Streaming analytics is an invaluable capability for organizations seeking to extract real-time insights from the log data they continuously generate through applications and cloud services. To help our community get started with streaming analytics on AWS, we published a piece last year called An Overview of Streaming Analytics in AWS for Logging Applications, where we covered all the basics.

In the past, querying a database required Structured Query Language (SQL) skills, or knowledge of other database query languages, such as Kibana Query Language (KQL). Today, with the emergence of generative AI (GenAI), teams can query their analytic database using natural language — and get plain English results in return. Or, if you prefer to still use SQL, many teams use GenAI for database query optimization, making queries faster and more efficient.

Mobile and video games generate huge amounts of data that can be captured and analyzed by game developers to power gaming analytics initiatives and improve games.

Recently at re:Invent, Amazon unveiled S3 Express One Zone for AWS. Express Zone for S3 responds to the demand for faster analytical query speeds, with the convenience of centrally storing all of your application telemetry data in cloud object storage. In the past, for data-intensive applications, data access speeds were slower than desired.

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.