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DP-203 Training: Data Engineering on Microsoft Azure


DP 203 Course, Training and Certification: Overview

SpireTec is a one-stop solution to all your DP203-related needs. It provides a comprehensive online classroom training to aspiring IT professionals who want to earn Azure DP 203 certification. The DP-203 training is an instructor-led training session to help you design and implement data storage, data processing and data security. The course also focuses on building data storage monitoring and optimization skills. We deliver the best DP-203 classes prepare you for the DP 203 Exam in the best possible manner.

Why should you take DP 203 certification? The certification is an endorsement to your industry-oriented skills that IT recruiters prefer in an Azure Data Engineer. The professionals have to make clients understand the data through exploration, and build and maintain secure and compliant data processing pipelines harnessing Azure tools and techniques.

DP 203 Certification: Breakup of the Exam Topics

If you are applying for a DP203 exam, you will be tested on the following skills:

  1. Implement data storage solutions
  2. Manage and develop data processing
  3. Monitor and optimize data solutions
  4. Design Azure data storage solutions
  5. Design data processing solutions
  6. Design for data security and compliance

SpireTec’s DP-203 training helps you gain expertise in all the sections, making it easier for you to get the DP 203 certification in one go.

 Who Should Take DP 203 Certification?

Wondering if you should go for DP-203 training and certification? See if you fall under the following categories:

Azure Data Engineers

These professionals make use of Azure data services and languages to store and produce high-quality relevant datasets for analysis. They develop data solutions working with divergent data sets that meet the requirements of the data science and data analytics teams. They build and design data architectures using Azure Data factory, Databricks, Data lake, and Synapse. They also perform data mapping activities to describe source data, target data and the high-level or detailed transformations that need to occur. The DP 203 training benefits data engineers in a myriad ways.

Data Analysts

DP-203 training is also very helpful for data analysts. Data analysts working in the Azure IT ecosystem are responsible for designing and maintaining data systems and databases; troubleshooting coding errors and issues with data accessibility; mining data from primary and secondary sources, reorganizing said data in a readable format, interpreting data sets, paying particular attention to trends and patterns, and much more. Our DP-203 training focused on the syllabus of DP 203 Exam helps you earn the desired skills and expertise, making it very easy to get that DP 203 certification.

Prerequisites for DP 203 Certification

Candidates for Exam DP 203 must know the ins and outs of data processing languages, such as SQL, Python, or Scala. They should also have sound knowledge of parallel processing and data architecture patterns. A DP 203 training is only fruitful if you fulfil the above conditions.

Full Description

Design and Implement Data Storage (40-45%)

Design a data storage structure

  • design an Azure Data Lake solution
  • recommend file types for storage
  • recommend file types for analytical queries
  • design for efficient querying
  • design for data pruning
  • design a folder structure that represents the levels of data transformation design a distribution strategy
  • design a data archiving solution

Design a partition strategy

  • design a partition strategy for files
  • design a partition strategy for analytical workloads
  • design a partition strategy for efficiency/performance
  • design a partition strategy for Azure Synapse Analytics
  • identify when partitioning is needed in Azure Data Lake Storage Gen2

Design the serving layer

  • design star schemas
  • design slowly changing dimensions design a dimensional hierarchy
  • design a solution for temporal data
  • design for incremental loading
  • design analytical stores
  • design metastores in Azure Synapse Analytics and Azure Databricks

Implement physical data storage structures

  • implement compression
  • implement partitioning
  • implement sharding
  • implement different table geometries with Azure Synapse Analytics pools
  • implement data redundancy
  • implement distributions
  • implement data archiving

Implement logical data structures

  • build a temporal data solution
  • build a slowly changing dimension
  • build a logical folder structure
  • build external tables
  • implement file and folder structures for efficient querying and data pruning

Implement the serving layer

  • deliver data in a relational star schema
  • deliver data in Parquet files
  • maintain metadata
  • implement a dimensional hierarchy

Design and Develop Data Processing (25-30%)

