Prep for Microsoft Azure Data Engineer Associate Cert DP-203
- Offered byCoursera
Prep for Microsoft Azure Data Engineer Associate Cert DP-203 at Coursera Overview
Duration | 2 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Official Website | Explore Free Course |
Credential | Certificate |
Prep for Microsoft Azure Data Engineer Associate Cert DP-203 at Coursera Highlights
- Earn a certificate from SkillUp EdTech
- Add to your LinkedIn profile
Prep for Microsoft Azure Data Engineer Associate Cert DP-203 at Coursera Course details
- What you'll learn
- Explain how to design data for analysis using Azure Databricks, Apache Spark, and Azure Synapse pipelines.
- Describe how to ingest, clean, and transform data using Azure Synapse.
- Identify data processing solutions using Azure Databricks and manage pipelines in Azure Synapse pipelines.
- Discuss the steps to secure, optimize, and monitor data storage using Azure Synapse Analytics.
- This course will guide you on how to prepare for the DP-203: Data Engineering on Microsoft Azure certificate exam.
- By the end of this course, you will be able to:
- - Explain how to design data for analysis using Azure Databricks, Apache Spark, and Azure Synapse pipelines.
- - Describe how to ingest, clean, and transform data using Azure Synapse.
- - Identify data processing solutions using Azure Databricks and manage pipelines in Azure Synapse pipelines.
- - Discuss the steps to secure, optimize, and monitor data storage using Azure Synapse Analytics. This course is designed for IT professionals who want to prepare for the Microsoft DP-203 exam and demonstrate their expertise in creating analytical solutions by integrating, transforming, and consolidating data from multiple data sources, such as structured, unstructured, and streaming data systems.
- According to Microsoft, candidates for the DP-203 exam should have experience with operationalization of data pipelines and ensure that data stores are high-performing, efficient, organized, and reliable, given a set of business requirements and constraints. You should be able to identify and troubleshoot operational and data quality issues, and design, implement, monitor, and optimize data platforms to meet the data pipelines.
Prep for Microsoft Azure Data Engineer Associate Cert DP-203 at Coursera Curriculum
Designing and implementing data storage and data exploration layer
Course introduction
Partitioning in Azure Data Lake Storage Gen2
Partitioning strategy for files in Azure Synapse Analytics
Partitioning Azure Blob Storage
Creating and executing a compute solution
Microsoft Purview Catalog
Course overview
Azure Synapse Analytics database templates
Practice quiz: Designing architecture and implementing data storage
Graded quiz: Designing and implementing data storage and data exploration layer
Developing data processing
Transforming data using Transact-SQL
Data transformations using Azure Synapse Analytics and pipelines
Transforming data using Apache Spark and Azure Stream Analytics
Performing exploratory data analytics
Developing batch processing solution
Data pipelines
Integrating Jupyter and Python Notebooks
Creating a stream processing solution
Handling duplicate, missing, and late-arriving data
Practice quiz: Ingesting and transforming data
Practice quiz: Developing batch processing and stream processing solution
Graded quiz: Developing data processing
Securing, monitoring, and optimizing data storage and data processing
Implementing data security
Implementing Azure role-based access control (RBAC)
Loading a data frame with sensitive information
Writing encrypted data to tables or Parquet files
Monitoring data storage and measuring query performance
Tuning queries by using indexes and cache
Troubleshooting a failed Spark job and pipeline run
Managing sensitive information
Handling the skew and spill in data
Congratulations and next steps
Thanks from the course team
Practice quiz: Data security
Practice quiz: Data storage and processing
Graded quiz: Securing, monitoring, and optimizing data storage and data processing