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Designing information architectures that adapt to altering circumstances

Cloud Computing

Designing information architectures that adapt to altering circumstances

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June 13, 2025

A greener path ahead: Overcoming the hidden power value of multi-system software program architectures

Software Development

A greener path ahead: Overcoming the hidden power value of multi-system software program architectures

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April 21, 2025

Transformers and Past: Rethinking AI Architectures for Specialised Duties

Transformers and Past: Rethinking AI Architectures for Specialised Duties

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February 9, 2025

Navigating Your Migration to Databricks: Architectures and Strategic Approaches

Navigating Your Migration to Databricks: Architectures and Strategic Approaches

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January 31, 2025

2025 Predictions: Cloud Architectures, Price Administration and Hybrid By Design

2025 Predictions: Cloud Architectures, Price Administration and Hybrid By Design

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December 24, 2024

Real-time analytics on streaming data requires a robust architecture that can handle high-speed ingest, processing, and analysis of data. Here are three reference architectures that can help you build such systems: 1.?The Lambda Architecture: This reference architecture is based on the idea of separating storage from processing. It consists of four layers: – Batch layer: For storing historical data – Serving layer: For serving aggregated results to applications – Processing layer: For processing real-time and batch data – Ingest layer: For ingesting raw data streams 2.?The Kappa Architecture: This reference architecture is an evolution of the Lambda Architecture. It eliminates the need for a separate batch layer by using a single stream-processing layer that handles both real-time and batch processing. 3.?The Dataflow-based Architecture: This reference architecture uses Apache Beam (formerly known as Google Cloud Dataflow) to process data streams in real-time. It consists of a pipeline of transformations that read from input sources, process the data, and write it to output sinks.

Real-time analytics on streaming data requires a robust architecture that can handle high-speed ingest, processing, and analysis of data. Here are three reference architectures that can help you build such systems: 1.?The Lambda Architecture: This reference architecture is based on the idea of separating storage from processing. It consists of four layers: – Batch layer: For storing historical data – Serving layer: For serving aggregated results to applications – Processing layer: For processing real-time and batch data – Ingest layer: For ingesting raw data streams 2.?The Kappa Architecture: This reference architecture is an evolution of the Lambda Architecture. It eliminates the need for a separate batch layer by using a single stream-processing layer that handles both real-time and batch processing. 3.?The Dataflow-based Architecture: This reference architecture uses Apache Beam (formerly known as Google Cloud Dataflow) to process data streams in real-time. It consists of a pipeline of transformations that read from input sources, process the data, and write it to output sinks.

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September 15, 2024

What makes information-in-motion architectures an indispensable asset for modern enterprises is their ability to seamlessly integrate real-time data streams from multiple sources, enabling organizations to make informed decisions in today’s fast-paced business environment.

What makes information-in-motion architectures an indispensable asset for modern enterprises is their ability to seamlessly integrate real-time data streams from multiple sources, enabling organizations to make informed decisions in today’s fast-paced business environment.

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July 31, 2024