Microsoft Azure Data Engineer Associate (DP-203) Cert Prep by Microsoft Press

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Microsoft Press and Tim Warner

9:25:15

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001 - Introduction.mp4

05:20

002 - Learning objectives.mp4

00:24

003 - Design an Azure Data Lake solution.mp4

03:59

004 - Recommend file types for storage.mp4

01:56

005 - Recommend file types for analytical queries.mp4

01:01

006 - Design for efficient querying.mp4

15:47

007 - Learning objectives.mp4

00:24

008 - Design a folder structure that represents levels of data transformation.mp4

02:32

009 - Design a distribution strategy.mp4

02:56

010 - Design a data archiving solution.mp4

20:41

011 - Learning objectives.mp4

00:36

012 - Design a partition strategy for files.mp4

03:58

013 - Design a partition strategy for analytical workloads.mp4

01:57

014 - Design a partition strategy for efficiency and performance.mp4

02:56

015 - Design a partition strategy for Azure Synapse Analytics.mp4

02:02

016 - Identify when partitioning is needed in Azure Data Lake Storage Gen2.mp4

23:46

017 - Learning objectives.mp4

00:40

018 - Design star schemas.mp4

03:13

019 - Design slowly changing dimensions.mp4

02:23

020 - Design a dimensional hierarchy.mp4

00:28

021 - Design a solution for temporal data.mp4

01:16

022 - Design for incremental loading.mp4

00:51

023 - Design analytical stores.mp4

02:18

024 - Design metastores in Azure Synapse Analytics and Azure Databricks.mp4

24:10

025 - Learning objectives.mp4

00:29

026 - Implement compression.mp4

01:42

027 - Implement partitioning.mp4

00:42

028 - Implement sharding.mp4

00:18

029 - Implement different table geometries with Azure Synapse Analytics pools.mp4

02:03

030 - Implement data redundancy.mp4

03:55

031 - Implement distributions.mp4

00:27

032 - Implement data archiving.mp4

13:19

033 - Learning objectives.mp4

00:30

034 - Build a temporal data solution.mp4

00:49

035 - Build a slowly changing dimension.mp4

00:53

036 - Build a logical folder structure.mp4

01:03

037 - Build external tables.mp4

02:31

038 - Implement file and folder structures for efficient querying and data pruning.mp4

10:00

039 - Learning objectives.mp4

00:30

040 - Deliver data in a relational star schema.mp4

01:34

041 - Deliver data in Parquet files.mp4

00:46

042 - Maintain metadata.mp4

00:33

043 - Implement a dimensional hierarchy.mp4

12:43

044 - Learning objectives.mp4

00:31

045 - Transform data by using Apache Spark.mp4

02:29

046 - Transform data by using Transact-SQL.mp4

01:05

047 - Transform data by using Data Factory.mp4

01:35

048 - Transform data by using Azure Synapse pipelines.mp4

01:24

049 - Transform data by using Stream Analytics.mp4

19:50

050 - Learning objectives.mp4

00:32

051 - Cleanse data.mp4

02:39

052 - Split data.mp4

01:40

053 - Shred JSON.mp4

02:03

054 - Encode and decode data.mp4

09:35

055 - Learning objectives.mp4

00:33

056 - Configure error handling for the transformation.mp4

01:38

057 - Normalize and denormalize values.mp4

02:19

058 - Transform data by using Scala.mp4

01:22

059 - Perform data exploratory analysis.mp4

13:15

060 - Learning objectives.mp4

00:46

061 - Develop batch processing solutions by using Data Factory, Data Lake, Spark, Azure Syn.mp4

01:14

062 - Create data pipelines.mp4

02:00

063 - Design and implement incremental data loads.mp4

01:20

064 - Design and develop slowly changing dimensions.mp4

00:36

065 - Handle security and compliance requirements.mp4

02:35

066 - Scale resources.mp4

21:11

067 - Learning objectives.mp4

00:37

068 - Configure the batch size.mp4

02:26

069 - Design and create tests for data pipelines.mp4

03:31

070 - Integrate Jupyter and Python Notebooks into a data pipeline.mp4

01:15

071 - Handle duplicate data.mp4

00:23

072 - Handle missing data.mp4

00:36

073 - Handle late-arriving data.mp4

07:39

074 - Learning objectives.mp4

00:39

075 - Upsert data.mp4

01:52

076 - Regress to a previous state.mp4

02:14

077 - Design and configure exception handling.mp4

01:44

078 - Configure batch retention.mp4

01:02

079 - Revisit batch processing solution design.mp4

01:16

080 - Debug Spark jobs by using the Spark UI.mp4

24:55

081 - Learning objective.mp4

00:46

082 - Develop a stream processing solution by using Stream Analytics, Azure Databricks, and.mp4

