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Cutting Big Data Costs: Effective Data Processing With Apache Spark

DZone

In today's data-driven world, efficient data processing plays a pivotal role in the success of any project. Apache Spark , a robust open-source data processing framework, has emerged as a game-changer in this domain.

Big Data 269
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What is IT operations analytics? Extract more data insights from more sources

Dynatrace

With 99% of organizations using multicloud environments , effectively monitoring cloud operations with AI-driven analytics and automation is critical. IT operations analytics (ITOA) with artificial intelligence (AI) capabilities supports faster cloud deployment of digital products and services and trusted business insights.

Analytics 187
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Mastering Hybrid Cloud Strategy

Scalegrid

Mastering Hybrid Cloud Strategy Are you looking to leverage the best private and public cloud worlds to propel your business forward? A hybrid cloud strategy could be your answer. Understanding Hybrid Cloud Strategy A hybrid cloud merges the capabilities of public and private clouds into a singular, coherent system.

Strategy 130
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What is software automation? Optimize the software lifecycle with intelligent automation

Dynatrace

In what follows, we define software automation as well as software analytics and outline their importance. What is software analytics? This involves big data analytics and applying advanced AI and machine learning techniques, such as causal AI. We also discuss the role of AI for IT operations (AIOps) and more.

Software 187
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What is a Distributed Storage System

Scalegrid

Distributed storage systems like HDFS distribute data across multiple servers or nodes, potentially spanning multiple data centers, focusing on partitioning, scalability, and high availability for structured and unstructured data. By implementing data replication strategies, distributed storage systems achieve greater.

Storage 130
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In-Stream Big Data Processing

Highly Scalable

The shortcomings and drawbacks of batch-oriented data processing were widely recognized by the Big Data community quite a long time ago. Distributed and parallel query processing heavily relies on data partitioning to break down a large data set into multiple pieces that can be processed by independent processors.

Big Data 154
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An overview of end-to-end entity resolution for big data

The Morning Paper

An overview of end-to-end entity resolution for big data , Christophides et al., It’s an important part of many modern data workflows, and an area I’ve been wrestling with in one of my own projects. There are a variety of strategies both for weighting and for pruning edges. ACM Computing Surveys, Dec. 2020, Article No.