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Apache Spark vs Google Cloud Data Fusion: What are the differences?
Introduction
Apache Spark and Google Cloud Data Fusion are both popular technologies used for data processing and analytics. However, there are key differences between the two platforms that set them apart in terms of functionality and use cases.
Architecture: Apache Spark is a powerful open-source data processing framework that provides a distributed computing environment for big data analytics. It operates on the concept of resilient distributed datasets (RDDs) and provides various high-level APIs for processing structured and unstructured data. On the other hand, Google Cloud Data Fusion is a fully managed data integration service that enables users to build, deploy, and manage data pipelines for batch and streaming data processing. It offers a graphical user interface (GUI) for designing and executing data pipelines without writing code, leveraging Google Cloud Platform's infrastructure.
Ease of Use: Apache Spark requires coding in languages like Scala, Java, Python, or R to develop data processing and analytics applications. It provides a rich set of APIs, libraries, and tooling, making it highly customizable but requiring programming skills. In contrast, Google Cloud Data Fusion focuses on a no-code or low-code approach, allowing users to visually design data pipelines using a drag-and-drop interface. This makes it more accessible to users without strong programming backgrounds and enables faster development and deployment of data integration workflows.
Scalability and Performance: Apache Spark is known for its ability to handle massive volumes of data with its distributed computing capabilities. It can scale horizontally across a cluster of machines, parallelizing data processing tasks efficiently. Spark also provides in-memory processing capabilities that significantly improve performance for iterative algorithms and real-time streaming applications. Google Cloud Data Fusion, being a managed service on Google Cloud Platform, also offers scalability and high-performance data processing. It leverages the underlying infrastructure and managed services provided by Google for seamless scalability and reliable performance.
Integration with Ecosystem: Apache Spark has a vibrant ecosystem with a wide range of libraries and connectors that can be leveraged for various tasks. It integrates well with other big data tools and technologies like Hadoop, Hive, HBase, Kafka, and more. This enables seamless data integration and interoperability with existing data infrastructure and workflows. On the other hand, Google Cloud Data Fusion is tightly integrated with Google Cloud Platform services. It provides pre-built connectors to various Google Cloud services like BigQuery, Cloud Storage, Cloud Pub/Sub, and more, simplifying data ingestion and integration with Google's ecosystem.
Managed vs. Self-managed: Apache Spark requires manual setup, configuration, and management of the underlying infrastructure and resources for running Spark clusters. Organizations need to provision and manage their own infrastructure or leverage cloud services to host and run Spark. In contrast, Google Cloud Data Fusion is a fully managed service provided by Google Cloud Platform. It abstracts away the complexity of infrastructure management, automates resource provisioning, and ensures high availability and reliability of data pipelines. This makes it easier for organizations to focus on their data integration and processing tasks rather than managing the underlying infrastructure.
Costs and Pricing: Apache Spark is an open-source framework, meaning it is free to use and deploy on your own infrastructure or cloud services. However, organizations need to consider the costs associated with maintaining and scaling their Spark clusters. On the other hand, Google Cloud Data Fusion follows a pay-as-you-go pricing model. Users are billed based on factors like the number of active data integration pipelines, the amount of data processed, and any additional services used within the Google Cloud Platform ecosystem. Organizations can choose the pricing plan that best suits their requirements and optimize costs accordingly.
In summary, Apache Spark and Google Cloud Data Fusion differ in terms of architecture, ease of use, scalability, integration with ecosystems, deployment model, and costs. Spark provides a powerful, customizable, and programmable environment for big data processing, while Data Fusion offers a user-friendly, no-code approach for building and managing data pipelines on Google Cloud Platform.
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 Google Cloud Data Fusion
- Lower total cost of pipeline ownership1
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 Google Cloud Data Fusion
Cons of Apache Spark
- Speed4
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