Azure Databricks Training | Data Engineering Training Hyderabad

1. Databricks & Powerbi Upsert Data Load Mode Mobile No: +91-9989971070 Visit: www.visualpath.in

2. Sure, let's break down the process of Azure Data Engineering with Databricks and Power BI for On-Premise Data Loads and Upsert operations. 1. Azure Data Factory (ADF): - ADF is a cloud-based data integration service that allows you to create data-driven workflows for orchestrating and automating data movement and data transformation. - Set up Azure Data Factory pipelines to connect to your on-premise data sources and ingest data into your Azure environment. www.visualpath.in

3. 2. Azure Databricks: - Databricks is an Apache Spark-based analytics platform optimized for Azure. - It provides a collaborative environment for data engineers, data scientists, and business analysts. - Use Databricks to process and transform data as it's ingested into Azure. You can use PySpark or Scala to write ETL (Extract, Transform, Load) jobs. - Utilize Databricks notebooks for collaborative development and testing of your ETL processes. www.visualpath.in

4. 3. Data Storage: - Store your transformed data in Azure Data Lake Storage (ADLS) or Azure Blob Storage. These are scalable and cost-effective storage options that integrate well with Databricks. 4. Azure Synapse Analytics (formerly SQL Data Warehouse): - Use Azure Synapse Analytics for data warehousing and analysis. It allows you to query large volumes of data with T-SQL queries. 5. Power BI: - Power BI is a business analytics service that enables you to visualize and share insights across your organization. www.visualpath.in

5. - Connect Power BI to Azure Synapse Analytics to create reports and dashboards based on the processed and transformed data. 6. On-Premise Data Loads: - For on-premise data loads, ensure that you have a secure and reliable connection between your on-premise data sources and Azure. - This might involve setting up a VPN or using Azure ExpressRoute. - Configure ADF to pull data from on-premise sources and load it into Azure Data Lake or Blob Storage. www.visualpath.in

6. 7. Upsert Operations: - Upsert operations involve updating existing records and inserting new records if they don't exist. - Depending on your data storage solution, implement upsert operations using appropriate techniques. - For example, in Azure Synapse Analytics, you might use T-SQL MERGE statements. 8. Monitoring and Logging: - Set up monitoring and logging for your data pipelines. Azure Monitor and Azure Log Analytics can help you track the performance and health of your data engineering workflows. www.visualpath.in

7. 9. Automation and Scheduling: - Use Azure Data Factory to schedule and automate your data workflows. You can set up triggers to run pipelines based on events or time schedules. 10. Security: - Implement appropriate security measures for your data, including Azure Active Directory authentication, role-based access control (RBAC), and encryption. www.visualpath.in

8. Conclusion: Remember to follow best practices for each service, and regularly monitor and optimize your pipelines for performance and cost efficiency. Additionally, stay updated with the latest features and updates from Azure and Power BI for any improvements or new capabilities that may enhance your data engineering processes. www.visualpath.in

9. Contact For More Details About Azure Databricks Training Address:- Flat no: 205, 2nd Floor, Nilgiri Block, Aditya Enclave, Ameerpet, Hyderabad-1 Ph. No: +91-9989971070 Visit: www.visualpath.in E-Mail:online@visualpath.in

10. THANK YOU www.visualpath.in