Hashicorp Vault is a tool for managing secrets—sensitive data such as passwords, certificates, and API keys. Vault allows you to encrypt your secrets, control access to them, and audit activity to see who has requested data from your Vault. Datadog already monitors the status of your Vault servers—for example, you can configure the Vault integration to automatically notify you if a Vault server is unexpectedly sealed, or if there is a leader change in your Vault cluster.

ClickHouse is an open source database management system, and was originally developed as a backend for Yandex’s Metrica analytics platform. ClickHouse is column oriented, meaning that it can quickly scan through ranges of values in a single column without touching irrelevant values in other columns. This makes ClickHouse well suited for online analytical processing (OLAP).

AWS Lambda is a compute service that enables you to build serverless applications without the need to provision or maintain infrastructure resources (e.g., server capacity, network, security patches). AWS Lambda is event driven, meaning it triggers in response to events from other services, such as API calls from Amazon API Gateway or changes to a DynamoDB table.

In Part 1 of this series, we discussed AWS Lambda functions and some key metrics for monitoring them. In this post, we’ll look at using Amazon’s native tooling to query those metrics. We’ll also show you how to collect logs and traces that provide further visibility into your Lambda functions. Amazon provides built-in monitoring functionality through CloudWatch and X-Ray.

In Part 2 of this series, we looked at how Amazon’s built-in monitoring services can help you get insights into all of your AWS Lambda functions. In this post, we’ll show you how to use Datadog to monitor all of the metrics emitted by Lambda, as well as function logs and performance data, to get a complete picture of your serverless applications. In this post, we will: Datadog integrates with AWS Lambda and other services such as Amazon API Gateway, S3, and DynamoDB.

AWS Lambda decouples the need to provision and maintain a runtime environment from running code, allowing developers to focus on applications rather than infrastructure. But, by abstracting away the underlying infrastructure of an application, serverless architectures introduce new challenges into monitoring and observability.

Since AWS Lambda was launched in 2014, serverless has transformed the way applications are built, deployed, and managed. By abstracting away the underlying infrastructure, developers are able to shift operational responsibilities to the cloud provider and focus on solving customer problems.

When an application in a Docker container emits logs, they are sent to the application’s stdout and stderr output streams. The container’s logging driver can access these streams and send the logs to a file, a log collector running on the host, or a log management service endpoint. In this post, we’ll explain how the driver you choose—and how you configure it—influences the performance of your containerized application and the reliability of your Docker logging.

To complement distributed tracing, runtime metrics, log analytics, synthetic testing, and real user monitoring, we’ve made another addition to the application developer’s toolkit to make troubleshooting performance issues even faster and simpler. Today, we’re excited to introduce Profiling—an always-on, production profiler that enables you to continuously analyze code-level performance across your entire environment, with minimal overhead.

Amazon Managed Streaming for Apache Kafka (MSK) is a fully managed service that allows developers to build highly available and scalable applications on Kafka. In addition to enabling developers to migrate their existing Kafka applications to AWS, Amazon MSK handles the provisioning and maintenance of Kafka and ZooKeeper nodes and automatically replicates data across multiple availability zones for high availability.