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Business Flow: Why IT operations teams should monitor business processes

Dynatrace

The business process observability challenge Increasingly dynamic business conditions demand business agility; reacting to a supply chain disruption and optimizing order fulfillment are simple but illustrative examples. Most business processes are not monitored. First and foremost, it’s a data problem.

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Effective Communication Strategies Between Microservices: Techniques and Real-World Examples

DZone

This step-by-step guide outlines the process of creating a microservices-based system, complete with detailed examples. Microservices allow teams to deploy and scale parts of their application independently, improving agility and reducing the complexity of updates and scaling.

Strategy 279
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Improving customer experience with business process monitoring

Dynatrace

A business process is a collection of related, usually structured tasks or steps, performed in sequence, that achieve a defined business goal. Tasks may be manual or automatic, and many business processes will include a combination of both. Make better decisions by providing managers with real-time data about the business.

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Practical business process monitoring for real-time business observability

Dynatrace

One of the more popular use cases is monitoring business processes, the structured steps that produce a product or service designed to fulfill organizational objectives. By treating processes as assets with measurable key performance indicators (KPIs), business process monitoring helps IT and business teams align toward shared business goals.

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Service level objective examples: 5 SLO examples for faster, more reliable apps

Dynatrace

Certain service-level objective examples can help organizations get started on measuring and delivering metrics that matter. Teams can build on these SLO examples to improve application performance and reliability. In this post, I’ll lay out five SLO examples that every DevOps and SRE team should consider. or 99.99% of the time.

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2. Diving Deeper into Psyberg: Stateless vs Stateful Data Processing

The Netflix TechBlog

By Abhinaya Shetty , Bharath Mummadisetty In the inaugural blog post of this series, we introduced you to the state of our pipelines before Psyberg and the challenges with incremental processing that led us to create the Psyberg framework within Netflix’s Membership and Finance data engineering team.

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Processing Time Series Data With QuestDB and Apache Kafka

DZone

Apache Kafka is a battle-tested distributed stream-processing platform popular in the financial industry to handle mission-critical transactional workloads. For example, the market data team may continuously update real-time quotes for a security to Kafka, and the trading team may consume that data to make buy/sell orders.