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Amazon AppFlow vs Apache Spark: What are the differences?
Introduction:
1. Scalability and Performance: Amazon AppFlow is a fully managed integration service that enables secure exchange of data between AWS services and SaaS applications without writing custom code, while Apache Spark is an open-source distributed computing system that provides high-level APIs in languages like Java, Scala, Python, and R. Amazon AppFlow is more suitable for simpler integration tasks with predefined connectors and transformations, whereas Apache Spark is designed for complex data processing tasks requiring scalability and high performance.
2. Use Cases: Amazon AppFlow is ideal for businesses looking to quickly set up data flows between various sources and destinations with minimal effort. On the other hand, Apache Spark is well-suited for organizations dealing with massive amounts of data that need to be processed efficiently in a distributed computing environment. The use cases for Apache Spark range from data analytics and machine learning to real-time processing and stream processing.
3. Cost Management: Amazon AppFlow follows a pay-as-you-go pricing model based on the volume of data processed, the number of flow runs, and other factors. In contrast, Apache Spark is open-source software that can be deployed on cloud platforms or on-premises clusters, allowing more control over infrastructure costs. Organizations using Apache Spark can optimize their resources based on workload demands to better manage costs.
4. Learning Curve and Development Effort: Amazon AppFlow offers a low-code approach to data integration, requiring minimal setup and configuration. In comparison, Apache Spark requires developers to have a strong understanding of distributed computing concepts and programming languages like Scala or Python. The learning curve for Apache Spark can be steeper, but it provides more flexibility and control over data processing workflows.
5. Real-time Data Processing: Amazon AppFlow excels in enabling data synchronization and batch processing tasks between different systems in near real-time. In contrast, Apache Spark provides robust real-time processing capabilities through its streaming APIs, allowing for continuous computation on live data streams. Organizations with stringent real-time data processing requirements may prefer the advanced capabilities offered by Apache Spark.
6. Ecosystem Integration: Amazon AppFlow seamlessly integrates with various AWS services, SaaS applications, and databases to facilitate data exchange and automation. Apache Spark, on the other hand, has a rich ecosystem of libraries and connectors for different data sources, machine learning frameworks, and streaming platforms. The extensibility and versatility of Apache Spark make it a preferred choice for organizations with diverse data processing and analysis needs.
In Summary, Amazon AppFlow and Apache Spark differ in terms of scalability, use cases, cost management, learning curve, real-time data processing, and ecosystem integration, catering to different requirements in data integration and processing workflows.
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 Amazon AppFlow
Pros of Apache Spark
- Open-source61
- Fast and Flexible48
- One platform for every big data problem8
- Great for distributed SQL like applications8
- Easy to install and to use6
- Works well for most Datascience usecases3
- Interactive Query2
- Machine learning libratimery, Streaming in real2
- In memory Computation2
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Cons of Amazon AppFlow
Cons of Apache Spark
- Speed4
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What is Amazon AppFlow?
What is Apache Spark?
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