Azure Data Engineering

Azure Data Engineer Associate
Exam DP-203

Microsoft Certified - Azure Data Engineer Associate

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

 Course Introduction [DP-203 Certification Training]

Azure Data Engineer Training in kolkata for DP-203 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 DP-203 Certification

This Azure Data Engineer course upgrate your professionals skills to:

 AEM Students are working globally ..

RedHat | TCS | Wipro | CTS | Accenture | Deloitte | Amazon | PWC | Ericsson and many more.....

 This course is for

  • Developer
  • Data Analyst
  • Project Managers
  • Consultants
  • Anyone interested in learning Azure Data Analytics



Dp-203 Course Details:

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)

 Azure Data Engineer DP-203 Training in Kolkata AEM Upcoming Class Schedule -

Start Date Class Timing Course Duration Course Fees
18th October 2022 7:30pm-9pm [Tue-Thu] Four Months INR 19,800/-
29th October 2022 10am-1pm [WeekEnd] Four Months INR 19,800/-
6th November 2022 1pm-4pm [weekEnd] Four Months INR 19,800/-
14th November 2022 9pm-10:30pm [Mon-Fri] One Month ININR 19,800/-

 - for customised class schedule.

 FAQs

Who can Join this course?
Any one who has basic analytical skills can join this course. Data Analyst, Project Leads, DataBase administrator, SQL developer, Application Developers can join this course.
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.

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