Ingest and transform data

  • transform data by using Apache Spark
  • transform data by using Transact-SQL
  • transform data by using Data Factory
  • transform data by using Azure Synapse Pipelines
  • transform data by using Stream Analytics
  • cleanse data
  • split data
  • shred JSON
  • encode and decode data
  • configure error handling for the transformation
  • normalize and denormalize values
  • transform data by using Scala
  • perform data exploratory analysis

Design and develop a batch processing solution

  • develop batch processing solutions by using Data Factory, Data Lake, Spark, Azure  Synapse Pipelines, PolyBase, and Azure Databricks
  • create data pipelines
  • design and implement incremental data loads
  • design and develop slowly changing dimensions
  • handle security and compliance requirements
  • scale resources
  • configure the batch size
  • design and create tests for data pipelines
  • integrate Jupyter/IPython notebooks into a data pipeline
  • handle duplicate data
  • handle missing data
  • handle late-arriving data
  • upsert data
  • regress to a previous state
  • design and configure exception handling
  • configure batch retention
  • design a batch processing solution
  • debug Spark jobs by using the Spark UI

Design and develop a stream processing solution

  • develop a stream processing solution by using Stream Analytics, Azure Databricks, and Azure Event Hubs
  • process data by using Spark structured streaming
  • monitor for performance and functional regressions
  • design and create windowed aggregates
  • handle schema drift
  • process time series data
  • process across partitions
  • process within one partition
  • configure checkpoints/watermarking during processing
  • scale resources
  • design and create tests for data pipelines
  • optimize pipelines for analytical or transactional purposes
  • handle interruptions
  • design and configure exception handling
  • upsert data
  • replay archived stream data
  • design a stream processing solution

Manage batches and pipelines

  • trigger batches
  • handle failed batch loads
  • validate batch loads
  • manage data pipelines in Data Factory/Synapse Pipelines
  • schedule data pipelines in Data Factory/Synapse Pipelines
  • implement version control for pipeline artifacts
  • manage Spark jobs in a pipeline

Design and Implement Data Security (10-15%)

Design security for data policies and standards

  • design data encryption for data at rest and in transit
  • design a data auditing strategy
  • design a data masking strategy
  • design for data privacy
  • design a data retention policy
  • design to purge data based on business requirements
  • design Azure role-based access control (Azure RBAC) and POSIX-like Access Control List (ACL) for Data Lake Storage Gen2
  • design row-level and column-level security

Implement data security

  • implement data masking
  • encrypt data at rest and in motion
  • implement row-level and column-level security
  • implement Azure RBAC
  • implement POSIX-like ACLs for Data Lake Storage Gen2
  • implement a data retention policy
  • implement a data auditing strategy
  • manage identities, keys, and secrets across different data platform technologies
  • 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

Monitor and Optimize Data Storage and Data Processing (10-15%)

Monitor data storage and data processing

  • implement logging used by Azure Monitor
  • configure monitoring services
  • measure performance of data movement
  • monitor and update statistics about data across a system
  • monitor data pipeline performance
  • measure query performance
  • monitor cluster performance
  • understand custom logging options
  • schedule and monitor pipeline tests
  • interpret Azure Monitor metrics and logs
  • interpret a Spark directed acyclic graph (DAG)

Optimize and troubleshoot data storage and data processing

  • compact small files
  • rewrite user-defined functions (UDFs)
  • handle skew in data
  • handle data spill
  • tune shuffle partitions
  • find shuffling in a pipeline
  • optimize resource management
  • tune queries by using indexers
  • tune queries by using cache
  • optimize pipelines for analytical or transactional purposes
  • optimize pipeline for descriptive versus analytical workloads
  • troubleshoot a failed spark job
  • troubleshoot a failed pipeline run

Fees & Schedule

Delivery Mode Course Duration Fees
Live Virtual Training 4 Days Ask for Quote
Onsite Classroom Training 4 Days Ask for Quote
Customized Training 4 Days Ask for Quote


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