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• No of position :- ( 1 )

• Post :- 4th Aug 2022

Design a data storage structure:
• design an Azure Data Lake solution | recommend file types for storage | recommend file types for analytical queries | design for efficient querying | design for data pruning | design a folder structure that represents the levels of data transformation | design a distribution strategy

Design a partition strategy:
• design a partition strategy for files | design a partition strategy for analytical workloads

Implement physical data storage structures
• compression | partitioning | shading | different table geometries with Azure Synapse Analytics pools | data redundancy | implement distributions | implement data archiving

Implement the serving layer:
• deliver data in a relational star schema | deliver data in Parquet files | maintain metadata |implement a dimensional hierarch Design and Develop Data Processing
• Ingest and transform data using Spark, T-SQL, Data Factory, Synapse Pipelines
• Implement stream and batch pipelines.

Design and Implement Data Security
• Data policies and standards: masking, encryption, row level and column level security, RBAC,
• Data retention, auditing
• Manage sensitive information Monitor and Optimize Data Storage and Data Processing
• Implement logging used by Azure Monitor, configure monitoring service
• Measure and improve data pipeline performance, cluster performance, query performance
• Manage storage related optimizations like compaction, handling data skews, tune queries using indexing, cache, trouble failed spark job.