Azure Data Engineering Training | Hyderabad

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Databricks Powerbi
Upsert Data Load Mode
Mobile No 91-9989971070
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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.
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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.
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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.
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- 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.
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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.
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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.
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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.
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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_at_visualpath.in
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THANK YOU
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