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Introducing Dynatrace built-in data observability on Davis AI and Grail

Dynatrace

Surveys from our recent Automation Pulse Report underscore this sentiment: 57% of C-level executives say the absence of data observability and data flow analysis makes it difficult to drive automation in a compliant way. An erroneous change in the database system leads to a subset of the data being categorized incorrectly.

DevOps 197
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Human Factors Behind Incidents: Why Settings Like “idle_session_timeout” Can Be a Bad Idea

Percona

Not everything can be addressed on the database side. Human factor: In an ideal case, there must be stringent restrictions and enforcement on what load and number of connections can be onboarded to the Database. It should be a tight scrutiny like airline security. This is more evident in connection-related incidents.

Servers 78
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Using Real-Time Digital Twins for Corporate Contact Tracing

ScaleOut Software

Using information from the company’s organizational database, it populates each twin with the employee’s ID, team ID, department type, and location. These are defined by the contact tracing application and can be written in C#, Java, or JavaScript. (C# C# was used for the demo application.)

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Using Real-Time Digital Twins for Corporate Contact Tracing

ScaleOut Software

Using information from the company’s organizational database, it populates each twin with the employee’s ID, team ID, department type, and location. These are defined by the contact tracing application and can be written in C#, Java, or JavaScript. (C# C# was used for the demo application.)

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Use Parallel Analysis – Not Parallel Query – for Fast Data Access and Scalable Computing Power

ScaleOut Software

Whether it’s ecommerce shopping carts, financial trading data, IoT telemetry, or airline reservations, these data sets need fast, reliable access for large, mission-critical workloads. Offloading the database boosts performance, reduces bottlenecks, and lowers costs. That’s where performance problems can begin.

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Use Parallel Analysis – Not Parallel Query – for Fast Data Access and Scalable Computing Power

ScaleOut Software

Whether it’s ecommerce shopping carts, financial trading data, IoT telemetry, or airline reservations, these data sets need fast, reliable access for large, mission-critical workloads. Offloading the database boosts performance, reduces bottlenecks, and lowers costs. That’s where performance problems can begin.

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Using Parallel Query with Amazon Aurora for MySQL

Percona

I’m using the “Airlines On-Time Performance” database from [link] (You can find the scripts I used here: [link] ). MySQL on ec2. mysql> show global variables like '%version%'; + -+ + | Variable_name | Value | + -+ + | innodb_version | 5.6.41-84.1 select one row only, no index.

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