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Exploring Parallel Processing: SIMD vs. MIMD Architectures

DZone

In the landscape of computer architecture, two prominent paradigms shape the realm of parallel processing: SIMD (Single Instruction, Multiple Data) and MIMD (Multiple Instruction, Multiple Data) architectures. This approach enables efficient processing of large datasets by applying the same operation to multiple elements concurrently.

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Redefining the Boundaries of People, Process, and Platforms

DZone

The theme of their discussion was redefining the boundaries of people, processes and platforms. Day two of Dynatrace Perform began with a great discussion between Kelsey Hightower , Distinguished Developer Advocate at Google Cloud Platform and Andi Grabner , DevOps Evangelist at Dynatrace.

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Batch vs. Real-Time Processing: Understanding the Differences

DZone

The decision between batch and real-time processing is a critical one, shaping the design, architecture, and success of our data pipelines. Understanding the key distinctions between these two processing paradigms is crucial for organizations to make informed decisions and harness the full potential of their data.

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Business Flow: Why IT operations teams should monitor business processes

Dynatrace

The business process observability challenge Increasingly dynamic business conditions demand business agility; reacting to a supply chain disruption and optimizing order fulfillment are simple but illustrative examples. Most business processes are not monitored. First and foremost, it’s a data problem.

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Rebuilding Netflix Video Processing Pipeline with Microservices

The Netflix TechBlog

Future blogs will provide deeper dives into each service, sharing insights and lessons learned from this process. The Netflix video processing pipeline went live with the launch of our streaming service in 2007. The Netflix video processing pipeline went live with the launch of our streaming service in 2007.

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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. Optimizing Data Input Make Use of Data Forma t In most cases, the data being processed is stored in a columnar format.

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Best Practices for Batch Processing in IBM App Connect Enterprise as a Service

DZone

Batch processing is a capability of App Connect that facilitates the extraction and processing of large amounts of data. Sometimes referred to as data copy , batch processing allows you to author and run flows that retrieve batches of records from a source, manipulate the records, and then load them into a target system.