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Best Practices for Effective Log Management

Can following log management best practices help organizations with their overall observability, as well as troubleshooting issues and security analytics? Absolutely. In addition, following log management best practices can provide significant competitive advantages when it comes to understanding your users. Centralized log management can help your team accelerate time to insights, and make changes to your applications that improve the user experience.

5 Multi-cloud Data Management Best Practices You Should Follow

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.
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Buyer Beware! Three Challenges with Elasticsearch and OpenSearch

Elasticsearch and OpenSearch are powerful enterprise search and analytics engines that have become popular in the world of data management and telemetry analysis. Their ability to swiftly search, analyze, and visualize data has made them indispensable for businesses and organizations. However, in this blog, we will explore a few key challenges faced by companies using Elasticsearch and OpenSearch, shedding light on important considerations when selecting the right tool for your needs.

A Simplified Guide to Kubernetes Monitoring

The open-source Kubernetes platform has become the de facto standard for deploying, managing, and scaling containerized services and workloads. In fact, 83% of DevOps teams are using Kubernetes to deploy containerized applications in production, taking advantage of its workload orchestration and automation capabilities to optimize the software development process and reduce web server provisioning costs.

5 Elasticsearch Disadvantages You Should Know

Since its initial release in 2010, Elasticsearch has grown into the most popular enterprise search engine with use cases that range from web crawling and website search to application performance monitoring and security log analytics. But despite its widespread adoption and success, Elasticsearch does have some notable disadvantages that you should consider - especially if you’re envisioning a high-scale deployment with a large amount of daily ingestion.

6 Reasons Your Data Lake Isn't Working Out

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.
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SIEM Logging for Enterprise Security Operations and Threat Hunting

Today's enterprise networks are diverse and complex. Rather than the simple network perimeter of old, bad actors can attack through multiple entry points, including cloud-based applications. Not to mention, these networks generate massive amounts of transactional data. Because enterprise networks have become larger, they're more difficult to secure and manage. As a result, IT operations teams and security analysts seek better ways to deal with the massive influx of information to improve security and observability.

OpenSearch vs. Elasticsearch: Which is Better?

Following its release under the open-source Apache 2.0 license in 2010, Elasticsearch rose to prominence as the world’s most popular enterprise search engine. Elasticsearch is frequently deployed alongside Logstash and Kibana, a combination known as the ELK stack, to enable log analytics use cases that include application observability, security log analysis, and understanding user behavior.

The Evolution of Search: How Multi-Modal LLMs Transcend Vector Databases

As we venture deeper into the data-driven era, the traditional systems we have employed to store, search, and analyze data are being challenged by revolutionary advancements in Artificial Intelligence. One such groundbreaking development is the notable advent of Large Language Models (LLMs), specifically those with Multi-Mod[a]l abilities (e.g., Image & Audio).

Can You Use the ELK Stack as a SIEM? A Fresh Take

A SIEM system (Security Information and Event Management) is often used by security operations centers (SOCs) for real-time detection of suspicious activity and security events. While some teams choose to adopt a purpose-built SIEM, others rely on the same DevOps tools they are already using for tasks like troubleshooting and operational log data analysis.