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Apache Spark vs Azure Data Factory: What are the differences?
Apache Spark and Azure Data Factory are two popular data processing platforms that are used for big data analytics and processing. While both platforms offer similar functionalities, there are some key differences that set them apart from each other.
Data Processing Approach: One key difference between Apache Spark and Azure Data Factory is their approach to data processing. Apache Spark is a distributed computing system that utilizes in-memory processing to perform fast and efficient data processing tasks. On the other hand, Azure Data Factory is a cloud-based data integration service that focuses on orchestrating and managing data pipelines for batch and real-time data movement and transformation.
Language Support: Another difference between Apache Spark and Azure Data Factory is the programming languages supported by each platform. Apache Spark provides support for multiple programming languages including Scala, Java, Python, and R, making it a versatile platform for data processing and machine learning tasks. Azure Data Factory, on the other hand, primarily relies on Azure Data Lake Analytics, a separate service that supports U-SQL, a language specifically designed for big data processing.
Data Processing Capabilities: Apache Spark offers a wide range of built-in libraries and APIs that provide various data processing capabilities such as data streaming, machine learning, graph processing, and SQL querying. This allows users to perform complex data analytics tasks within a single platform. Azure Data Factory, on the other hand, offers limited data processing capabilities and relies on other Azure services, such as Azure Data Lake Analytics and HDInsight, for advanced data processing tasks.
Deployment Options: Apache Spark offers flexible deployment options, allowing users to run Spark on various platforms including standalone mode, on a cluster, or on cloud providers such as Amazon Web Services (AWS) and Microsoft Azure. Azure Data Factory, being a cloud-based service, is primarily deployed on the Microsoft Azure platform, making it suitable for organizations already using Azure services or looking for a fully managed cloud solution.
Data Integration and Ecosystem: Apache Spark has a robust ecosystem, with integration capabilities for various data sources, such as Hadoop Distributed File System (HDFS), Apache Kafka, Apache Cassandra, and more. This allows users to seamlessly integrate with existing data sources and systems. Azure Data Factory, on the other hand, offers native integration with various Azure services, such as Azure Blob Storage, Azure SQL Database, Azure Cosmos DB, and more, making it a suitable choice for organizations heavily relying on Microsoft Azure services.
In summary, Apache Spark and Azure Data Factory differ in their approach to data processing, language support, data processing capabilities, deployment options, and data integration and ecosystem. These differences make each platform suitable for different use cases and requirements, depending on the organization's infrastructure, data sources, and preferences.
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"
I have to collect different data from multiple sources and store them in a single cloud location. Then perform cleaning and transforming using PySpark, and push the end results to other applications like reporting tools, etc. What would be the best solution? I can only think of Azure Data Factory + Databricks. Are there any alternatives to #AWS services + Databricks?
Pros of Azure Data Factory
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 Data Factory
Cons of Apache Spark
- Speed4
