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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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Real-Time Analytics

DZone

This is an article from DZone's 2023 Data Pipelines Trend Report. For more: Read the Report We live in an era of rapid data generation from countless sources, including sensors, databases, cloud, devices, and more. Stream processing is used to query a continuous stream of data and immediately process events in that stream.

Analytics 286
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Enhancing Azure data analytics and Azure observability with Dynatrace Grail

Dynatrace

Azure observability and Azure data analytics are critical requirements amid the deluge of data in Azure cloud computing environments. requires Azure observability Data has become a pivotal asset in the current IT landscape, and AI has unequivocally become the linchpin for differentiation. Digital transformation 2.0

Azure 182
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2. Diving Deeper into Psyberg: Stateless vs Stateful Data Processing

The Netflix TechBlog

By Abhinaya Shetty , Bharath Mummadisetty In the inaugural blog post of this series, we introduced you to the state of our pipelines before Psyberg and the challenges with incremental processing that led us to create the Psyberg framework within Netflix’s Membership and Finance data engineering team.

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

DZone

Data processing in the cloud has become increasingly popular due to its scalability, flexibility, and cost-effectiveness. This article will explore how these technologies can be used together to create an optimized data pipeline for data processing in the cloud.

Azure 246
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Improving customer experience with business process monitoring

Dynatrace

A business process is a collection of related, usually structured tasks or steps, performed in sequence, that achieve a defined business goal. Tasks may be manual or automatic, and many business processes will include a combination of both. Make better decisions by providing managers with real-time data about the business.

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Dynatrace completed Data Privacy Framework self-certification

Dynatrace

To enable participating organizations to meet the EU requirements for transferring personal data to the U.S., the Data Privacy Framework (DPF) is designed to serve as an adequate data transfer mechanism under the GDPR. Data Privacy Framework Program (The EU-U.S. Benefits of Data Privacy Framework for Dynatrace customers.