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How to Perfect Lambda Architecture with Oracle Data Integrator (and Kafka / MapR Streams)

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“Lambda architecture is a data-processing architecture designed to handle massive quantities of data by taking advantage of both batch- and stream-processing methods. This approach to architecture attempts to balance latency, throughput, and fault-tolerance by using batch processing to provide comprehensive and accurate views of batch data, while simultaneously using real-time stream processing to provide views of online data. The two view outputs may be joined before presentation. The rise of lambda architecture is correlated with the growth of big data, real-time analytics, and the drive to mitigate the latencies of map-reduce.” – Wikipedia

Previously, I’ve written some blogs covering many use-cases for using Oracle Data Integrator (ODI) for batch processing on top of MapR distribution and for using Oracle GoldenGate (OGG) to stream transactional data into MapR Streams and other Hadoop components. While combining both products perfectly fit for the lambda architecture, the latest release of ODI (12.2.1.2.6) has many new great features, including the ability to deal with Kafka streams as source and target from ODI itself. This feature has tremendous advantages to anyone already having or planning to have a lambda architecture, by simplifying the way we process and handle both batch and fast data within the same logical design, under one product. Now if we combine OGG streaming capabilities and ODI batch/streaming capabilities, …

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