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Apache Flink vs Azure Synapse: What are the differences?
Introduction: In the comparison between Apache Flink and Azure Synapse, there are key differences that differentiate these two technologies in terms of their capabilities and use cases.
1. Performance and Scalability: Apache Flink is known for its low latency and high throughput processing capabilities, making it suitable for real-time stream processing and event-driven applications. On the other hand, Azure Synapse is designed for large-scale data warehousing and analytics, offering capabilities for processing massive amounts of batch data efficiently.
2. Supported Use Cases: Apache Flink is commonly used for real-time data streaming, interactive analytics, and machine learning applications due to its high-performance processing engine. Azure Synapse, on the other hand, is primarily used for data warehousing, big data analytics, and business intelligence applications, offering comprehensive tools for data integration and visualization.
3. Programming Languages: Apache Flink supports multiple programming languages including Java, Scala, and Python, providing developers with flexibility in choosing their preferred language for application development. In contrast, Azure Synapse primarily supports SQL-based querying language for data exploration and analysis, limiting the programming languages available for development.
4. Deployment and Management: Apache Flink can be deployed on various cloud platforms and on-premises environments, enabling organizations to have flexibility in choosing their deployment options. Azure Synapse, as a cloud-based service, offers easy deployment and management through the Azure portal, providing scalability and reliability for data processing tasks.
5. Integration with Ecosystem: Apache Flink has strong integration with popular big data ecosystem tools such as Apache Kafka, Apache Hadoop, and Apache Beam, making it easier for organizations to build end-to-end data processing pipelines. Azure Synapse, being part of the Azure ecosystem, integrates seamlessly with other Azure services like Azure Data Lake Storage, Azure SQL Data Warehouse, and Azure Machine Learning.
6. Cost Consideration: Apache Flink is an open-source framework, which means it is free to use and deploy in production environments, making it a cost-effective option for organizations looking to build real-time processing applications. On the other hand, Azure Synapse is a cloud-based service that requires payment based on usage and resources consumed, making it more suitable for organizations with specific budget considerations.
In Summary, the key differences between Apache Flink and Azure Synapse lie in their performance, supported use cases, programming languages, deployment options, ecosystem integrations, and cost considerations.
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"
Pros of Azure Synapse
- ETL4
- Security3
- Serverless2
- Doesn't support cross database query1
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
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Cons of Azure Synapse
- Dictionary Size Limitation - CCI1
- Concurrency1
