Datadog announces integration with Microsoft Azure DevOps to derive metrics from their continuous integration pipelines

Datadog announced Thursday a new integration with Microsoft Azure DevOps. Users can pull in events, derive metrics from their continuous integration pipelines, and correlate this information with real-time data from their entire stack in Datadog. 

In addition, teams can set Datadog monitors as gates in their Azure DevOps deployment pipelines, enabling them to detect and abandon bad releases automatically before users are impacted.

Companies are increasingly adopting continuous integration and continuous deployment best-practices to improve the speed and agility of their developer operations. As part of this, many teams rely on services like Azure DevOps in order to release more frequent, smaller updates to their code. 

As updates proliferate, monitoring this activity in context with application performance data becomes critical. Without this visibility, determining a root cause and fixing bugs in dynamic environments can be very time consuming, resulting in extended outages.

In addition to troubleshooting deployments, managers can also use this integration to understand the efficiency of their DevOps processes. Datadog generates metrics to help track Azure DevOps events, so users can see how often builds are completing or failing, the frequency of code commits, and the duration of work items. This data provides feedback for teams to track and improve their workflows as they implement agile methodology.

“We’re excited that users will no longer need to jump between tools to monitor how deployments might be impacting their applications,” said Steve Harrington, product manager at Datadog. “With the ability to stop Azure DevOps deployments automatically based on issues detected in Datadog, this integration provides a powerful tool for mitigating or preventing downtime altogether.”

New Matillion Data Loader release delivers speed and simplicity of data integration

Matillion announced Matillion Data Loader, a free Software-as-a-Service (SaaS) data integration solution that empowers data analytics professionals and business users to load and migrate data with a powerful and scalable product.

Matillion products reduce time to value for cloud data analytics projects with data integration and data transformation solutions for Amazon Redshift, Snowflake and Google BigQuery.

With Matillion Data Loader, data professionals can use a low-friction solution to replicate data from data sources into a CDW using a code-free, wizard-based pipeline builder; monitor jobs with a dashboard view of pipeline runs and data volumes; save on infrastructure, resources, and DevOps costs, using technology built to leverage the speed and scale of the cloud; and upgrade to handle advanced data transformation, integration, and orchestration needs with Matillion's top-rated ETL solution

"Exploding data volumes and the rapid shift toward data democratization mean enterprises have increasingly diverse data analytics needs across teams. A flexible data analytics platform should ensure companies can address simple use cases, sophisticated analytics projects, or both at the same time, without the inherent complexity of other tools," said Matthew Scullion, CEO of Matillion. "Matillion Data Loader works by itself to deliver low-friction data loading but also works with Matillion ETL as a platform to join and transform data sources for analytics. This is a far better option than having to choose between solutions that are either quick and simple or functional and complex."

Codefresh debuts CI/CD Live Debugger that make DevOps platform more responsive

Codefresh launched its live CI/CD debugging tool to take the suck out of DevOps, as creation of CI/CD pipelines is considered a frustrating and time-consuming process. Each change has to be saved, committed and the pipeline rerun from the beginning. DevOps engineers are missing a key component that has been available for software developers for years -debugging breakpoints.

The Codefresh pipeline debugger works similar to an integrated development environment (IDE) debugger. Developers can place breakpoints on one or more pipeline steps and once the pipeline hits one of them, it will pause and a debugging window will open. Users can then test commands, explore the state of the pipeline, and identify required changes.

“The initial feedback from our users piloting live debugging has been very positive. They love how much faster and easier the CI/CD pipeline creation process is when engineers are able to test, troubleshoot, and fix their pipelines with this new tool,” said Raziel Tabib, founder and CEO of Codefresh.

With Codefresh, users can run their pipelines locally and see them executing on the workstation. This helps a lot when it comes to monitoring a pipeline and allows them to run the pipeline in a fully controlled context, such as a laptop.

As more customers are adopting Codefresh, the company wants to help them debug their pipelines in a more user-friendly manner. While most CI solutions offer just a simple SSH connection to the builder node, Codefresh wanted to take pipeline debugging to the next level and allow them to debug the Codefresh pipelines using the breakpoints.

When debugging an application, users can place breakpoints in one or more positions in the source code, run the application in debug mode and inspect its state when it hits any of the breakpoints.