In Microsoft Fabric, raw data from multiple sources flows into the OneLake environment. But raw data isn’t always ready for analytics. It needs to be cleaned, reshaped, and enriched before it powers business intelligence, AI, or advanced analytics. That’s where Dataflows Gen2 come in. They let you prepare and transform data at scale inside Fabric, without needing heavy coding, while still integrating tightly with other Fabric workloads.
OneLake is Microsoft Fabric’s built-in data lake, designed to store data in open formats like Delta Parquet and make it instantly available to all Fabric experiences (Lakehouse, Data Factory, Power BI, Real-Time Analytics).
In a world where organizations rely on dozens of apps, platforms, and databases, one thing remains true: fragmented data leads to flawed decisions. That’s why top-performing enterprises are investing in Golden Records: the trusted, unified versions of critical business entities like customers, products, and vendors.
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.
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.
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.
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 good Data Analyst should combine technical expertise, strong analytical skills, business acumen, and effective communication to deliver valuable insights and drive data-informed decision-making within an organization.
Azure SQL DB is a fully managed relational database service, Azure Dedicated SQL pool is a distributed analytics service for large datasets, and Elastic Pool is a resource allocation model for managing multiple SQL databases with varying workloads.
The best BI tool for creating reports and dashboards will depend on your specific needs and requirements. It is important to evaluate each tool carefully to determine which one will best meet your needs.
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