Table of Contents
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Tag: Cloud Data Engineering
The Complete Guide To Data Pipelines With Architecture, Types and Use Cases
In the modern enterprise, data isn’t just an asset, it’s the lifeblood of decision-making. But raw data is like crude oil – it holds immense potential but is unusable in its natural state. It must be extracted, refined and transported to where it can power the business. This is the fundamental role of a data pipeline. For any leader looking to build a truly data-driven organization, understanding and investing in robust data pipeline architecture is not an IT expense, it’s a strategic imperative.
This guide moves beyond the technical jargon to explore why data pipelines are the bedrock of business agility, how to build them effectively and the tangible outcomes they deliver. Continue reading “The Complete Guide To Data Pipelines With Architecture, Types and Use Cases “
Building a Reliable Cloud Data Storage Architecture for Big Data
Introduction
As businesses continue to generate large amounts of data every day, it has become essential to establish a reliable cloud data storage architecture. Whether you’re working with analytics workloads, IoT data, or datasets for AI training, a thoughtfully designed cloud storage setup guarantees scalability, availability, and high performance while keeping costs and security under control.
In this guide, we will discuss designing a cloud data storage architecture suitable for big data, its components, best practices, and cutting-edge technologies that are fueling data-driven innovation. Continue reading “Building a Reliable Cloud Data Storage Architecture for Big Data”
The Ultimate Guide to Cloud Data Engineering with Azure, ADF, and Databricks
Introduction
In today’s data-driven world, organisations are constantly seeking better ways to collect, process, transform, and analyse vast volumes of data. The combination of Databricks, Azure Data Factory (ADF), and Microsoft Azure provides a powerful ecosystem to address modern data engineering challenges. This blog explores the core components and capabilities of these technologies while diving deeper into key technical considerations, including schema evolution using Delta Lake in Databricks, integration with Synapse Analytics, and schema drift handling in ADF. Continue reading “The Ultimate Guide to Cloud Data Engineering with Azure, ADF, and Databricks”
The Ultimate Guide to Postgres MCP for Claude Desktop
Connecting PostgreSQL to a cloud desktop via the Model Completion Protocol (MCP) opens up new possibilities for using the powerful features of the cloud with your database information.
Introduction
If you’ve ever been stuck waiting for your data team to run a quick SQL query or worse, had to wrestle with writing your own you already know how frustrating traditional analytics workflows can be. But what if you could simply ask your database questions in plain English and get instant insights?
That’s exactly what Postgres MCP (Model Context Protocol for PostgreSQL) brings to the table when integrated with Claude Desktop. This setup not only saves valuable time but also ensures your database remains secure while supercharging team productivity.
In this post, we’ll break down what Postgres MCP is, why it matters, real-world applications, and a step-by-step guide to setting it up. Continue reading “The Ultimate Guide to Postgres MCP for Claude Desktop”