Amazon Security Lake is a new service from Amazon Web Services (AWS) that is designed to help organizations improve their security posture by automating the collection, normalization, and consolidation of security-related log and event data from integrated AWS services and third-party services (Source Partners). By centralizing all the security data in a single location, organizations can gain greater visibility and identify potential threats more quickly.

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.

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.

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.

As fraud continues to grow in prevalence, SecOps teams are increasingly investing in fraud prevention capabilities to protect themselves and their customers. One approach that’s proved reliable is the use of log analytics and telemetry data for fraud prevention. By collecting and analyzing data from various sources, including server logs, network traffic, and user behavior, enterprise SecOps teams can identify patterns and anomalies in real time that may indicate fraudulent activity.

DevOps has become the dominant software development and deployment methodology over the past decade. In Atlassian’s DevOps Trends Survey, over half of the respondents said that their organizations had a dedicated DevOps team and 99% of respondents indicated that DevOps had a positive impact on their organization. In addition to DevOps teams, many have implemented Platform Engineering as a discipline, or designing technology platforms as a foundation for developers to build and deploy applications.

Product-led growth (PLG) is on the rise, a discipline that relies on the product itself to drive user acquisition, expansion, conversion and retention. Today, 60% of Cloud 100 companies embrace a PLG strategy, because it’s an efficient method of growth with low customer acquisition costs. Plus, cloud-native companies have the unique opportunity to collect more data that shows exactly how customers are using their products, and where potential friction occurs.

Cyber threats are becoming more sophisticated, and enterprise security teams are under constant pressure to improve and enhance their threat detection and response capabilities. But as security teams expand their security logging tools and capabilities, the burden of monitoring those tools and investigating alerts grows exponentially.

ChaosSearch was built for live analytics at scale on cloud storage. Our architecture was designed for high volume ingestion of streams & analytics at scale via ElasticSearch & Trino API via a stateless fabric that can scale to meet the customers’ scale & latency requirements. Because we don’t store any data, under the hood, ChaosSearch is basically a set of containers that are deployed in cloud compute instances in a dedicated VPC to each customer managed by ChaosSearch.