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Apache Spark vs Azure Synapse: What are the differences?
Introduction
Apache Spark and Azure Synapse are both powerful data processing platforms used in big data analytics. While they have several similarities, there are key differences that set them apart.
Execution Framework: Apache Spark is built on top of the Spark execution engine, which provides in-memory distributed data processing capabilities. On the other hand, Azure Synapse leverages the distributed processing capabilities of Azure Data Lake Analytics for executing big data workloads.
Integration with Azure Services: Azure Synapse offers tight integration with various Azure services, such as Azure Data Factory, Azure Machine Learning, and Azure Databricks. This integration allows seamless data movement, transformation, and analytics across different Azure services. Apache Spark, on the other hand, is not specifically designed for the Azure ecosystem and may require additional setup and configuration to integrate with Azure services.
Data Warehouse Capabilities: Azure Synapse is primarily designed as a unified analytics platform, combining enterprise data warehousing and big data processing capabilities. It provides a fully-managed SQL-based data warehouse, allowing users to query and analyze structured and semi-structured data. Apache Spark, on the other hand, is more focused on big data processing and provides a flexible, distributed computing framework.
Scalability and Performance: Both Apache Spark and Azure Synapse are designed for scalability and can handle large-scale data processing. However, Azure Synapse leverages the underlying scalability and performance capabilities of the Azure platform, making it well-suited for processing massive amounts of data. Apache Spark provides distributed computing capabilities, but it may require additional tuning and configuration for optimal performance.
Pricing and Cost Model: Apache Spark is an open-source project and can be used for free. However, when using it in a cloud environment, there may be additional costs for compute resources and storage. Azure Synapse, on the other hand, is a managed service offered by Microsoft and follows a metered pricing model based on usage. The pricing for Azure Synapse includes compute resources, storage, and data transfer costs.
Development and Programming Paradigm: Apache Spark supports multiple programming languages, including Scala, Python, Java, and R. It offers a rich set of APIs and libraries for data processing, machine learning, and streaming analytics. Azure Synapse, on the other hand, primarily focuses on SQL-based development and provides integration with T-SQL and PolyBase for querying and manipulating data.
In summary, Apache Spark and Azure Synapse are both powerful data processing platforms, but they differ in terms of execution framework, integration with Azure services, data warehouse capabilities, scalability and performance, pricing and cost model, and development and programming paradigm.
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 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 Azure Synapse
- Dictionary Size Limitation - CCI1
- Concurrency1
Cons of Apache Spark
- Speed4
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