Data collection and ingestion is the process of gathering and managing vast amounts of data to enable further processing, analysis, and decision-making.
Examples of Data Collection and Ingestion in Azure:
IoT Hub: Connects and manages billions of IoT assets, enabling data streaming from devices.
Event Hub: Allows for real-time ingestion of millions of events per second, such as logs, telemetry, and application data.
Key Components of Data Storage on Azure:
Azure Data Lake – A storage solution that allows you to store massive amounts of structured and unstructured data. It ensures scalability, security, and seamless integration for big data analytics.
Azure SQL – A cloud-managed relational database engine supporting MySQL, PostgreSQL, and SQL Server, used for storing organized data and running relational queries.
Azure Data Factory (Pipeline) – An ETL (Extract, Transform, Load) tool that automates the movement and transformation of data between various systems.
Key Tools for Data Processing and Transformation:
Azure Data Bricks – A data and AI platform that helps build, deploy, and maintain big data and AI solutions efficiently.
Stream Analytics – A process engine for analyzing and processing streaming data in real-time with sub-millisecond latency.
Synapse Analytics (Pipeline) – Combines technologies for analyzing large data sets and transforming data for enterprise-level analytics.
Data Explorer – Helps analyze large amounts of log and time-series data to extract insights.
Azure Synapse Analytics (Spark) – A tool for big data processing using Spark-based distributed computing frameworks.
Key Solutions for Data Warehousing:
Cosmos DB – A NoSQL database service designed for modern, high-performance applications that require fast and flexible data access.
Synapse Analytics SQL Pool – An enterprise data warehousing solution that enables scalable data storage and analytics for large-scale queries.
Azure Data Engineering Workflow Overview:
Data Collection and Ingestion: Tools like IoT Hub and Event Hub gather real-time and batch data.
Data Storage: Secure and scalable storage is managed using Azure Data Lake, SQL, and Data Factory.
Data Processing and Transformation: Azure Data Bricks, Stream Analytics, and Synapse Analytics handle large-scale data processing and enrichment.
Data Warehousing: Tools like Cosmos DB and Synapse SQL Pool support enterprise-level data storage for analytics.
Data Consumption: Insights are visualized and delivered using Power BI, Tableau, React, and Synapse BI Tool.
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