Whether you're a data engineer, analyst, or developer stepping into the world of cloud-based data integration, Azure Data Factory (ADF) is a powerful tool worth mastering. It allows you to build robust, scalable data pipelines to move and transform data from various sources, all without managing infrastructure.
As a data engineer, your job isn’t just about moving data. It’s about doing it reliably, efficiently, and repeatably, especially when working with cloud data platforms like Azure SQL Data Warehouse (Azure Synapse Analytics). Python is one of the best tools to automate workflows, clean data, and interact with Azure SQL DW seamlessly.
If you’ve used Power BI for any amount of time, you know that DAX (Data Analysis Expressions) is both powerful and, at times, puzzling. While it unlocks deep analytical capabilities, it can also slow down your reports if not used wisely.
In a world full of dashboards, reports, and visualizations, simply showing data is not enough. If you want your audience to understand, remember, and act on the insights, you need to go beyond the graph. You need to tell a story. Welcome to the world of storytelling with data, where raw numbers are transformed into compelling, clear, and meaningful narratives that spark decisions.
In today’s data-driven world, dashboards are everywhere; in executive meetings, marketing reviews, sales stand-ups, and operations war rooms. Yet despite their prevalence, most dashboards fail to deliver real value. They’re cluttered, confusing, or just ignored. A high-impact dashboard isn’t just a collection of charts. It’s a powerful decision-making tool. When designed well, it answers questions before they’re asked and guides teams to take confident, timely action.
At its core, a data warehouse (DW) is a centralized repository that consolidates data from across the enterprise, spanning sales, finance, marketing, supply chain, and HR into a clean, structured, and analytics-ready format.
At its core, MDM is the practice of defining, managing, and governing the most critical data entities such as customers, products, suppliers, locations, employees so that the entire organization agrees on a single version of the truth.
This year marks a major milestone for me: 10 years in the data and analytics industry. Over the past decade, I’ve had the privilege to work across various domains, lead talented teams, implement large-scale cloud architectures, and help organizations transform raw data into strategic advantage.
In today’s data-driven world, integrating marketing and sales data from platforms like HubSpot into a centralized storage system such as Azure Data Lake is critical for advanced analytics and reporting. Azure Data Factory (ADF) makes it simple to automate this process without writing any code. In this guide, we'll walk through the exact steps needed to pull data from HubSpot and store it in Azure Data Lake Storage Gen2 in JSON format.
Scaling up and scaling down your Azure Dedicated SQL Pool helps optimize both performance and costs.
With Azure Data Factory, copying files from SharePoint to Data Lake becomes a breeze. By leveraging Azure Data Factory's intuitive interface and robust features, you can easily set up connections to your SharePoint environment and Data Lake Storage Gen2. This allows you to define datasets representing the source (SharePoint) and the sink (Data Lake), specifying the exact locations from which data will be extracted and where it will be stored.
A step-by-step guide on how to send emails to multiple recipients using SparkPost in Azure Data Factory.
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