Design metastores in Azure Synapse
DP-203 Analytics and Azure Databricks, Configuration of physical data storage structures., Implementing compression, Partition Implementation, Implement different table geometries with Azure Synapse Analytics groups, Set up data redundancy, Deployment of distributions, Configure data file, Implementation of logical data structures.
Creating a temporary data solution, Building a slowly evolving 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
DP-203 exam dumps 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 denormalization values., Data transformation using Scala, Perform
DP-203 dumps 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.
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May 10 2024, 01:57 PM
