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Introduction to Azure Data Lake Storage Gen2

DZone

Built on Azure Blob Storage, Azure Data Lake Storage Gen2 is a suite of features for big data analytics. Azure Data Lake Storage Gen1 and Azure Blob Storage's capabilities are combined in Data Lake Storage Gen2.

Azure 243
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Building an Optimized Data Pipeline on Azure Using Spark, Data Factory, Databricks, and Synapse Analytics

DZone

Modern tech stacks such as Apache Spark, Azure Data Factory, Azure Databricks, and Azure Synapse Analytics offer powerful tools for building optimized data pipelines that can efficiently ingest and process data on the cloud.

Azure 246
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Microsoft Azure Event Hubs

DZone

Introduction With big data streaming platform and event ingestion service Azure Event Hubs , millions of events can be received and processed in a single second. Any real-time analytics provider or batching/storage adaptor can transform and store data supplied to an event hub.

Azure 294
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What is a Distributed Storage System

Scalegrid

A distributed storage system is foundational in today’s data-driven landscape, ensuring data spread over multiple servers is reliable, accessible, and manageable. Understanding distributed storage is imperative as data volumes and the need for robust storage solutions rise.

Storage 130
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Dynatrace OpenPipeline: Stream processing data ingestion converges observability, security, and business data at massive scale for analytics and automation in context

Dynatrace

By putting data in context, OpenPipeline enables the Dynatrace platform to deliver AI-driven insights, analytics, and automation for customers across observability, security, software lifecycle, and business domains. This “data in context” feeds Davis® AI, the Dynatrace hypermodal AI , and enables schema-less and index-free analytics.

Analytics 194
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Why log monitoring and log analytics matter in a hyperscale world

Dynatrace

Log monitoring, log analysis, and log analytics are more important than ever as organizations adopt more cloud-native technologies, containers, and microservices-based architectures. Driving this growth is the increasing adoption of hyperscale cloud providers (AWS, Azure, and GCP) and containerized microservices running on Kubernetes.

Analytics 208
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Adding New Capabilities for Real-Time Analytics to Azure IoT

ScaleOut Software

Whether it’s health-tracking watches, long-haul trucks, or security sensors, extracting value from these devices requires streaming analytics that can quickly make sense of the telemetry and intelligently react to handle an emerging issue or capture a new opportunity.

IoT 52