Azure Databricks simplifies big data analytics and machine learning with managed Spark clusters on Azure. It scales dynamically, optimizing resource usage and costs. Integrated with Azure services, it supports high-performance data processing.
This video shows how to leverage Azure Data Factory to directly integrate data from Azure Data Lake Storage Gen2 to Fabric Lakehouse Delta Table
Learn how to enable GitHub integration in the Microsoft Fabric Admin Portal, configure delegate settings, and connect a GitHub account for workspace Git integration and CI/CD workflows.
In this video, I demonstated how to leverage Azure Databricks to read JSON data, create Spark DataFrame and perform filtering.
This video shows dives into Scala as an important languages supported in the Fabric Notebook. Covered in this vide is how to read delta table from Lakehouse in the Fabric Notebook leveraging the scala language.
In this data engineering video, I demonstrated how to recursively traverse multiple CSV files in Azure Data Lake Storage Gen2 using Azure Synapse Notebook and write the data to OneLake in Fabric Lakehouse.
In this end to end video, I covered how to Implement Practical CI/CD in Microsoft Fabric Leveraring Azure DevOps.
This video shows how to use Azure Synapse Analytics to read, combine, and analyze multiple CSV files residents in ADLS Gen2 using Spark SQL.
In this video, I demonstrated how to use the Dataflow Gen2 to connect and append to multiple Excel files and published to the Fabric Warehouse for analysis.
This video shows how to implement Fabric Data Pipeline Triggers & Schedule to seamlessly and continuously ingest CSV files from Azure Blog Storage to Fabric Warehouse without intervention!
This video shows how to integrate Azure Data Factory to git using Azure DevOps.
This video shows how to transfer data between lakehouses from one workspace to another using data pipeline dynamic content.
Understanding Query Folding in Fabric Dataflow Gen2 (Power Query Online)" explores the concept of query folding within Microsoft's advanced data integration tool. Learn how query folding optimizes data transformation processes, enhances performance, and streamlines ETL tasks in Power Query Online.
This video shows how to implement blog event trigger in the azure data factory to perform data movement from source to destination.
In this Data engineering video, I demonstrate how to Move SQL Server Database Tables to Azure SQL using Fabric Data Pipeline.
Discover seamless Azure integration in the Microsoft Fabric Admin Portal. Effortlessly manage cloud connections, network configurations, and infrastructure deployments. Simplify your workflow with intuitive tools for Azure connectivity and administration.
This video shows how to use Azure Data Factory Data Pipeline to Ingest Data from ADLS Gen 2 to Azure Cosmos DB.
This video shows how to Orchestrate BigQuery Data to Fabric Lakehouse using Data Pipeline for analysis in Power BI.
In this video, we continue in our data engineering videos on how to use Azure Data Factory to ETL data from Google Cloud Storage account to Azure Data Lake Storage Gen2.
In this Data Engineering video, I walked you through how to create GCP Cloud Storage, upload file into the bucket, create fabric lakehouse and shortcut to the GCP data.
In this end-to-end Data Engineering and Analytics project, I walked you through how to migrate 34m of records from on-prems SQL Server to Fabric Data Warehouse using Fabric Data Pipeline.
This video shows how to create Azure ADLS Gen 2, create container, upload files and create a shortcut in Microsoft Fabric Lakehouse for downstream analytics.
This video shows how to ingest data into Azure SQL Database using SQL Server Management Studio.
In this video, I walk you through how to get started with Snowflake Database, create warehouse, database, tables and records and mirrored the database in Fabric Database for downstream analytics. If you like the video, share with your friends and connections and give it a thumps up!
Explore seamless data integration from Fabric Lakehouse to Snowflake Database using a robust data pipeline. This process involves extracting, transforming, and loading data efficiently, ensuring smooth migration and synchronization between the two platforms.
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