Azure Data Engineer Associate
Exam DP-203

Microsoft Certified - Azure Data Engineer Associate

Rating: ★★★★★ (4.8/5) | Level: Professional | Duration: 4 Months

 Course Introduction

This program helps a candidate to get hands on knowledge how to manage and analyse BigData in Azure cloud platform.In todays world of huge data driven decission making process it is providing new opportunities for business to explore. You will learn the various data platform technologies that are available, and how a Data Engineer can take advantage of this technology to an organization benefit.

AEM Kolkata is the best certification and training institute for Microsoft Certified: Azure Data Engineer Associate. We have our Data Analytics Students working in reputed industries in national and international level.

 Learn Domain

This Azure Data Engineer course upgrate your professionals skills to:

After completion of training and handson lab you will get a capstone project to showcase your skills for future employment.


Azure Data Lake | Azure Synapse Analytics | Azure Data Lake Storage Gen2 | Azure Databricks | Apache Spark | Data Factory | Stream Analytics | Data Factory/Synapse Pipelines | Spark directed acyclic graph (DAG) |
Section 1: 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 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 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 compression  implement partitioning  implement sharding  implement different table geometries with Azure Synapse Analytics pools  implement data redundancy  implement distributions  implement data archiving
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
 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
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
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
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 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 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
 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)

 Upcoming Class Schedule

Start Date Class Timing Duration Class Mode Fees [ INR ]
21st June 7am To 9am [Mon-Fri] Four Months Instructor Led Online Live EMI @4950/-
26th June 7pm To 9pm [WeekEnd] Four Months Instructor Led Online Live EMI @4950/-

 Benefits of Learning Data Engineering on Microsoft Azure

  • As a cloud leader Azure Cloud professionals are getting a well-paid job in this field would never be a problem.
  • The global predictive analytics market size to grow from USD 7.2 billion in 2020 to USD 21.5 billion by 2025.
  • Growth in your career with morethan 70% hike as average.
  • Azure has a more mature model of infrastructure in comparison to other cloud analytics services.
  • Certification DP-203 provides more job opportunities.

 FAQs

What if I miss a session?
Our learning system makes it possible for you to access the recorded session at any time of your convenience. In case you need further clarification on anything related to the content, we also have a dedicated support team in place to help you out. Hence, missing a session won’t be much of a problem.
Do you offer demo classes before enrollment?
Our live sessions are offered only to a limited number of participants to maintain the quality standards. Hence, there is no provision for one to participate without enrollment. However, we can provide you with a few sample recordings of our classes to clarify your doubts. You will have to contact us directly for those samples though.
What should be my internet speed to attend the live classes?
The recommended speed is 2 MBps if you want to attend an uninterrupted live class from AEM.
Are your instructors experienced enough?
All the instructors at AEM are industry experts with a minimum of ten to fifteen years of experience in their relevant fields. They are also further trained by AEM to provide a smooth learning experience to the participants.
Do you provide a course completion certificate?
Of course, we do. We will provide you with a certificate based on a few parameters like exam performance, session attendance, etc. upon the completion of your course.

Contact Us

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contact@aemonline.net

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