Microsoft DP-700
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Data engineering focuses on designing, building, and managing data pipelines to enable efficient data analysis. It is essential for transforming raw data into meaningful insights for businesses.
Data engineering deals with the infrastructure and processing of data, while data science focuses on analyzing and extracting insights from data using statistical and machine learning models.
Data ingestion, data storage, data transformation, data validation, orchestration, and data visualization.
Structured data is organized in a tabular format (e.g., databases), semi-structured data has some organization but lacks a fixed schema (e.g., JSON, XML), and unstructured data has no predefined structure (e.g., images, videos, text).
ETL transforms data before loading it into storage, while ELT loads raw data first and then applies transformations, often using cloud-based processing.
A data lake stores raw and processed data at scale, supporting analytics, machine learning, and real-time processing while maintaining cost efficiency.
Cloud solutions offer scalability, flexibility, and cost-effectiveness, while on-premises solutions provide greater control and security.
It provides seamless integration with various data sources through connectors, APIs, and streaming services.
Power BI acts as the visualization and reporting layer within Microsoft Fabric, making data insights accessible to business users.
It natively integrates with services like Azure Data Lake, Azure Machine Learning, and Azure AI for a cohesive data strategy.
Microsoft Fabric supports OneLake storage, which simplifies data management across different workloads and domains.
It follows Microsoft’s compliance standards, providing role-based access control (RBAC), encryption, and auditing features.
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TESTED 24 Mar 2025
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