Peak visibility is achieved by analyzing the three pillars of observability: Logs, metrics and traces. Depending on who you ask, some use MELT as the four pillars of essential telemetry data (or metrics, events, logs and traces) but we'll stick with the three core pillars for this piece.

Companies who develop software products generate massive quantities of product performance and user engagement data that can be analyzed to support decision-making about everything from feature planning and UX design to sales, marketing, and customer support. Leveraging product data throughout the enterprise represents a significant opportunity to achieve a competitive advantage, but challenges like siloed data systems, poor data literacy, and the complexity of data analytics in the cloud can prevent organizations from making full use of their raw data.

A data lake is a centralized data repository where structured, semi-structured, and unstructured data from a variety of sources can be stored in their raw format. Data lakes help eliminate data silos by acting as a single landing zone for data from multiple sources. But what's the difference between a traditional data lake and a real-time data lake? Some traditional data lakes use batch processing, which involves processing and analyzing a collection of data that has been stored over a specific timeframe. For example, payroll and billing systems that are handled on a weekly or monthly basis might use batch processing.

Access to data is critical for SaaS companies to understand the state of their applications, and how that state affects customer experience. However, most companies use multiple applications, all of which generate their own independent data. This leads to data silos, or a group of raw data that is accessible to one stakeholder or department and not another. Data silos also prevent information from different sources from being blended together to gain a more accurate picture of what's happening in your application.

This year, Amazon Web Services (AWS), a leading cloud services provider, announced a comprehensive security solution called Amazon Security Lake. In this blog post, we will explore what Amazon Security Lake is, how it works, the benefits for organizations, and partners you can leverage alongside it to enhance security analytics and quickly respond to security events.

Since the data lake concept emerged more than a decade ago, data lakes have been pitched as the solution to many of the woes surrounding traditional data management solutions, like databases and data warehouses. Data lakes, we have been told, are more scalable, better able to accommodate widely varying types of data, cheaper to build and so on. Much of that is true, at least theoretically. In reality, the way many organizations go about building data lakes leads to a variety of problems that undercut the value their data lakes are supposed to deliver.

As organizations increasingly adopt serverless architectures and embrace the benefits of microservices, managing logs in this dynamic environment presents unique challenges. In this blog, we're taking a closer look at the differences between serverless and traditional log management, as well as 8 challenges associated with log management for serverless microservices.

Managed Detection and Response (MDR) services occupy an important niche in the cybersecurity industry, supporting SMBs and enterprise organizations with managed security monitoring and threat detection, proactive threat hunting, and incident response capabilities. In this week's blog, we're taking a closer look at the role of MDRs in cybersecurity, the biggest challenges they face, and how integrating ChaosSearch is helping MDRs manage complexity, reduce data retention costs, and enable long-term security analytics use cases that are critical for customer success.