Microsoft Certified Azure Data Engineer Associate Course in Dubai
Overview of Microsoft Certified Azure Data Engineer Associate Course
Microsoft Certified Azure Data Engineer Associate Course, a candidate for the Azure Data Designer Affiliate qualification ought to have subject knowledge integrating, changing, and settling data from various structured and disorganized data systems into structures appropriate for building analytics options. Zabeel Institute is considered the best training institute in Dubai for Microsoft Certified Azure Data courses.
Responsibilities for this role include assisting stakeholders in understanding the data via exploration, structure, and keeping safe, secure, and compliant data processing pipelines using various tools and methods. These experts usages different Azure data solutions and languages to maintain and generate cleansed and improved datasets for analysis.
An Azure Information Designer also helps ensure that data pipelines and data stores are high-performing, reliable, organized, and dependable, providing a particular set of service requirements and restraints. This professional deals with unanticipated concerns quickly as well as minimizes data loss. An Azure Data Engineer also develops, applies, monitors, and optimizes data platforms to satisfy the data pipe needs.
A prospect for this accreditation should have solid expertise in data processing languages, such as SQL, Python, or SCALA. Also, they need to recognize parallel handling and information architecture patterns.
You will earn this certification if you complete one of the following options:
Pass DP-200 and DP-201 (retiring on June 30, 2021) or
Pass DP-203 (beta released February 23, 2021)
Job role: Data Engineer
Required exams: DP-203
Microsoft Certified Azure Data Engineer Associate Course Certification details
Exam DP-203: Data Engineering on Microsoft Azure
Languages: English
This exam gauges your capacity to complete the following technical jobs: layout and implement data storage space; design and establish data handling; design and also execute information security; and also keep track of as well as maximize information storage space and information handling.
Azure Information Engineers integrate, transform, and settle data from numerous organized and disorganized information systems right into structures suitable for building analytics remedies. Candidates for Examination DP-203: Data Design on Microsoft Azure must have solid understanding of data handling languages, such as SQL, Python, or Scala, and they need to recognize parallel handling and also information design patterns.
Microsoft Certified Azure Data Engineer Associate Course Skills measured
Style and also implement data storage space
Design and also establish information handling
Layout as well as execute data protection
Display and also maximize information storage space and also information processing
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Microsoft Certified Azure Data Engineer Associate Course content
Exam DP-203: Data Engineering on Microsoft Azure
Design and Implement Data Storage (40-45%)
Design a data storage structure
style an Azure Information Lake solution
advise file types for storage
recommend documents kinds for analytical questions
style for effective querying
style for information pruning
layout a folder structure that represents the levels of data transformation
style a distribution method
style an information archiving solution
Design a partition strategy
design a dividers technique for files
style a dividers technique for analytical workloads
design a dividers strategy for efficiency/performance
style a dividers method for Azure Synapse Analytics
determine when partitioning is needed in Azure Data Lake Storage Space Gen2
Design the serving layer
style celebrity schemas
style slowly altering dimensions
design a dimensional hierarchy
layout a service for temporal information
style for step-by-step loading
design analytical stores
style megastores in Azure Synapse Analytics as well as Azure Information blocks
Implement physical data storage structures
execute compression
execute partitioning
execute sharing
carry out different table geometries with Azure Synapse Analytics pools
implement information redundancy
carry out distributions
carry out information archiving
Implement logical data structures
develop a temporal information service
build a gradually altering dimension
develop a rational folder structure
construct outside tables
execute documents as well as folder structures for effective quizzing and also data pruning
Implement the serving layer
provide data in a relational celebrity schema
deliver data in Parquet files
maintain metadata
apply a dimensional power structure
Design and Develop Data Processing (25-30%)
Ingest and transform data
change data by utilizing Apache Flicker
change information by using Transact-SQL
change information by using Information Manufacturing facility
change data by using Azure Synapse Pipelines
change information by utilizing Stream Analytics
cleanse data
split information
shred JSON
inscribe and also decode information
set up mistake dealing with for the improvement
normalize as well as denormalize values
transform information by utilizing Scala
carry out information exploratory evaluation
Design and develop a batch processing solution
create set handling services by using Information Manufacturing facility, Information Lake, Flicker, Azure Synapse Pipelines, PolyBase, and also Azure Data blocks
produce data pipes
layout and implement incremental data lots
design and also establish gradually changing dimensions
take care of safety and conformity requirements
scale resources
configure the batch dimension
style as well as produce tests for data pipelines
integrate Jupyter/IPython note pads into an information pipeline
manage duplicate information
manage missing out on information
deal with late-arriving data
insert information
regress to a previous state
layout as well as configure exception handling
configure set retention
layout a batch processing remedy
debug Flicker work by using the Glow UI
Design and develop a stream processing solution
establish a stream handling solution by using Stream Analytics, Azure Databricks, as well as Azure Event Hubs
process information by utilizing Flicker organized streaming
display for efficiency and useful regressions
design and develop windowed accumulations
handle schema drift
procedure time collection information
process throughout dividings
procedure within one partition
configure checkpoints/watermarking during processing
range resources
design and also develop tests for information pipes
optimize pipes for logical or transactional objectives
handle interruptions
style and set up exemption handling
insert information
replay archived stream information
style a stream processing remedy
Manage batches and pipelines
trigger batches
handle fell short batch lots
confirm batch tons
take care of information pipelines in Information Factory/Synapse Pipelines
timetable information pipelines in Information Factory/Synapse Pipelines
implement version control for pipe artifacts
handle Flicker work in a pipeline
Design and Implement Data Security (10-15%)
Design security for data policies and standards
design data security for information at rest and in transit
design a data bookkeeping method
layout an information masking strategy
layout for data personal privacy
design a data retention policy
style to purge data based on business needs
style Azure role-based gain access to control (Azure RBAC) and also
POSIX-like Accessibility Control List (ACL) for Data Lake Storage Gen2
design row-level and column-level protection
Implement data security
execute data covering up
encrypt information at rest as well as in motion
apply row-level and column-level safety and security
execute Azure RBAC
execute POSIX-like ACLs for Data Lake Storage Gen2
apply an information retention policy
execute an information bookkeeping approach
take care of identities, tricks, and also secrets across different data
Platform innovations
Implement protected endpoints (private and also public).
Carry out resource tokens in Azure Information bricks.
Load a Data Structure with sensitive info.
Create encrypted information to tables or Parquet data.
Take care of sensitive details.
Monitor and Optimize Data Storage and Data Processing (10-15%)
Monitor data storage and data processing
implement logging made use of by Azure Display
set up keeping track of solutions
step efficiency of data activity
monitor as well as upgrade data about information across a system
monitor data pipeline efficiency
procedure question performance
screen cluster efficiency
comprehend custom logging alternatives
schedule and monitor pipe tests
analyze Azure Screen metrics and logs
Translate a Spark directed acyclic chart (DAG).
Optimize and troubleshoot data storage and data processing
compact small documents
Revise user-defined features (UDFs).
Take care of skew in data.
Manage data spill.
Song shuffle partitions.
Find shuffling in a pipeline.
Maximize source monitoring.
tune queries by utilizing indexers
tune questions by using cache
enhance pipelines for logical or transactional objectives
maximize pipe for detailed versus logical workloads
repair a failed stimulate task
fix an unsuccessful pipe run
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