This part is divided into four subareas shown below.
Ingest and transform data, Data transformation using Apache Spark, Data transformation using Transact-SQL, Data transformation using
DP-203 Data Factory, Data transformation using Azure Synapse Pipelines, Data Transformation Using Stream Analytics, Data Cleaning, Split data, JSON shredding, Data encoding and decoding.,
Error configuration and transformation management., Normalization and denormalization values., Data transformation using Scala, Perform exploratory data analysis., Design and develop a batch processing solution., Develop batch processing solutions using Data Factory, Data Lake, Spark, Azure Synapse Pipelines, PolyBase, and Azure Databricks.
Creating data pipelines,
DP-203 exam dumps Design and implementation of incremental data load, Design and develop dimensions that change slowly, Security and compliance requirements management, Develop resources, Set the batch size, Design and create tests for data pipelines, Integration of Jupyter/Python notebooks with the corresponding data pipeline,
Management of duplicate data, missing data and late arrivals, Inserting data, Return to a previous state, Design and configuration of exception handling, Set up batch retention, Design a batch processing solution, Debugging
DP-203 dumps Spark jobs using Spark UI
Design and develop a steam treatment solution, Developed a stream processing solution using Stream Analytics, Azure Databricks, and Azure Event Hubs, Data processing using Spark structured streaming, Performance monitoring and functional regressions., Design and creation of windowed aggregates.
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