In a world where 2.5 quintillion bytes of data are created every day, it’s not surprising that organizations want to harness the power of being data-driven. In our 2022 Data Health Barometer, 99% of companies surveyed recognized that data is crucial for success — but 97% said they face challenges in using data effectively. Perhaps in response to those challenges, 65% of companies reported that they'd started a data literacy program.

Learn more about the similarities and differences between code coverage and test coverage, and how they help you create better software.

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.

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.

Fivetran partners with Databricks on their launch of Lakehouse for Manufacturing (L4M)

Good data is the foundation of any successful business. Forecasting, planning, or tracking a company’s long-term performance is difficult without good data. Real-time information provided by data supports decision-making and helps save time and money. So, it becomes essential to test the data better to draw accurate insights.

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.

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.

Generative AI and large language models (LLMs) are revolutionizing many aspects of both developer and non-coder productivity with automation of repetitive tasks and fast generation of insights from large amounts of data. Snowflake users are already taking advantage of LLMs to build really cool apps with integrations to web-hosted LLM APIs using external functions, and using Streamlit as an interactive front end for LLM-powered apps such as AI plagiarism detection, AI assistant, and MathGPT.

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.