Creating a temporary data solution, Building a slowly evolving DP-203 dimension, Building a logical folder structure, Building external tables, Implementation of file and folder structures for efficient data querying and pruning, Implementing the service layer, Data delivery in a relational star schema, Data delivery in Parquet files, Maintain metadata, Implementing a dimensional hierarchy, Design and develop data processing – (25%-30%)
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 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 DP-203 exam dumps 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, 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, DP-203 dumps Design and configuration of exception handling, Set up batch retention, Design a batch processing solution, Debugging Spark jobs using Spark UI.


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