In 2009, as the world became increasingly data-driven, organizations began to accumulate vast amounts of data — a period that would later be characterized as the Big Data revolution. While most organizations were used to handling well-structured data in relational databases, this new data was appearing more and more frequently in semi-structured and unstructured data formats.

With its simplicity, flexibility, and cost-efficient characteristics, Amazon Simple Storage Service (Amazon S3) cloud object storage has become the preferred platform for collecting, analyzing, and retaining today’s growing mountain of diverse and disjointed enterprise data. And as Amazon Web Services (AWS) continues to grab market share in the hyperscale IaaS/PaaS/SaaS marketplace, organizations of every size are leveraging Amazon S3 to underpin a variety of use cases, such as.

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).

JavaScript Object Notation (JSON) has become the de facto standard format for writing cloud infrastructure and web application log data - and for good reason. Structured logging with JSON reduces the cost and complexity of extracting valuable insights from log data.

ELK has become one of the most popular log analytics solutions for software-driven businesses, with thousands of organizations relying on ELK for log analysis and management in 2021. In this ultimate guide to using ELK for log management and analytics, we’re providing insights and information that will help you know what to expect when deploying, configuring, and operating an ELK stack for your organization. Keep reading to discover answers to the following.

Data warehouses and data lakes represent two of the leading solutions for enterprise data management in 2023. While data warehouses and data lakes may share some overlapping features and use cases, there are fundamental differences in the data management philosophies, design characteristics, and ideal use conditions for each of these technologies.

Is your organization currently relying on an ELK cluster for log analytics in the cloud? While the ELK stack delivers on its major promises, it isn’t the only search and analytics engine — and may not even be your best option for log management. As cloud data volumes grow, ELK monitoring can become too costly and complex to manage.

The success of businesses like Zoom, DropBox, and Slack demonstrates the power of product-led growth (PLG) as a strategy for scaling software companies in 2023. Central to this approach is event analytics, the practice of analyzing event data from a software product to unlock data-driven insights. Companies following a PLG strategy (“PLG companies”) use this data to inform product development decisions to enhance user experiences and drive revenue.

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.

Organizations are leveraging log analytics in the cloud for a variety of use cases, including application performance monitoring, troubleshooting cloud services, user behavior analysis, security operations and threat hunting, forensic network investigation, and supporting regulatory compliance initiatives. But with enterprise data growing at astronomical rates, organizations are finding it increasingly costly, complex, and time-consuming to capture, securely store, and efficiently analyze their log data.