Azure Data Engineer Course Overview
This Azure Data Engineer course is framed to expertise in designing, implementing data solutions using Azure data services. It is suitable for data engineers and IT developers. Each training course is practical oriented and in-built skills development. If you are preparing for the DP-203, join our Azure Data Engineer course in Chennai.
- Firstly, essential skills to design data storage structures and develop partitioning strategy using live sessions.
- Knowledge of developing and implementing serving layers and logical data structures.
- With practical training, learn to perform Ingesting and transforming of data in our Azure Data Engineer training.
- Using Azure Databricks and Stream Analytics, ensure to know developing stream processing solutions.
- Importantly, Knowledge of design and develop data security using expert trainers
- In our Azure Data Engineer course, skills acquired to monitor and optimize data storage and data processing
Course Features
- Duration60 hours
- Skill levelAll level
- Batch Strength15
- AssessmentsYes
- Mock InterviewsYes
- Resume BuildingYes
- PlacementsYes
- Flexible TimingYes
- Fee InstallmentsYes
- LanguageTamil/English
Section 1: What is Partition strategy
- Implement a partition strategy for files
- Implement a partition strategy for analytical workloads, streaming workloads
- Implement a partition strategy for Azure Synapse Analytics
- Azure Data Lake Storage Gen2
Section 2: Understanding the data exploration layer
- How to create and execute queries
- Compute solution that leverages SQL server less and Spark cluster
- Recommend and implement Azure Synapse Analytics database templates
- Data lineage to Microsoft Purview
- Browse and search metadata in Microsoft Purview Data Catalog
Section 3: Overview of Ingest and transform data
- Design and implement incremental loads
- Transform data by using Apache Spark, Transact-SQL (T-SQL)
- Ingest and transform data by using Azure Synapse Pipelines or Azure Data Factory
- Transform data by using Azure Stream Analytics
- What is Cleanse data
- How to handle duplicate data
- How to handle missing data
- How to handle late-arriving data
- What is Split data
- Concept of Shred JSON
- Encode and decode data
- Configure error handling for a transformation
- Normalize and denormalize data
- How to perform data exploratory analysis
Section 4: Batch processing solution
Develop batch processing solutions by using
- Azure Data Lake Storage
- Azure Data bricks
- Azure Synapse Analytics
- Azure Data Factory
- Use Poly Base to load data to a SQL pool
- Implement Azure Synapse Link and query the replicated data
- To create data pipelines
- What is Scale resources
- Configure the batch size
- To create tests for data pipelines
- Integrate Jupyter or Python notebooks into a data pipeline
- Upsert data
- Revert data to a previous state
- Configure exception handling, batch retention
- Read from and write to a delta lake
Section 5: How to develop a stream processing solution
- Create a stream processing solution by using Stream Analytics and Azure Event Hubs
- Process data by using Spark structured streaming
- Create windowed aggregates
- Handle schema drift
- Process time series data
- Process data across partitions
- Process within one partition
- Configure checkpoints and watermarking during processing
- Scale resources
- Create tests for data pipelines
- Optimize pipelines for analytical or transactional purposes
- Handle interruptions
- Configure exception handling
- Upsert data
- Replay archived stream data
Section 6: How to manage batches and pipelines
- Trigger batches
- Handle failed batch loads
- Validate batch loads
- Manage data pipelines in Azure Data Factory or Azure Synapse Pipelines
- Schedule data pipelines in Data Factory or Azure Synapse Pipelines
- Implement version control for pipeline artifacts
- Manage Spark jobs in a pipeline
Section 7: Implement data security
- Implement data masking
- Encrypt data at rest and in motion
- Implement row-level and column-level security
- Implement Azure role-based access control (RBAC)
- Implement POSIX-like access control lists (ACLs) for Data Lake Storage Gen2
- Implement a data retention policy
- Implement secure endpoints (private and public)
- Implement resource tokens in Azure Databricks
- Load a DataFrame with sensitive information
- Write encrypted data to tables or Parquet files
- Manage sensitive information
Section 8: Monitoring data storage and data processing
- Implement logging used by Azure Monitor
- Configure monitoring services
- Monitor stream processing
- Measure performance of data movement
- Monitor and update statistics about data across a system
- Monitor data pipeline performance
- To measure query performance
- How to schedule and monitor pipeline tests
- Interpret Azure Monitor metrics and logs
- How to implement a pipeline alert strategy
Section 9: Optimize and troubleshoot data storage and data processing
- Compact small files
- How to handle skew in data
- Handle data spill
- Optimize resource management
- Tune queries by using indexers, cache
- Troubleshoot a failed Spark job
- Troubleshoot a failed pipeline run, including activities executed in external services
You will be going through detailed 2 to 3 months of Azure Developer AZ 203 certification Hands-on training
- Detailed instructor led sessions to help you become a proficient Expert in Azure Developer AZ 203 certification.
- Build a Azure Developer AZ 203 certification professional portfolio by working on hands on assignments and projects.
- Personalised mentorship from professionals working in leading companies.
- Lifetime access to downloadable Azure Developer AZ 203 certification course materials, interview questions and project resources.
Credo Systemz - Velachery, Chennai
Call Us +91 9884412301
Credo Systemz - OMR, Chennai
Call Us +91 9600112302
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