Systems | Development | Analytics | API | Testing

Automate your Databricks to BigQuery migration. Learn how to use Gemini to translate complex SQL code accurately and save hours of manual work.

If you are a developer that is running Cursor as your IDE, you have probably had the ability to experiment with different AI agents in pursuit of productivity. One of the most exciting new offerings is DeepSeek V3 is open-source LLM, with added capabilities for code generation, reasoning, and multi-turn conversations.

Traditional test coverage focuses on code paths and user flows but agentic AI doesn’t follow flows. It reasons, adapts, and improvises. That means your 95% coverage report might look solid while the system still makes unsafe, biased, or unexpected decisions. To test these systems, coverage must evolve: you now measure things like goal alignment, reasoning paths, tool usage patterns, memory accuracy, and failure behavior.

Think about launching a new AI chatbot for the company. After a short period, it is providing customers with inaccurate information about your return policy. Within hours, you receive customer complaints, and the customers are annoyed. Your support team is trying to address the technology-induced chaos caused by the AI chatbot. This is happening far more often than you might think, simply because a large number of businesses skip a proper chatbot evaluation platform before deploying their bot.

Data streaming equips modern organizations to rapidly ingest and understand new information and use it to solve real-world problems at scale. For some of these real-time insights—critical operational cues that demand a timely response—delivering that information directly to your team’s inbox is the best way to act on it.

Real-time AI is transforming how businesses process and use data, demanding faster, more reliable, and scalable infrastructure. Unlike older batch processing systems, real-time AI provides instant insights for applications like fraud detection, personalized recommendations, supply chain adjustments, and predictive maintenance. However, scaling these systems introduces challenges like managing massive data streams, ensuring low latency, and maintaining security.

Android app development is the process of building software for Android devices, such as smartphones, smart TVs, tablets and wearables. It’s a Linux-based system and open source, which means manufacturers can customize it. Android version names used to be named after desserts, like Lollipop, Marshmallow, KitKat and Oreo. From version 10, Google switched to number-based names like Android 10, Android 11, Android 12, etc, up to the most recent, Android 15, which was released in September 2024.

Apache Iceberg is transforming how organizations build and manage their data infrastructure, enabling lakehouse architectures that combine the best of data lakes and data warehouses. In this blog, we look at five real-world implementations demonstrate Iceberg's versatility and the advantages it brings to modern data management challenges. Learn more about Data Lakehouses.

Growing business demands require enterprises to manage a vast number of APIs. The volume and complexity of these integrations make maintaining quality a challenge. To address this, governing the API lifecycle—from design to retirement—has become essential. API management vendors are therefore developing tools and frameworks to assist.

You have probably seen pages where fresh information loads in without a page reload, or some forms that submit without an apparent refresh. What is happening here is that API calls are being made to send and receive information in background. API calls generate seamless and responsive application experiences. In this introductory tutorial, you will learn to examine an API call in the Chrome DevTools; replay it in Python, and sanitise the Flask REST API data for safe and organised input into the database.