Author: Oriol Sr

Documentation plays a crucial role in any project, serving as a compass for development teams, and as a good way for the new joiners to know how the project is done. More often than not, documentation becomes a task that is either indefinitely postponed or completed once and never updated, leaving it outdated and potentially counterproductive.

An effective strategy for developers is to leverage a tool seamlessly integrated into their IDE. This eliminates friction caused by the need to switch between different tools, ensuring convenience, ease of use, and the ability to effortlessly update documentation. An excellent way to approach it is with C4 PlantUML — a powerful tool that allows developers to generate comprehensive system architecture documentation with just a few lines of code, making the documentation process not only efficient but also an integral part of the development workflow.

Change Data Capture (CDC) in SQL Server is a powerful feature designed to track and capture changes made to data within a database. It provides a reliable and efficient way to identify alterations to tables, allowing for the extraction of valuable insights into data modifications over time. By enabling CDC with Azure Data Factory, SQL Server enables a systematic and automated approach to monitoring and capturing changes, facilitating better data management, auditing, and analysis within the database environment.

Most Common Use-Cases: CDC With Azure Data Factory

Common scenarios where the CDC with Azure Data Factory proves beneficial include:

When working with Azure Functions, one common scenario is importing a file for processing. In our specific case, we encounter an Excel file with thousands of lines that need to be processed, checking the accuracy of values using multiple rules and data sources, followed by sending out notifications. Initially, with a small number of lines, the processing time was acceptable. However, as the size of the Excel file grew, reaching tens of thousands of lines, the processing time became unbearable. In order to overcome this challenge, we will explore the capabilities of Durable Functions and leverage their power to address this issue effectively.

Fan-Out/Fan-In Pattern

The “fan-out/fan-in” pattern is a technique used to parallelize and consolidate the execution of multiple tasks. During the fan-out phase, tasks are initiated in parallel to perform different activities concurrently. Then, during the fan-in phase, the function waits for all the parallel tasks to complete and combines their results. This pattern allows for efficient parallel execution, reduces processing time, and enables the consolidation of data or results obtained from parallel tasks. It is a scalable approach that maximizes resource utilization and enhances the overall efficiency of Durable Azure Functions.

Prompt engineering is a vital aspect of leveraging the full potential of AI language models. By refining and optimizing the instructions given to these models, we can obtain more accurate and contextually relevant responses. In this article, we explore the principles and techniques of prompt engineering, along with its limitations and potential applications.

Principles of Prompt Engineering

1. Writing Clear and Specific Instructions

The success of prompt engineering begins with providing clear and unambiguous instructions. Clear doesn’t necessarily mean a short description. Being specific about the desired output helps the model understand the task more accurately. For example, tell the LLA to be an expert in the field you are asking for.