At a time when AI is exploding in popularity and finding its way into nearly every facet of business operations, data has arguably never been more valuable. More recently, that value has been made clear by the emergence of AI-powered technologies like generative AI (GenAI) and the use of Large Language Models (LLMs).

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Organizations are building data lakes and bringing data together from many systems in raw format into these data lakes, hoping to process and extract differentiated value out of this data. However, if you’re trying to get value out of operational data, whether on prem or in the cloud, there are inherent risks and costs associated with moving data from one environment to another.

As organizations seek to drive more value from their data, observability plays a vital role in ensuring the performance, security and reliability of applications and pipelines while helping to reduce costs. At Snowflake, we aim to provide developers and engineers with the best possible observability experience to monitor and manage their Snowflake environment. One of our partners in this area is Observe, which offers a SaaS observability product that is built and operated on the Data Cloud.

Nowadays, developers prefer a one-stop solution for web development where developers can build their entire web application on the go. Full-stack development platforms such as MEVN, MEAN, and MERN are the contemporary solutions for building an end-to-end application. Developers can create every part of the applications in the same language for every aspect - including a front-end, backend, database, API, security, web server, and more.

Today, enterprises are focused on enhancing decision-making with the power of AI and machine learning (ML). But the complexity of ML models and data science techniques often leaves behind organizations without data scientists or with limited data science resources. And for those organizations with strong data analyst resources, complex ML models and frameworks may seem overwhelming, potentially preventing them from driving faster, higher-quality insights.

Microfrontends is a modern front-end architectural approach, by which web applications are segmented (or decomposed) into smaller self-contained units, allowing for easier management and scalability. You can our introduction article to Microfrontends to understand better this architecture approach. Today we’re going to provide a step-by-step guide to the process of building a real-world application using React and Module Federation.

Perhaps it's the awe-inspiring films about space exploration (my personal favorite – Apollo 13) that evoke the image of NASA as a place buzzing with activity, filled with screens displaying data, charts, and ALWAYS a big countdown clock. However, one of NASA's most recent challenges may surprise you - the task of cracking open a billion-dollar canister filled with ancient asteroid dust.

Just shy of a year ago and coinciding with the Atlassian Team ‘23 conference, Testlio unveiled an initiative to help product teams adopt signal-driven testing as a core pillar of the future of software quality engineering. A lot of exciting things have happened and continue to happen since that announcement, which collectively serves as validation of the opportunity for product teams to dramatically improve test coverage efficiency through signal-driven testing.