01:53

083 - Process data by using Spark structured streaming.mp4

01:52

084 - Monitor for performance and functional regressions.mp4

01:34

085 - Design and create windowed aggregates.mp4

01:50

086 - Handle schema drift.mp4

21:50

087 - Learning objectives.mp4

00:47

088 - Process time series data.mp4

01:53

089 - Process across partitions.mp4

02:09

090 - Process within one partition.mp4

01:00

091 - Configure checkpoints and watermarking during processing.mp4

01:02

092 - Scale resources.mp4

01:49

093 - Design and create tests for data pipelines.mp4

01:20

094 - Optimize pipelines for analytical or transactional purposes.mp4

15:26

095 - Learning objectives.mp4

00:28

096 - Handle interruptions.mp4

01:41

097 - Design and configure exception handling.mp4

00:41

098 - Upsert data.mp4

01:25

099 - Replay archived stream data.mp4

01:49

100 - Design a stream processing solution.mp4

09:53

101 - Learning objectives.mp4

00:34

102 - Trigger batches.mp4

01:53

103 - Handle failed batch loads.mp4

01:50

104 - Validate batch loads.mp4

00:45

105 - Manage data pipelines in Data Factory and Synapse pipelines.mp4

01:16

106 - Schedule data pipelines in Data Factory and Synapse pipelines.mp4

00:22

107 - Implement version control for pipeline artifacts.mp4

00:56

108 - Manage Spark jobs in a pipeline.mp4

12:01

109 - Learning objectives.mp4

00:26

110 - Design data encryption for data at rest and in transit.mp4

01:57

111 - Design a data auditing strategy.mp4

00:36

112 - Design a data masking strategy.mp4

01:17

113 - Design for data privacy.mp4

11:40

114 - Learning objectives.mp4

00:37

115 - Design a data retention policy.mp4

01:22

116 - Design to purge data based on business requirements.mp4

01:06

117 - Design Azure RBAC and POSIX-like ACL for Data Lake Storage Gen2.mp4

01:37

118 - Design row-level and column-level security.mp4

14:33

119 - Learning objectives.mp4

00:40

120 - Implement data masking.mp4

01:49

121 - Encrypt data at rest and in motion.mp4

01:40

122 - Implement row-level and column-level security.mp4

00:18

123 - Implement Azure RBAC.mp4

01:31

124 - Implement POSIX-like ACLs for Data Lake Storage Gen2.mp4

00:54

125 - Implement a data retention policy.mp4

00:21

126 - Implement a data auditing strategy.mp4

15:28

127 - Learning objectives.mp4

00:40

128 - Manage identities, keys, and secrets across different data platforms.mp4

02:20

129 - Implement secure endpoints Private and public.mp4

01:38

130 - Implement resource tokens in Azure Databricks.mp4

01:34

131 - Load a DataFrame with sensitive information.mp4

00:54

132 - Write encrypted data to tables or Parquet files.mp4

00:34

133 - Manage sensitive information.mp4

16:51

134 - Learning objectives.mp4

00:30

135 - Implement logging used by Azure Monitor.mp4

01:09

136 - Configure monitoring services.mp4

01:15

137 - Measure performance of data movement.mp4

00:57

138 - Monitor and update statistics about data across a system.mp4

01:12

139 - Monitor data pipeline performance.mp4

00:13

140 - Measure query performance.mp4

10:15

141 - Learning objectives.mp4

00:34

142 - Monitor cluster performance.mp4

01:32

143 - Understand custom logging options.mp4

01:34

144 - Schedule and monitor pipeline tests.mp4

01:58

145 - Interpret Azure Monitor metrics and logs.mp4

01:21

146 - Interpret a Spark Directed Acyclic Graph (DAG).mp4

16:44

147 - Learning objectives.mp4

00:32

148 - Compact small files.mp4

01:09

149 - Rewrite user-defined functions (UDFs).mp4

01:26

150 - Handle skew in data.mp4

01:50

151 - Handle data spill.mp4

01:29

152 - Tune shuffle partitions.mp4

01:07

153 - Find shuffling in a pipeline.mp4

00:21

154 - Optimize resource management.mp4

12:00

155 - Learning objectives.mp4

00:31

156 - Tune queries by using indexers.mp4

01:53

157 - Tune queries by using cache.mp4

00:55

158 - Optimize pipelines for analytical or transactional purposes.mp4

01:38

159 - Optimize pipeline for descriptive versus analytical workloads.mp4

01:28

160 - Troubleshoot failed Spark jobs.mp4

00:30

161 - Troubleshoot failed pipeline runs.mp4

01:14

162 - Summary.mp4

02:08

Description

In this course, Microsoft MVP Tim Warner walks you through what to expect on the DP-203 Data Engineering on Microsoft Azure exam, covering every Exam DP-203 objective in a friendly and logical way. Tim dives into the intricacies of data engineering on Microsoft Azure, focusing on deploying efficient, secure, and robust data processing solutions. Learn how to design and implement diverse data storage strategies, including leveraging Azure Synapse Analytics for managing massive datasets efficiently. Discover techniques for data compression, partitioning, and sharding to optimize storage and access speed. Investigate table geometries, data redundancy, and archival methods to ensure data is both accessible and protected. Ideal for IT professionals, data scientists, and anyone interested in the data engineering capabilities of Azure, this course empowers you to build scalable data solutions and ensure that your data-driven applications perform seamlessly.

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