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Explore Apache Kafka Tiered Storage

Get an introduction to Apache Kafka® Tiered Storage which enables more effective data management by utilizing two different storage types—local disk and remote cloud storage solutions such as Amazon S3, Google Cloud Storage, and Azure Blob Storage. This feature offers a tailored approach to data storage, allowing you to allocate frequently accessed data to high-speed local disks while offloading less critical or infrequently accessed data to more cost-effective remote storage solutions. Tiered storage enables you to indefinitely store data on specific topics without running out of space. Once enabled, it is configured per topic, giving you granular control over data storage needs.

Opening a Company Abroad in the Tech Age? Here's How to Do It

In today's interconnected world, the prospect of opening a company abroad is more enticing than ever. The tech age has ushered in a new era of opportunities for entrepreneurs looking to expand their ventures beyond domestic borders. However, the process is not without its complexities. From legal considerations to cultural nuances, successfully establishing a business in a foreign country requires careful planning and execution. In this comprehensive guide, we'll delve into the intricacies of venturing into the international business arena, offering practical insights and step-by-step guidance for those looking to make their mark on the global stage.

How to monitor a mobile App's store ratings with Sematext

Launching a mobile App is no walk in the park, especially when it comes to your App’s store position and ratings. One of the trickiest parts is maintaining a good App store rating. An App’s store ratings are the key to its success. If you’re serious about your App succeeding in an oversaturated market, it is vital to implement strategies to both increase and maintain your App’s store ratings.

How to Manage IoT Device Metrics Using Telegraf and MetricFire

Monitoring your IoT devices provides insights into their usage patterns, environmental conditions, and user behaviors. This information can be leveraged to optimize device performance, improve user experiences, and develop better-targeted services or products. By analyzing patterns and trends in device data, it's possible to implement predictive maintenance strategies, scheduling repairs or replacements before a device completely fails, which will reduce downtime and maintenance costs.

Tech Horizons: A Practical Guide for Emerging Data Developers

It's no surprise that the world is becoming increasingly driven by data. We rely on it more and more each day to power our decisions, inform policy, and structure our lives. With the rise of emerging technologies such as machine learning, artificial intelligence, big data analytics, blockchain-based systems, and many other toolsets - navigating this brave new world can seem intimidating or even downright impossible for aspiring tech developers.

Mastering Integration: A Comprehensive Guide on How to Integrate with ChatGPT

ChatGPT, with its advanced natural language processing capabilities, has opened up a new realm of possibilities for enhancing digital services and user experiences. From automating customer service to providing interactive user interfaces, the applications of ChatGPT are vast and varied. Understanding how to integrate with ChatGPT is crucial for businesses and developers looking to leverage this technology effectively.

The real costs of Datadog Synthetics monitoring

How much do Synthetics matter to your team? I think they matter a whole lot. Back when I was a freelance developer, I doubled my annual income with synthetics. Working mainly in database optimizations, I would finish out a contract and leave a synthetic monitor running at a very low frequency on their service. When I saw a pattern of slower performance, I knew it was time to hit the team lead-up to ask if I could help.

Ship First, Model Later: A Short Recap of AI.Dev

In a keynote at AI.Dev, Robert Nishihara (CEO, Anyscale) described the shift: A year ago, the people working with ML models were ML experts. Now, they’re developers. A year ago, the process was to experiment with building a model, then put a product on top of it. Now, it’s ship a product, find the market fit, then create customized models. The general-purpose generative AI models available to all of us today (such as ChatGPT) change the way work is done.