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Introduction to Microsoft Certified: Azure Data Engineer Associate Training
Microsoft Certified: Azure Data Engineer Associate , The world of data has evolved and the advent of cloud technologies is providing new opportunities for businesses to explore. In this course, you will learn the various data platform technologies available, and how a Data Engineer can take advantage of this technology to an organization’s benefit.
This course part of a Specialization intended for Data engineers and developers who want to demonstrate their expertise in designing and implementing data solutions that use Microsoft Azure data services anyone interested in preparing for the Exam DP-203: Data Engineering on Microsoft Azure (beta).
WHAT YOU WILL LEARN
- You will learn the various data platform technologies available, and how to take advantage of this technology to an organizations benefit.
- You will learn the basics of storage management in Azure, how to create a Storage Account, and how to choose the right model for your data.
- You will learn how to create and manage data pipelines in the cloud using Azure Data Factory and Azure Synapse Pipeline.
- How to use Azure Synapse Analytics to build Data Warehouses using modern architecture patterns.
Why Should I choose Lara Online Training?
- Laraonlinetraining provide very in-depth course material with real time scenarios for each topic with its Solutions for Microsoft Certified: Azure Data Engineer Associate Training.
- We also provide production case studies during the training.
- Schedule the sessions based upon your comfort by our Highly Qualified Trainers and Real-time Experts.
- Get the Class recordings, immediately after the session for further Reference.
- Avail flexibility with Normal Track, Fast Track, and Weekend Batches also for Ms Certified: Azure Data Engineer Associate Training.
- Cost Effective and Flexible Payment Schemes.
- 100% Placement assistance
- We provide Assessment and Mock Interviews
Design a data storage structure
-
1
design an Azure Data Lake solution -
2
recommend file types for storage
-
3
recommend file types for analytical queries
-
4
design for efficient querying
-
5
design for data pruning
-
6
design a folder structure that represents the levels of data transformation
-
7
design a distribution strategy
-
8
design a data archiving solution
Design a partition strategy
Design the serving layer
Implement physical data storage structures
Implement logical data structures
Implement the serving layer
Design and Develop Data Processing (25-30%)
Ingest and transform data
-
37
transform data by using Apache Spark
-
38
transform data by using Transact-SQL
-
39
transform data by using Data Factory
-
40
transform data by using Azure Synapse Pipelines
-
41
transform data by using Stream Analytics
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42
cleanse data
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43
split data
-
44
shred JSON
-
45
encode and decode data
-
46
configure error handling for the transformation
-
47
normalize and denormalize values
-
48
transform data by using Scala
-
49
perform data exploratory analysis
Design and develop a batch processing solution
-
50
develop batch processing solutions by using Data Factory, Data Lake, Spark, Azure Synapse Pipelines, PolyBase, and Azure Databricks
-
51
create data pipelines
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52
design and implement incremental data loads
-
53
design and develop slowly changing dimensions
-
54
handle security and compliance requirements
-
55
scale resources
-
56
configure the batch size
-
57
design and create tests for data pipelines
-
58
integrate Jupyter/IPython notebooks into a data pipeline
-
59
handle duplicate data
-
60
handle missing data
-
61
handle late-arriving data
-
62
upsert data
-
63
regress to a previous state
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64
design and configure exception handling
-
65
configure batch retention
-
66
design a batch processing solution
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67
debug Spark jobs by using the Spark UI
Design and develop a stream processing solution
-
68
develop a stream processing solution by using Stream Analytics, Azure Databricks, and Azure Event Hubs
-
69
process data by using Spark structured streaming
-
70
monitor for performance and functional regressions
-
71
design and create windowed aggregates
-
72
handle schema drift
-
73
process time series data
-
74
process across partitions
-
75
process within one partition
-
76
configure checkpoints/watermarking during processing
-
77
scale resources
-
78
design and create tests for data pipelines
-
79
optimize pipelines for analytical or transactional purposes
-
80
handle interruptions
-
81
design and configure exception handling
-
82
upsert data
-
83
replay archived stream data
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84
design a stream processing solution
Manage batches and pipelines
Design and Implement Data Security (10-15%)
Design security for data policies and standards
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91
design data encryption for data at rest and in transit
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92
design a data auditing strategy
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93
design a data masking strategy
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94
design for data privacy
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95
design a data retention policy
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96
design to purge data based on business requirements
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97
design Azure role-based access control (Azure RBAC) and POSIX-like Access Control List (ACL) for Data Lake Storage Gen2
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98
design row-level and column-level security
Implement data security
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99
implement data masking
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100
encrypt data at rest and in motion
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101
implement row-level and column-level security
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102
implement Azure RBAC
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103
implement POSIX-like ACLs for Data Lake Storage Gen2
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104
implement a data retention policy
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105
implement a data auditing strategy
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106
manage identities, keys, and secrets across different data platform technologies
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107
implement secure endpoints (private and public)
-
108
implement resource tokens in Azure Databricks
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109
load a DataFrame with sensitive information
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110
write encrypted data to tables or Parquet files
-
111
manage sensitive information
Monitor and Optimize Data Storage and Data Processing (10-15%)
Monitor data storage and data processing
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112
implement logging used by Azure Monitor
-
113
configure monitoring services
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114
measure performance of data movement
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115
monitor and update statistics about data across a system
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116
monitor data pipeline performance
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117
measure query performance
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118
monitor cluster performance
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119
understand custom logging options
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120
schedule and monitor pipeline tests
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121
interpret Azure Monitor metrics and logs
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122
interpret a Spark directed acyclic graph (DAG)
Optimize and troubleshoot data storage and data processing
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123
compact small files
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124
rewrite user-defined functions (UDFs)
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125
handle skew in data
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126
handle data spill
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127
tune shuffle partitions
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128
find shuffling in a pipeline
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129
optimize resource management
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130
tune queries by using indexers
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131
tune queries by using cache
-
132
optimize pipelines for analytical or transactional purposes
-
133
optimize pipeline for descriptive versus analytical workloads
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134
troubleshoot a failed spark job
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135
troubleshoot a failed pipeline run
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Course details
Duration
30hrs
Lectures
135
Level
Beginner
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