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Apache Spark vs StreamSets: What are the differences?
Introduction
Apache Spark and StreamSets are two widely used technologies in the field of big data processing. While both are designed to handle large volumes of data, they have some key differences that distinguish them from each other. In this article, we will explore these differences in depth.
Integration with Hadoop Ecosystem: One of the major differences between Apache Spark and StreamSets is their integration with the Hadoop ecosystem. Apache Spark is primarily designed to work with the Hadoop ecosystem and seamlessly integrates with Hadoop Distributed File System (HDFS), Apache HBase, Apache Hive, and other components. On the other hand, StreamSets is a data integration platform that can work with various systems, including Hadoop, but does not have the same level of deep integration as Apache Spark.
Real-time Processing vs Batch Processing: Another key difference between Apache Spark and StreamSets is their primary focus on real-time processing and batch processing, respectively. Apache Spark is known for its real-time processing capabilities, allowing users to process and analyze data in near real-time, making it suitable for applications that require fast data processing. StreamSets, on the other hand, is focused on batch processing, where data is processed in batches rather than in real-time.
Programming Languages and APIs: Apache Spark and StreamSets also differ in terms of the programming languages and APIs they support. Apache Spark provides APIs in multiple languages, including Scala, Java, Python, and R, allowing developers to choose the language they are most comfortable with. StreamSets, on the other hand, provides a visual interface for designing data pipelines, making it more accessible for non-programmers and those who prefer a visual approach.
Data Transformation and Processing: When it comes to data transformation and processing, Apache Spark and StreamSets have different approaches. Apache Spark provides a rich set of transformations and processing operations, allowing users to manipulate and analyze data in various ways. StreamSets, on the other hand, focuses more on data integration and movement, providing tools for extracting, transforming, and loading data from various sources.
Scalability and Resource Management: Both Apache Spark and StreamSets are designed to handle large volumes of data, but they differ in terms of scalability and resource management. Apache Spark is known for its ability to scale horizontally, allowing users to add more nodes to the cluster to handle increasing workloads. StreamSets, on the other hand, is designed to be lightweight and can be easily deployed on smaller systems, making it suitable for use cases where scalability is not a primary concern.
Use Cases and Industry Adoption: Lastly, Apache Spark and StreamSets have different use cases and industry adoption. Apache Spark is widely used in industries such as finance, healthcare, and e-commerce, where real-time data processing and analytics are crucial. StreamSets, on the other hand, is popular in industries such as data integration, data engineering, and data governance, where the focus is more on data movement and transformation.
In summary, Apache Spark and StreamSets differ in terms of their integration with the Hadoop ecosystem, focus on real-time processing or batch processing, programming languages and APIs supported, approach to data transformation and processing, scalability and resource management capabilities, as well as their use cases and industry adoption.
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 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
Pros of StreamSets
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Cons of Apache Spark
- Speed4
Cons of StreamSets
- No user community2
- Crashes1
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What is Apache Spark?
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