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Architecture considerations For Data Engineering

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Architecture considerations For Data Engineering

DP201-Module 01 - Architecture considerations
Lesson 01 – Describe the core principles for creating architectures
Lesson 02 – Design with Security in mind
Lesson 03 – Consider performance and scalability
Lesson 04 – Design for availability and recoverability
Lesson 05 – Design for efficiency and operations

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Instructed by

Multicloud4u Approved Trainer
Azure Solution and Data Architect,Cloud Community Leader

Lokesh (Multicloud4u Certified Trainer)

Lokesh Is a ☁ Cloud Solutions Architect with a hands-on experience in  Azure, AWS, and works At a multinational.

Awards and Certificates:-

Refer his profile here:-

More About Lokesh

lokesh has extensive experience in the following:-

1. Building Cloud Solution For Azure and Multicloud

2. Cloud Migration on Azure and Multicloud

3. Data Modernization with Data Lake, Delta Lake, Big data Tech and Streaming solutions not forgetting the principal in existence with a data warehouse.

4. Data Engineering on Cloud

5. Data Operations using Data Bricks and Data Factory

6. Data Streaming

7. ETL and Extensive Analytics tool Including Synapse

8. Utilizing data with Power BI and API'S

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Who is this Architecture considerations For Data Engineering for?
azure real-time reference architectures data azure real-time reference architectures preyash data cloud cloud data azure cloud microsoft azure
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