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Apache Spark vs Azure Databricks: What are the differences?
Introduction
Apache Spark and Azure Databricks are both popular tools for big data processing and analytics, but they have key differences that distinguish them in terms of performance, ease of use, and integration with other services.
Programming Language Support: Apache Spark mainly supports Scala, Java, and Python, while Azure Databricks extends its support to R as well, providing more flexibility in coding languages for data processing and analysis tasks.
Integration with Azure Services: Azure Databricks is tightly integrated with various Azure services such as Azure Data Lake Storage, Azure SQL Data Warehouse, and Azure Cosmos DB, making it easier to seamlessly work with other Azure components in a unified data platform.
Collaboration and Sharing: Azure Databricks offers built-in collaboration features like shared notebooks, job scheduling, and interactive dashboards, enabling efficient teamwork and sharing of insights among data scientists and analysts.
Unified Workspace: Azure Databricks provides a unified workspace for data engineering, collaborative data science, and business analytics, enabling a seamless transition between different activities without the need for separate tools or environments.
Cost Management: Azure Databricks has built-in cost management tools that help monitor and optimize usage to control costs effectively, whereas Apache Spark does not offer such built-in cost management features, requiring manual monitoring and optimization efforts.
Security and Compliance: Azure Databricks provides enhanced security features like Azure Active Directory integration, role-based access control, and encryption at rest, ensuring compliance with industry and organizational security standards more easily compared to Apache Spark.
In Summary, Azure Databricks offers extended language support, integration with Azure services, enhanced collaboration tools, unified workspace, cost management features, and improved security and compliance compared to Apache Spark.
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 Databricks
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 Databricks
Cons of Apache Spark
- Speed4
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What is Azure Databricks?
What is Apache Spark?
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