Real-time data processing

Data volumes are increasing at geometric progression, meanwhile, decisions must be made in seconds. To have an effective real-time data processing capability, you need proper data ingestion and processing architecture in place.

No, it's not a monster data warehouse with some analytics running on top of it.

Effective real-time data aggregations need multiple capabilities working in tandem, including a simple way to ingest the data from multiple sources in multiple formats, quickly build and deploy data aggregators into a pipeline and pull appropriate data, and a clear strategy on how that aggregated data will be utilized and surfaced for interpretation.

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Model your data ingestion strategy and quickly add additional data sources.

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Make sure you have the capability to quickly build aggregation services that tap into incoming data.

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Build out your data perspectives. Surface them to consumers. Move your ingested data for long time storage and replay capability.

Creating a real-time data streaming solution

You need to have multiple capabilities for quick data ingestion and preprocessing data. Adding data streams into your ingestion pipeline and modify only parts that are not currently in place shouldn't be hard. It should be easy and independent to deploy aggregation or processing routines that consume available data quickly. At the same time, handling multiple moving parts becomes more involved with proper monitoring and tracking required.

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Data ingestion strategy

Design business event-based data ingestion architecture based on an existing or open-source stack. Staged processing will allow you to quickly add data sources or additional pre/post-processing steps and tap into any available stage of processing.

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Processing aggregation services deployment

Create and implement a scalable platform that will allow quick deployment of processing services with built-in scaling, logging, and monitoring. This can be achieved using a Kubernetes orchestrator.

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Building data perspectives

After the data is aggregated and processed, you need a way either to expose it to other services and decision engines or human data consumers. Design and build applications or dashboards modeled explicitly to convey the required information in an understandable and actionable way.

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Long term storage

There is always a question of how to keep data for a long time, especially when streaming. Craft a long-term storage strategy in order to have full recoverability and also the capability to replay all of the data for new aggregation or remedy an issue with an existing aggregator.

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