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Why log monitoring and log analytics matter in a hyperscale world

Dynatrace

Log monitoring, log analysis, and log analytics are more important than ever as organizations adopt more cloud-native technologies, containers, and microservices-based architectures. What is log analytics? Log analytics is the process of evaluating and interpreting log data so teams can quickly detect and resolve issues.

Analytics 214
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Google Analytics and Dynatrace – Why you need both

Dynatrace

In this post, I wanted to share how I use Google Analytics together with Dynatrace to give me a more complete picture of my customers, and their experience across our digital channels. Google Analytics. Almost all marketers will be familiar with Google Analytics. Digital and Business Analytics. This is my demo dashboard.

Google 153
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Automate CI/CD pipelines with Dynatrace: Part 4, Validation stage

Dynatrace

In the previous blog post of this series , we discussed the crucial role of Dynatrace as an orchestrator that steps in to stop the testing phase in case of any errors. It involves carefully examining the test results from the previous testing phase. What’s next? Curious to see how it all works?

DevOps 208
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Best of breed observability with Spring Micrometer and Dynatrace

Dynatrace

We’ll demonstrate this with a demo Spring application, which uses the Spring Web and Dynatrace Micrometer registry, as shown below. The demo application orders and delivers tacos, so we’ll use a simple counter for successful, and failed, taco deliveries.

Metrics 203
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Dynatrace ensures continuous software quality by combining synthetic monitoring and automatic release validation

Dynatrace

With this approach, teams can scale testing for all environments, which reduces efforts in replicating, updating, and maintaining test scripts. The ability to scale testing as part of the software development lifecycle (SDLC) has proven difficult. No external tools or additional configurations are needed.

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Create compelling insights into business and operational KPIs through metric calculations in the Data explorer

Dynatrace

But without complex analytics to make sense of them in context, metrics are often too raw to be useful on their own. Often referred to as calculated metrics (see Adobe Analytics and Google Analytics ), such metric processing takes one or more existing metrics as input to create a new user-defined metric. Dynatrace news.

Metrics 246
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Using JSONB in PostgreSQL: How to Effectively Store & Index JSON Data in PostgreSQL

Scalegrid

If you store each of the keys as columns, it will result in frequent DML operations – this can be difficult when your data set is large - for example, event tracking, analytics, tags, etc. demo=# select * from books where data ? demo=# explain analyze select * from books where data ? braille'; QUERY PLAN.

Storage 321