Need advice about which tool to choose?Ask the StackShare community!
Apache Flink vs Azure Data Factory: What are the differences?
Introduction
Apache Flink and Azure Data Factory are both popular tools used for data processing and analytics. While they share some similarities, there are key differences that set them apart. This article aims to highlight the six main differences between Apache Flink and Azure Data Factory.
Processing Model: Apache Flink is a stream processing framework that supports both batch and real-time data processing. It processes data as continuous streams, enabling low-latency and event-driven applications. On the other hand, Azure Data Factory focuses more on data integration and orchestration. It is designed for batch processing and data movement across various data sources and destinations.
Data Storage and Computation: Apache Flink operates on data stored in distributed file systems or Apache Kafka, making it suitable for real-time analytics on large datasets. It provides in-memory computing capabilities, which allows for faster processing and analysis. In contrast, Azure Data Factory is a managed service on Microsoft Azure that can work with a variety of data storage options, including Azure Blob Storage, Azure Data Lake Storage, and more. It leverages Azure services like Azure Databricks and Azure Synapse Analytics for data processing and computation.
Advanced Analytics: Apache Flink offers a rich set of operators and libraries for complex event processing, pattern matching, and machine learning. It supports stateful processing, allowing for event time processing and windowing. Azure Data Factory, on the other hand, primarily focuses on data movement and transformation. While it integrates with other Azure services like Azure Machine Learning, it does not provide as extensive analytical capabilities as Apache Flink.
Programming Language Support: Apache Flink provides APIs and libraries for writing applications in Java, Scala, and Python. It allows developers to leverage their existing skills and choose the language that suits their requirements. In contrast, Azure Data Factory offers a declarative approach using Azure Data Factory Markup Language (DFML) or JSON. While it supports custom activities using Azure Batch or Azure Functions, the programming language support is limited.
Ecosystem and Community: Apache Flink has a vibrant ecosystem with a wide range of integration options, including connectors for popular data sources like Apache Kafka, Apache Hadoop, and more. It has an active open-source community that constantly contributes to its development and improvement. Azure Data Factory, being a managed service within the Azure ecosystem, integrates seamlessly with other Azure services, such as Azure Data Lake Storage, Azure SQL Database, and Azure Cosmos DB. It benefits from the overall Azure community and ecosystem.
Deployment and Scalability: Apache Flink can be deployed on various infrastructures, including standalone clusters, Apache Mesos, Hadoop YARN, and Kubernetes. It provides elasticity and automatic scaling based on the workload. Azure Data Factory, as a managed service, takes care of the infrastructure and scaling aspects. It scales automatically based on the data volume and workload, offering high availability and fault tolerance.
In summary, Apache Flink is a powerful stream processing framework with support for real-time analytics and advanced processing capabilities. It operates on distributed file systems and provides in-memory computing. On the other hand, Azure Data Factory is a managed service focused on data integration and orchestration, supporting batch processing and data movement across different storage options. While both tools have their strengths, the choice between them depends on specific use cases and requirements.
We have a Kafka topic having events of type A and type B. We need to perform an inner join on both type of events using some common field (primary-key). The joined events to be inserted in Elasticsearch.
In usual cases, type A and type B events (with same key) observed to be close upto 15 minutes. But in some cases they may be far from each other, lets say 6 hours. Sometimes event of either of the types never come.
In all cases, we should be able to find joined events instantly after they are joined and not-joined events within 15 minutes.
The first solution that came to me is to use upsert to update ElasticSearch:
- Use the primary-key as ES document id
- Upsert the records to ES as soon as you receive them. As you are using upsert, the 2nd record of the same primary-key will not overwrite the 1st one, but will be merged with it.
Cons: The load on ES will be higher, due to upsert.
To use Flink:
- Create a KeyedDataStream by the primary-key
- In the ProcessFunction, save the first record in a State. At the same time, create a Timer for 15 minutes in the future
- When the 2nd record comes, read the 1st record from the State, merge those two, and send out the result, and clear the State and the Timer if it has not fired
- When the Timer fires, read the 1st record from the State and send out as the output record.
- Have a 2nd Timer of 6 hours (or more) if you are not using Windowing to clean up the State
Pro: if you have already having Flink ingesting this stream. Otherwise, I would just go with the 1st solution.
Please refer "Structured Streaming" feature of Spark. Refer "Stream - Stream Join" at https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#stream-stream-joins . In short you need to specify "Define watermark delays on both inputs" and "Define a constraint on time across the two inputs"
I have to collect different data from multiple sources and store them in a single cloud location. Then perform cleaning and transforming using PySpark, and push the end results to other applications like reporting tools, etc. What would be the best solution? I can only think of Azure Data Factory + Databricks. Are there any alternatives to #AWS services + Databricks?
Pros of Azure Data Factory
Pros of Apache Flink
- Unified batch and stream processing16
- Easy to use streaming apis8
- Out-of-the box connector to kinesis,s3,hdfs8
- Open Source4
- Low latency2
Sign up to add or upvote prosMake informed product decisions
What is Azure Data Factory?
What is Apache Flink?
Need advice about which tool to choose?Ask the StackShare community!
What companies use Azure Data Factory?
What companies use Apache Flink?
Sign up to get full access to all the companiesMake informed product decisions
What tools integrate with Azure Data Factory?
What tools integrate with Apache Flink?
Sign up to get full access to all the tool integrationsMake informed product decisions
Blog Posts
+6
+4
+7
+8