DevOps and DevSecOps have gained more attention in recent years in the world of software development. While both of these methodologies emphasize the agile development process and team collaboration, there are some key differences that distinguish them. Understanding these distinctions is critical for software development teams and organizations to determine which methodology is best suited to their requirements. In this article, we’ll learn about the difference between DevOps and DevSecOps.

In software development, GitOps and DevOps are prominent techniques for enhancing collaboration and automating software delivery processes. While these two models aim to improve the efficiency of the software development life cycle, they differ in their core principles and implementation.

This post was written by Talha Khalid, a full-stack developer and data scientist who loves to make the cold and hard topics exciting and easy to understand. No one has any doubt that microservices architecture has already proven to be efficient. However, implementing security, particularly in an immutable infrastructure context, has been quite the challenge.

This post was written by Siddhant Varma. Scroll down to read the author’s bio. Software development isn’t just about building and deploying software. There’s a wide range of operations and activities you need to tackle even after you’ve successfully deployed it. The two most common are observability and monitoring. While they’re similar in a lot of ways, it’s important to understand that they are not exactly the same, and each has its own purpose.

Since 2020, the Intel team has been contributing to Telegraf, including both telemetry from Intel-specific platform features (such as Intel® Resource Director Technology, Intel® Dynamic Load Balancer, or power statistics from Intel-based platforms) and telemetry gathered from generic tools and frameworks; for example, Data Plane Development Kit (DPDK), Libvirt, P4 Runtime, or Reliability Availability Serviceability (RAS).

As more companies rely on data insights to drive critical business decisions, data must be accurate, reliable, and of high quality. Gaining insights from data is essential, but so is the data’s integrity so that you can be sure that data isn’t missing, incorrectly added, or misused. This is where data observability comes in.

This post was written by Joe Cozzupoli. Scroll down to read the author’s bio. As the cybersecurity landscape evolves and threats become more sophisticated, organizations need to stay ahead with the right tools and strategies to protect their valuable data. Two key technologies in this domain are Security Orchestration, Automation, and Response (SOAR) and Security Information and Event Management (SIEM).

Users with real-time and other analytic workloads want or need to keep large volumes of historical data to aid in important activities, such as ad hoc historical trend analysis and training AI models. However, storing this much data in a way that also makes it easily queryable becomes prohibitively expensive. As a result, users must balance data availability and usability with sacrificing data fidelity and storage costs. That is until now.

If we turn the clocks back to September 2013, we released InfluxQL alongside InfluxDB. InfluxQL is a SQL-like query language, specifically designed to query time series data. For many of our users, InfluxQL still remains the primary way they interact with InfluxDB. Based on this feedback, InfluxQL has been reborn in InfluxDB 3.0 alongside native support for the SQL query language. So what do I mean by reborn?

We recently introduced a new version of InfluxDB, rewritten from the ground up to improve performance across the board. As with any undertaking of this nature, developers will need to make some adjustments to their applications in order to incorporate the new database. We even faced this challenge internally. We had many Telegraf instances sending data to legacy versions of InfluxDB.