Data is quickly becoming the new currency of the digital economy, but it is useless if it can’t be processed. The processing of data is essential for subsequent decision-making or executable actions either by the human brain or various devices/applications, etc. There are two primary ways of processing data: namely, batch processing and stream processing. Typically batch processing has been adopted for very large data sets and projects where there is a necessity for deeper data analysis. On the other side, stream processing is used for speed and quickness as soon as data gets generated at the source. In stream processing, a data point or “micro-batch” is inserted directly into the analytical system bit-by-bit as soon as it is generated and processed subsequently to produce key insights in near real-time. By leveraging platforms/frameworks like Apache Kafka, Apache Flink, Apache Storm, or Apache Samza, we can make decisions quickly and efficiently from generated key insights after processing the streaming of data. (In my previous post “Crafting a Multi-Node Multi-Broker Kafka Cluster- A Weekend Project,” read more on how to install Apache Kafka.)

Before developing a system or new infrastructure at the enterprise level for data processing, the adoption of efficient architecture is mandatory to ensure software/frameworks are flexible and scalable enough to handle the massive volume of data with an open design principle. In today’s Big Data landscape, the Lambda architecture is a new archetype for handling the vast amount of data. This architecture can be adopted for both batches as well as stream processing of data as it is a combination of three layers namely batch layer, speed or real-time layer, and service layer. Each layer in the Lambda Architecture relay on various software components.

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