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From Cloud-native to Hybrid and back again

Picnic Engineering

From Cloud-native to Hybrid and back again: Picnic’s on-premises computing journey Many companies are working on their digital transformation, transitioning their traditional on-premises deployment to a cloud setup. Picnic’s infrastructure design focuses on a rapidly scalable cloud solution.

Cloud 97
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7 Best Cloud Database Platforms

KDnuggets

Cloud databases have made it easier and cheaper to develop enterprise-level applications, offering flexibility, convenience, and standard database functionality. See what KDnuggets recommends.

Database 131
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Top 4 Cloud Platforms to Host or Run Docker Containers for Free

Analytics Vidhya

Docker is still considered one of the top tools for creating containers by building Images between containerization platforms or cloud platforms.

Cloud 218
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Confluent + Immerok: Cloud Native Kafka Meets Cloud Native Flink

Confluent

Introducing fully managed Apache Kafka® + Flink for the most robust, cloud-native data streaming platform with stream processing, integration, and streaming analytics in one.

Kafka 145
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New Study: 2018 State of Embedded Analytics Report

Why do some embedded analytics projects succeed while others fail? We surveyed 500+ application teams embedding analytics to find out which analytics features actually move the needle. Read the 6th annual State of Embedded Analytics Report to discover new best practices. Brought to you by Logi Analytics.

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What's new on the cloud for data engineers - part 12 (10.2023-02.2024)

Waitingforcode

It's time for another part of "What's new on the cloud for data engineers" Let's see what happened in the last 5 months.

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Data Science vs Cloud Computing: Differences With Examples

Knowledge Hut

Some techniques add to the development of technology in the business sectors, including Data Science and Cloud Computing, essential aspects of the technology industry. With the help of data science, one can gather all the critical analyses from vast chunks of data stored in clouds. It is equally important to know in both sectors.

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Best Practices for Deploying & Scaling Embedded Analytics

Deployment: Benefits and drawbacks of hosting on premises or in the cloud. Application Design: Depending on your capabilities, you can choose either a VM or a container-based approach. Packaging and Deployment Workflow: Embedded analytics solution should work seamlessly with your current application.