Systems | Development | Analytics | API | Testing

eBPF: The future of the service mesh and network innovation

The conversations around eBPF and how this technology will shape the future of the service mesh caused a huge buzz in the last year — yes, bee pun intended. eBPF lets you run sandboxed programs in an operating system kernel. Imagining how eBPF could improve the service mesh brings exciting possibilities, but it also raises security and operational concerns given the current state and limitations of eBPF.

Managing technical debt: How to go from 12 BI tools to 1

CIOs are fed up with having a plethora of BI and analytics tools with business units seemingly chasing the shiniest new solution. And although most industry surveys show data and analytics budgets continuing to grow despite a faltering economy, there is closer scrutiny and belt tightening to rid teams of overlapping capabilities. Here’s a look at how BI tool portfolios have become such a mess and how to streamline them.

Why You Should Move From Management Reporter to Jet Reports

Much like Apple people tend to be all Apple, all the time, Microsoft Dynamics ERP users tend to prefer Microsoft products for all their computing needs. It’s not hard to understand why. Using products from the same ecosystem prevents compatibility issues and saves time in learning multiple systems.

Top 10 Data Extraction Tools for 2023

A data extraction tool can help improve the accuracy of data by automating the extraction process and reducing the risk of human error. This can lead to more reliable and consistent data that can be used to make better business decisions. Moreover, data extraction tools can help you increase productivity and improve the quality of your data as they automate the process of retrieving data from multiple sources.

Announcing The NodeSource-GitHub Partnership

NodeSource, a leader in Node.js application management, monitoring, and security, is excited to announce our partnership as a launch partner for Deployment Protection Rules with GitHub Actions, the world's largest software development platform, to integrate Node Certified Modules (NCM) directly into the GitHub Marketplace.

Reach for the Clouds: A Crawl/Walk/Run Strategy with Kong and AWS - Part 1: Crawl

Reach for the Clouds: A Crawl/Walk/Run Strategy with Kong and AWS - Part 1: Crawl Brought to you by @KongInc Senior Partner Developer Danny Freese Welcome to the "Crawl" stage of our Cloud Migration Journey! In this tutorial, we will show you how to de-risk and lift-and-shift your connections using Kong Mesh and #Konnect during a migration to the cloud. The "Crawl" stage is the first phase of our 3-phased approach, and it aims to deploy the monolith and Konnect runtime instance, and onboard the monolith to Konnect.

Reach for the Clouds: A Crawl/Walk/Run Strategy with Kong and AWS - Part 2: Walk

Reach for the Clouds: A Crawl/Walk/Run Strategy with Kong and AWS - Part 2: Walk Brought to you by @KongInc Senior Partner Developer Danny Freese Welcome to the "Walk" stage of the Cloud Migration Journey, where we will take you through the second phase of the migration process. In this stage, we will show you how to de-risk and lift-and-shift your connections during the migration process to the cloud using Konnect and Kong Mesh.

Reach for the Clouds: A Crawl/Walk/Run Strategy with Kong and AWS - Part 3: Run

Reach for the Clouds: A Crawl/Walk/Run Strategy with Kong and AWS - Part 2: Walk Brought to you by @KongInc Senior Partner Developer Danny Freese In this video, we'll guide you through the "Run" stage of the cloud migration journey, the final step of our crawl-walk-run tutorial. By this point, you should have already deployed the monolith and Konnect runtime instance, onboarded the monolith to Konnect, deployed the Kong Mesh control plane and the on-prem mesh zone, and reconfigured the Konnect runtime-instance so that runtime-instance monolith communication occurs over the mesh.

The Best Big Data Tools in 2023

Data engineers who work with huge amounts of data know that “big data” is not just an overhyped term. When the volumes of data get into petabytes the best data engineering tools start to break down. This is when you need devoted big data technologies that are fault-tolerant, scalable, and offer high performance even when amounts of data test the limits of your data platform. This article won’t be just another listicle. Instead, we’ll showcase the best big data tools by use case.