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Real-time Data Processing: Databricks vs Flink

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Real-time data processing is a critical need for modern-day businesses. It involves processing data as soon as it is generated to derive insights and take immediate actions. Databricks Streaming and Apache Flink are two popular stream processing frameworks that enable developers to build real-time data pipelines, applications and services at scale. In this article, we will compare Databricks Streaming and Apache Flink to understand the underlying architecture, performance, scalability, latency and fault tolerance characteristics as well as programming model differences between them. We will also discuss deployment differences between the two frameworks and finally, provide an overview of pros & cons of Databricks Streaming vs Apache Flink.


Databricks is an integrated platform for data engineering, machine learning, data science and analytics built on top of Apache Spark. Databricks Streaming provides a high-level API for real-time processing, making it easy to use for developers. Databricks Streaming uses the Spark engine to process data in micro-batches, allowing it to achieve low latency and high throughput. It also provides fault-tolerance and guarantees at-least-once processing semantics.

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Apache Flink is a real-time processing platform that provides a unified stream and batch processing engine. It uses a dataflow model to process data, which enables it to achieve low latency and high throughput. Apache Flink also provides fault-tolerance and guarantees exactly-once processing semantics.

Databricks Streaming provides APIs in Python, Java, Scala, and R for developing stream processing applications. Databricks Streaming also supports SQL queries to process streaming data in real-time. Databricks runs on the Databricks Unified Analytics Platform which is a cloud platform that provides an easy-to-use environment for business users, data engineers and machine learning engineers. Databricks Streaming leverages the Databricks Unified Platform to offer automatic scaling for streaming applications.

Apache Flink is a distributed streaming engine that provides efficient stream processing with low latency and high scalability. Flink’s architecture consists of user-defined operators that can be chained together to form data pipelines. This allows for easy parallelization and fault tolerance through checkpointing and stateful operators that enable easy recovery from failures. Flink also provides APIs for Java and Scala for developing applications. Apache Flink can be run on-premise, in the cloud, or as a hybrid mode since it requires manual installation on a platform such as Kubernetes or YARN. This model requires a more manual scaling approach.

Databricks Streaming provides low-latency processing through its integration with Apache Spark Streaming. However, Spark Streaming is designed for micro-batch processing, which can result in higher latency than Flink for small batches. Apache Flink is designed for low-latency processing and provides sub-millisecond latency for event processing. The Flink architecture uses a pipelined data processing approach that enables low-latency processing.


Ultimately, when deciding between Databricks Streaming and Apache Flink for stream processing, businesses need to consider their requirements, budget, scalability needs and the desired programming languages. Databricks Streaming may be a better option if you need an integrated data engineering platform with support for multiple programming languages and auto-scaling capabilities. However, Apache Flink may be the better choice for businesses looking for more control over their streaming architecture and the ability to deploy it on virtually any platform.

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David Callaghan, Solutions Architect

As a solutions architect with Perficient, I bring twenty years of development experience and I'm currently hands-on with Hadoop/Spark, blockchain and cloud, coding in Java, Scala and Go. I'm certified in and work extensively with Hadoop, Cassandra, Spark, AWS, MongoDB and Pentaho. Most recently, I've been bringing integrated blockchain (particularly Hyperledger and Ethereum) and big data solutions to the cloud with an emphasis on integrating Modern Data produces such as HBase, Cassandra and Neo4J as the off-blockchain repository.

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