Data Engineering on Microsoft Azure Certification Training Course Singapore.

Data Engineering on Microsoft Azure Course Overview

Today, everything depends on thorough data analysis to understand the customer pain points and also to identify new opportunities to gain market share. In such a challenging business landscape, it is critical for individuals and enterprises to know how to integrate, transform, and consolidate data across platforms. This Data Engineering on Microsoft to Azure certification (DP-203) is one such certification that helps professionals to build some of the best analytics solutions using Microsoft Azure as a platform. Check out the dates below to enroll for the DP-203 certification training course.

Module 1: Explore compute and storage options for data engineering workloads

Understand Azure Synapse Analytics

Describe Azure Databricks

Describe Azure Databricks Delta Lake architecture

Understand Azure Data Lake storage

Work with data streams by using Azure Stream Analytics

Module 2:Run interactive queries using serverless SQL pools

Explore Azure Synapse serverless SQL pools capabilities

Query data in the lake using Azure Synapse serverless SQL pools

Create metadata objects in Azure Synapse serverless SQL pools

Secure data and manage users in Azure Synapse serverless SQL pools

Module 3: Data Exploration and Transformation in Azure Databricks

Describe Azure Databricks

Read and write data in Azure Databricks

Work with DataFrames in Azure Databricks

Work with DataFrames advanced methods in Azure Databricks

Module 4: Explore, transform, and load data into the Data Warehouse using Apache Spark

Understand big data engineering with Apache Spark pools in Azure Synapse Analytics

Ingest data with Apache Spark notebooks in Azure Synapse Analytics

Integrate SQL and Apache Spark pools in Azure Synapse Analytics

Transform data with DataFrames in Apache Spark pools in Azure Synapse Analytics

Monitor and manage data engineering workloads with Apache Spark pools in Azure Synapse Analytics

Module 5: Ingest and load data into the Data Warehouse

Use data loading best practices in Azure Synapse Analytics

Perform Petabyte-scale ingestion with Azure Data Factory or Azure Synapse Pipelines

Module 6: Transform Data with Azure Data Factory or Azure Synapse Pipelines

Perform Data integration with Azure Data Factory or Azure Synapse Pipelines

Perform Code-free transformation at scale with Azure Data Factory or Azure Synapse Pipelines

Module 7: Integrate Data from Notebooks with Azure Data Factory or Azure Synapse Pipelines

Understand how to add an activity to the control flow to orchestrate data from other technologies

Understand how to use parameters in Azure Data Factory/Synapse pipelines

Module 8: End-to-end security with Azure Synapse Analytics

Secure a data warehouse

Configure and manage secrets in Azure Key Vault

Implement compliance controls for sensitive data

Module 9: Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link

At the end of this module, the students will be able to

Design hybrid transactional and analytical processing using Azure Synapse Analytics

Configure Azure Synapse Link with Azure Cosmos DB

Query Azure Cosmos DB with Apache Spark for Azure Synapse Analytics

Query Azure Cosmos DB with SQL Serverless for Azure Synapse Analytics

Module 10: Real-time Stream Processing with Stream Analytics

Work with data streams by using Azure Stream Analytics

Enable reliable messaging for Big Data applications using Azure Event Hubs

Ingest data streams with Azure Stream Analytics

Module 11: Create a Stream Processing Solution with Event Hubs and Azure Databricks

Learn the key features and uses of Structured Streaming

Stream data from a file and write it out to a distributed file system.

Use sliding windows to aggregate over chunks of data rather than all data

Apply watermarking to throw away stale old data that you do not have space to keep.

Connect to Event Hubs read and write streams