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I'm new to big data processing and I'm reading about tools for stream processing and building data pipelines. I found Apache Spark and Spring Cloud Data Flow. I want to know the main differences and the pros and cons of them. Could anybody help me?
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They have similar directed acyclic graph-based (DAG) systems in their core that run jobs in parallel. But while Spark is a cluster-computing framework designed to be fast and fault-tolerant, Dataflow is a fully-managed, cloud-based processing service for batched and streamed data.
Spring Cloud Data Flow provides tools to create complex topologies for streaming and batch data pipelines. The data pipelines consist of Spring Boot apps, built using the Spring Cloud Stream or Spring Cloud Task microservice frameworks.
Apache Beam means a unified programming model. It implements batch and streaming data processing jobs that run on any execution engine. It executes pipelines in multiple execution environments. Apache Spark defines as a fast and general engine for large-scale data processing.
GCP Dataflow is a Unified stream and batch data processing that's serverless, fast, and cost-effective. It is a fully managed data processing service and has many other features which you can find on its website here.
They are 2 completely different tools.
Spring Data Flow is a toolkit for building data integration and real-time data processing pipelines. This tool will help you to orchestrate data pipelines using Spring Boot Apps (Stream or Task). Under the hood, SCDF might use Spring Batch. Note this Spring Boot Apps can call Spark or Kafka applications to support Stream processing.
Apache Spark is an engine for data processing, it is being highly used for data intensive processing and data science. It has libraries such as ML (Machine Learning), Graph (graph processing), integration with Apache Kafka (Spark Streaming), among others.
For streaming, I highly recommend you to study Apache Kafka.
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