Big Data & Azure
Last updated
Last updated
Big Data refers to large volumes of structured, semi-structured, and unstructured data that require specialized processing techniques due to their size, variety, and velocity. Azure provides a suite of Big Data analytics services to store, process, and analyze massive datasets. These services include Azure Synapse Analytics, Azure Databricks, Azure HDInsight, and Azure Data Lake, offering capabilities for real-time analytics, machine learning, and large-scale distributed computing.
Azure Synapse Analytics, formerly Azure SQL Data Warehouse, is a cloud-based data analytics service that integrates big data and enterprise data warehousing. It enables parallel query execution, data integration, real-time analytics, and AI-powered insights using T-SQL, Apache Spark, and Data Explorer. It supports massive scalability, data lake integration, and business intelligence (BI) tools, making it ideal for data-driven decision-making and enterprise analytics.
Azure HDInsight is a fully managed cloud service for open-source big data frameworks like Apache Hadoop, Spark, Hive, Kafka, and HBase. It enables distributed data processing, real-time data streaming, and large-scale analytics for structured and unstructured data. HDInsight is widely used for batch processing, machine learning, and IoT analytics, providing scalable and cost-effective big data solutions in Azure.
Azure Databricks is an Apache Spark-based analytics platform designed for big data processing, AI, and machine learning. It offers high-performance data engineering, collaborative data science, and interactive analytics with auto-scaling clusters, MLflow integration, and seamless data lake connectivity. It supports Python, Scala, SQL, and R, making it an ideal solution for AI-driven analytics, data lakes, and advanced data engineering pipelines.