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Optimize mobile app performance, adoption, and conversions with Session Replay and business analytics

Dynatrace

However, today’s highly dynamic cloud-native environments with containers, microservices, and platforms like Kubernetes, make it more challenging to ensure that applications are working as expected and that customers are adopting new features and generating conversions.

Analytics 206
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Designing Instagram

High Scalability

Generating machine learning based personalized recommendations to discover new people, photos, videos, and stories relevant one’s interest. 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.

Design 334
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Jira Cloud, Jira Server, and Jira Data Center Integrations

Tasktop

Whether it’s a result of different teams or lines of business within the same company on separate instances, or even a merger or acquisition, you see different flavors of the multi-Jira architecture: Split across Jira server and Jira Cloud. Split across Jira Data Center and Jira Cloud instances.

Cloud 71
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Procella: unifying serving and analytical data at YouTube

The Morning Paper

Procella: unifying serving and analytical data at YouTube Chattopadhyay et al., to understand YouTube video performance) drive tens of thousands of canned (known in advance) queries per second, that need to be served with latency in the tens of milliseconds. VLDB’19. Reporting and dashboarding use cases (e.g.

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Driving down the cost of Big-Data analytics - All Things Distributed

All Things Distributed

Driving down the cost of Big-Data analytics. The Amazon Elastic MapReduce (EMR) team announced today the ability to seamlessly use Amazon EC2 Spot Instances with their service, significantly driving down the cost of data analytics in the cloud. However, this cannot be done without efficient, scalable data analytics.

Big Data 112
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Release readiness through AI-based white box resiliency testing with JMeter and Dynatrace

Dynatrace

In recent years, customer projects have moved towards complex cloud architectures, including dozens of microservices and different technology stacks which are challenging to develop, maintain, and optimize for resiliency. Automation : Single load test executions can be repeated and tracked. The white box load testing project setup.

Testing 217
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Rebuilding Netflix Video Processing Pipeline with Microservices

The Netflix TechBlog

The Netflix video processing pipeline went live with the launch of our streaming service in 2007. To that end, the Video and Image Encoding team in Encoding Technologies (ET) has spent the last few years rebuilding the video processing pipeline on our next-generation microservice-based computing platform Cosmos.