The Role of Data Engineering in Real-time Analytics

Data pipelines and solutions to drive instant actionable insights

Key Takeaways

The overview of data transformation architecture underpinning real-time analytics.
Popular applications and solutions for processing, integration, and storage of data.
Comparison of Streaming ETL and ELT for real-time data transformation and processing.
The importance of different visualization techniques for real-time analytics.
Role of AI, edge computing, and serverless architecture for real-time analytics.

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Gain an understanding of the data engineering pipelines required for collecting, ingesting, and processing data to for the sake of real-time analytics. The fundamental data processing methods can be broadly categorized under stream and batch processing wherein sometimes hybrid processing like Lambda and Kappa is implemented. The data-engineering process for real-time analytics is a sequential pipeline of ingestion, processing, integration, storage, and visualization. Get a hang of what’s trending in the real-time data analytics forefront with the integration of technologies like AI, edge computing, and serverless architecture.