Operations | Monitoring | ITSM | DevOps | Cloud

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.

Continuous monitoring is a crucial practice in the fields of DevOps, cybersecurity, and compliance. It involves the proactive and ongoing process of observing, assessing, and collecting data from various systems, applications, and infrastructure components in real-time or near real-time. Continuous monitoring is closely related to observability, which goes beyond simple monitoring to provide a deep understanding of complex and dynamic systems.

A multi-cloud approach helps organizations avoid vendor lock-in, leverage the best available technologies, and reduce costs - but it can also result in added complexity when it comes to centralizing, securing, and analyzing data from cloud applications and services. This blog highlights 5 multi-cloud data management best practices that can help you make the most of your data in multi-cloud environments.

SecOps teams at midsize companies face a unique set of challenges when it comes to managing organizational cybersecurity. Midsize companies (those with 100-999 employees and $50 million-$1 billion in annual revenue, according to Gartner) possess significant financial resources and valuable data that may be targeted by digital adversaries. But, unlike larger enterprise organizations, midsize companies can't always afford to invest heavily in the expensive security tools and dedicated IT security staff needed to prevent cyber attacks.