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Extend business observability: Extract business events from online databases (Part 2)

Dynatrace

In part 2, we’ll show you how to retrieve business data from a database, analyze that data using dashboards and ad hoc queries, and then use a Davis analyzer to predict metric behavior and detect behavioral anomalies. Similar to the tutorial extension, we created an extension that performs queries against databases.

Database 276
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Scalable Annotation Service?—?Marken

The Netflix TechBlog

An example for storing both time and space based data would be an ML algorithm that can identify characters in a frame and wants to store the following for a video In a particular frame (time) In some area in image (space) A character name (annotation data) Pic 1 : Editors requesting changes by drawing shapes like the blue circle shown above.

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Creating a Standby Cluster With the Percona Operator for PostgreSQL

Percona Community

In this video, Nickolay Ihalainen, a Senior Scaling Specialist at Percona Global Services, explains how to set up replication with standby clusters for Kubernetes databases using Percona’s open-source tools, including the Percona Operator for PostgreSQL A Standby Cluster is a backup version of your main database.

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Performance Testing at MongoDB

Alex Podelko

David Daly’s presentation at LTB 2020 , How to Waste Time and Money Test ing the Performance of a Software Product, is probably a good introduction [Video] — [Slides]. These two papers provide many more insights: Automated system performance testing at MongoDB , DBTest 2020 [ Video ]. The MongoDB Podcast, Ep.

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MongoDB Performance Resources

Alex Podelko

Some good videos on the topic: Solving MongoDB Performance Riddles with Systems Thinking. Impact of Available IOPS On Your Database Performance. You may check The MongoDB Engineering Journal and the list of papers for more. Tips and Tricks for Query Performance: Let Us.explain() Them. How MongoDB uses MongoDB at Scale.

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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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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.