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AI-Driven API and Microservice Architecture Design for Cloud

Dzone - DevOps

Here are some key aspects where AI can drive improvements in architecture design: Intelligent planning : AI can assist in designing the architecture by analyzing requirements, performance metrics, and best practices to recommend optimal structures for APIs and microservices.

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Why Use Kong API Gateway

Dzone - DevOps

The Kong API Gateway is highly performant and offers the following features: Request/Response Transformation : Kong can transform incoming and outgoing API requests and responses to conform to specific formats. Monitoring and Logging : Kong offers detailed metrics and logs to help monitor API performance and identify issues.

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Optimizing Medication Management: How AWS ETL Transforms Healthcare Data for a Leading PBM

Perficient

Data Analyses To get insights from such vast and diverse data sets, an appropriate analytical tool is essential. Scalability Demands As the volume of data grows, the systems have to handle & manage the data without compromising on performance. PySpark helps in performing fast distributed transformations on large data sets.

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Vitech uses Amazon Bedrock to revolutionize information access with AI-powered chatbot

AWS Machine Learning - AI

Vitech helps group insurance, pension fund administration, and investment clients expand their offerings and capabilities, streamline their operations, and gain analytical insights. Additionally, Vitech uses Amazon Bedrock runtime metrics to measure latency, performance, and number of tokens. “We langsmith==0.0.43 pgvector==0.2.3

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Streams Replication Manager Prefixless Replication

Cloudera

Replication is a crucial capability in distributed systems to address challenges related to fault tolerance, high availability, load balancing, scalability, data locality, network efficiency, and data durability. SRM replicates data at high performance and keeps topic properties in sync across clusters.

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Fraud Detection with Cloudera Stream Processing Part 1

Cloudera

In a previous blog of this series, Turning Streams Into Data Products , we talked about the increased need for reducing the latency between data generation/ingestion and producing analytical results and insights from this data. Building real-time streaming analytics data pipelines requires the ability to process data in the stream.

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AIOps and our Robot Kubernetes Kops

Linux Academy

N-Tier architectures and micro-services applications must be tuned for performance. This bursting is intentional and guided by state-of-the-art monitoring and metrics to know exactly which tiers of the application need to be scaled to maintain SLA’s (Service Level Agreements). Federating Metrics. Machine Learning.