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Introduction to DP-203: Data Engineering on Microsoft Azure Training
The DP-203 Data Engineering on Microsoft Azure certification training course from laraonlinetraining offers candidates proper training and relevant study material to prepare and successfully clear the DP-203 exam.
This learning path is designed to help you prepare for Microsoft’s DP-203 Data Engineering on Microsoft Azure exam. Even if you don’t plan to take the exam, these courses and hands-on labs will help you learn how to deploy and manage a variety of Azure data solutions.
Candidates who pass the DP-203 exam will earn the Microsoft Certified: Azure Data Engineer Associate certification.
The DP-203 exam tests your knowledge of four subject areas: designing and implementing data storage, designing and developing data processing, designing and implementing data security, and monitoring and optimizing data storage and data processing.
After completing this course, students will be able to:
- Design and implement data storage
- Design and develop data processing
- Design and implement data security
- Monitor and optimize data storage and data processing
Prerequisites
The prerequisites of DP-203 exam include:
- A candidate must have solid knowledge of data processing languages, such as SQL, Python, or Scala, and they need to understand parallel processing and data architecture patterns.
- Candidate should have subject matter expertise integrating, transforming, and consolidating data from various structured and unstructured data systems into structures that are suitable for building analytics solutions.
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 MS-100: Microsoft 365 Identity and Services 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-100: Microsoft 365 Identity and Services Training.
- Cost Effective and Flexible Payment Schemes.
- 100% Placement assistance
- We provide Assessment and Mock Interviews
Module 1 Design and Implement Date Storage Design a data storage structure
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1
Design an Azure Data Lake solution -
2
Recommend file types for storage
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3
Recommend file types for analytical queries
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4
Design for efficient querying
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5
Design for data pruning
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6
Design a folder structure that represents the levels of data transformation
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7
Design a distribution strategy
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8
Design a data archiving solution
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9
Design a partition strategy
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10
Design a partition strategy for files
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11
Design a partition strategy for analytical workloads
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12
Design a partition strategy for efficiency/performance
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13
Design a partition strategy for Azure Synapse Analytics
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14
Identify when partitioning is needed in Azure Data Lake Storage Gen2
Design the serving layer
Implement physical data storage structures
Implement logical data structures
Implement the serving layer
Module 2 Design and Develop Data Processing Ingest and transform data
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38
Transform data by using Apache Spark
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39
Transform data by using Transact-SQL
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40
Transform data by using Data Factory
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41
Transform data by using Azure Synapse Pipelines
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42
Transform data by using Stream Analytics
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43
Cleanse data
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44
Split data
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45
Shred JSON
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46
Encode and decode data
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47
Configure error handling for the transformation
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48
Normalize and denormalize values
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49
Transform data by using Scala
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50
Perform data exploratory analysis
Design and develop a batch processing solution
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51
Develop batch processing solutions by using Data Factory, Data Lake, Spark, Azure Synapse Pipelines, PolyBase, and Azure Databricks
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52
Create data pipelines
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53
Design and implement incremental data loads
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54
Design and develop slowly changing dimensions
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55
Handle security and compliance requirements
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56
Scale resources
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57
Configure the batch size
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58
Design and create tests for data pipelines
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59
Integrate Jupyter/IPython notebooks into a data pipeline
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60
Handle duplicate data
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61
Handle missing data
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62
Handle late-arriving data
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63
Upsert data
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64
Regress to a previous state
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65
Design and configure exception handling
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66
Configure batch retention
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67
Design a batch processing solution
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68
Debug Spark jobs by using the Spark UI
Design and develop a stream processing solution
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69
Develop a stream processing solution by using Stream Analytics, Azure Databricks, and Azure Event Hubs
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70
Process data by using Spark structured streaming
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71
Monitor for performance and functional regressions
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72
Design and create windowed aggregates
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73
Handle schema drift
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74
Process time series data
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75
Process across partitions
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76
Process within one partition
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77
Configure checkpoints/watermarking during processing
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78
Scale resources
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79
Design and create tests for data pipelines
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80
Optimize pipelines for analytical or transactional purposes
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81
Handle interruptions
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82
Design and configure exception handling
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83
Upsert data
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84
Replay archived stream data
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85
Design a stream processing solution
Manage batches and pipelines
Module 3 Design and Implement Data Security Design Security for Data Policies and Standards
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93
Design data encryption for data at rest and in transit
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94
Design a data auditing strategy
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95
Design a data masking strategy
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96
Design for data privacy
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97
Design a data retention policy
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98
Design to purge data based on business requirements
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99
Design Azure role-based access control (Azure RBAC) and POSIX-like Access Control List (ACL) for Data Lake Storage Gen2
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100
Design row-level and column-level security
Implement data security
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101
Implement data masking
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102
Encrypt data at rest and in motion
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103
Implement row-level and column-level security
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104
Implement Azure RBAC
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105
Implement POSIX-like ACLs for Data Lake Storage Gen2
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106
Implement a data retention policy
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107
Implement a data auditing strategy
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108
Manage identities, keys, and secrets across different data platform technologies
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109
Implement secure endpoints (private and public)
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110
Implement resource tokens in Azure Databricks
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111
Load a DataFrame with sensitive information
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112
Write encrypted data to tables or Parquet files
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113
Manage sensitive information
Module 4 Monitor and Optimize Data Storage and Data Processing Monitor Data Storage and Data Processing
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114
Implement logging used by Azure Monitor
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115
Configure monitoring services
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116
Measure performance of data movement
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117
Monitor and update statistics about data across a system
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118
Monitor data pipeline performance
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119
Measure query performance
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120
Monitor cluster performance
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121
Understand custom logging options
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122
Schedule and monitor pipeline tests
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123
Interpret Azure Monitor metrics and logs
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124
Interpret a Spark directed acyclic graph (DAG)
Optimize and troubleshoot data storage and data processing
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125
Compact small files
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126
Rewrite user-defined functions (UDFs)
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127
Handle skew in data
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128
Handle data spill
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129
Tune shuffle partitions
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130
Find shuffling in a pipeline
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131
Optimize resource management
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132
Tune queries by using indexers
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133
Tune queries by using cache
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134
Optimize pipelines for analytical or transactional purposes
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135
Optimize pipeline for descriptive versus analytical workloads
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136
Troubleshoot a failed spark job
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137
Troubleshoot a failed pipeline run
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Course details
Duration
30hrs
Lectures
137
Level
Beginner
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