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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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No need to compromise visibility in public clouds with new Azure services supported by Dynatrace (Part 2)

Dynatrace

This is the second part of our blog series announcing the massive expansion of our Azure services support. Part 1 of this blog series looks at some of the key benefits of Azure DB for PostgreSQL, Azure SQL Managed Instance, and Azure HDInsight. Fully automated observability into your Azure multi-cloud environment.

Azure 154
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Designing Instagram

High Scalability

We will use a graph database such as Neo4j to store the information. Additionally, we can use columnar databases like Cassandra to store information like user feeds, activities, and counters. After that, the post gets added to the feed of all the followers in the columnar data storage. Sample Queries supported by Graph Database.

Design 334
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Artificial Intelligence in Cloud Computing

Scalegrid

The partnership between AI and cloud computing brings about transformative trends like enhanced security through intelligent threat detection, real-time analytics, personalization, and the implementation of edge computing for quicker on-site decision-making. These services are tailored to meet various business requirements.

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The Importance of Selecting the Proper Azure VM Size

SQL Performance

Migrating an on-premises SQL Server instance to an Azure Virtual Machine (VM) is a common method to migrate to Azure. IT professionals are familiar with scoping the size of VMs with regards to vCPU, memory, and storage capacity. You'll see the types referenced as Family in the Azure Portal when sizing a VM. Generation.

Azure 76
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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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The Need for Real-Time Device Tracking

ScaleOut Software

Real-Time Device Tracking with In-Memory Computing Can Fill an Important Gap in Today’s Streaming Analytics Platforms. The Limitations of Today’s Streaming Analytics. How are we managing the torrent of telemetry that flows into analytics systems from these devices? The list goes on.

IoT 78