The increase in ransomware attacks and high-profile data breaches over the last few years has reinforced the importance of data security. It should be noted that WannaCry infected more than 300,000 computers worldwide, encrypting sensitive business data and disrupting productivity for an entire week.

We are thrilled to announce Dash 2019, the second year of Datadog’s conference on building and scaling the next generation of applications, infrastructure, and technical teams. This two-day conference will be attended by forward-thinking software developers and operations engineers who are taking the velocity, performance, reliability, and scale of their organizations to the next level.

With continuous integration becoming standard practice, getting full visibility into your CI pipelines has become a key part of monitoring and troubleshooting. Datadog gives you that visibility with out-of-the-box support for several continuous integration tools, including: GitLab, Jenkins, Travis CI, CircleCI and TeamCity. Monitoring your CI servers can help you identify bottlenecks in your pipelines.

During the Entity Extraction For Product Searches talk that Radu Gheorghe and I gave at Activate conference in Montreal last year, we talked about various natural language processing and machine learning algorithms. We showed entity extraction both on top of Solr and using external libraries. In this post we dig into Learning to Rank with Solr Streaming Expressions.

On this episode of Exception Perceptions, Katy Farmer, InfluxData’s Developer Advocate, tells us all about Kubernetes (also known as K8s). Watch the episode, and then accompany Katy on her journey to uncover the “captain” of containerization’s secrets.

I am embracing managed Kubernetes services and here’s my journey. While I attended KubeCon 2018 ready to soak up all I could about Kubernetes and the cloud-native ecosystem, I sought to learn as much as I could to aid me in running my clusters day to day. More importantly, though, I experienced a fundamental shift in what I see as the future of Kubernetes, and what getting started in Kubernetes looks like for companies today.

Forty-three percent of application performance problems occur because of an issue in the application code, according to a DZone study. Code-level issues include bugs in the code constructs, such as long waits, poor iterations, inefficient code algorithms, unhandled exceptions, bad choice of data structures, etc. Developers and application owners need code-level insight, so they can pinpoint issues in the code and fix them before users notice.