Google Cloud Data Fusion vs Google Cloud Dataflow

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Google Cloud Data Fusion

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Google Cloud Dataflow

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Google Cloud Data Fusion vs Google Cloud Dataflow: What are the differences?

Google Cloud Data Fusion and Google Cloud Dataflow are two popular services offered by Google Cloud Platform for working with large-scale data processing and analytics. Here are the key differences between them:

  1. Data Integration vs Data Processing: Google Cloud Data Fusion is primarily designed for data integration tasks, allowing users to easily ingest, transform, and integrate data from various sources into a unified and actionable format. It provides a visual interface and pre-built connectors for seamless data integration workflows. On the other hand, Google Cloud Dataflow is focused on large-scale data processing and analytics. It allows users to build and run data processing pipelines using Apache Beam, which is an open-source unified programming model for batch and stream processing. Dataflow provides a scalable and fully managed service for executing data processing jobs in parallel.

  2. Managed vs Customizable: Google Cloud Data Fusion is a fully managed service where Google takes care of the infrastructure, maintenance, and scaling aspects. It provides a low-code development environment with drag-and-drop capabilities, making it easy for users to create data integration workflows without worrying about the underlying infrastructure. In contrast, Google Cloud Dataflow provides more flexibility and customization options for users. It allows users to write custom Apache Beam code to define their data processing pipelines and provides control over the execution environment. Users can choose to run Dataflow pipelines on managed infrastructure or on their own infrastructure using Dataflow SDKs.

  3. Real-time vs Batch Processing: Google Cloud Data Fusion is well-suited for batch data integration tasks where data can be processed in bulk and transformed incrementally. It provides tools and capabilities for efficiently handling large volumes of data in a batch-oriented manner. Alternatively, Google Cloud Dataflow is designed for both batch and real-time data processing. It supports continuous streaming and allows users to process data in real-time as it arrives. Dataflow provides windowing and triggering capabilities for handling streaming data and enables users to perform real-time analytics and actions.

  4. Pricing Model: Google Cloud Data Fusion follows a subscription-based pricing model, where users pay for the specific edition and the number of nodes used. The pricing is based on the specific requirements and usage needs of the users. On the other hand, Google Cloud Dataflow follows a pay-as-you-go model, where users are billed based on the actual usage of processing resources (CPU, memory, etc.) during the execution of data processing pipelines. The pricing is based on the amount of data processed and the duration of pipeline execution.

  5. Pre-built Connectors vs Polyglot Support: Google Cloud Data Fusion provides a wide range of pre-built connectors for seamless integration with various data sources and platforms. These connectors are designed to work out-of-the-box and provide configuration options for easily accessing and transforming data from different systems. In contrast, Google Cloud Dataflow offers polyglot support, allowing users to write pipelines using multiple programming languages such as Java, Python, and Go. It provides a flexible and extensible programming model for building data processing pipelines using the language of choice.

In summary, Google Cloud Data Fusion is a managed service focused on data integration tasks, providing a visual interface and pre-built connectors, while Google Cloud Dataflow is a customizable service for large-scale data processing and analytics, offering support for both batch and real-time processing, custom code, and polyglot support.

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Pros of Google Cloud Data Fusion
Pros of Google Cloud Dataflow
  • 1
    Lower total cost of pipeline ownership
  • 7
    Unified batch and stream processing
  • 5
    Autoscaling
  • 4
    Fully managed
  • 3
    Throughput Transparency

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What is Google Cloud Data Fusion?

A fully managed, cloud-native data integration service that helps users efficiently build and manage ETL/ELT data pipelines. With a graphical interface and a broad open-source library of preconfigured connectors and transformations, and more.

What is Google Cloud Dataflow?

Google Cloud Dataflow is a unified programming model and a managed service for developing and executing a wide range of data processing patterns including ETL, batch computation, and continuous computation. Cloud Dataflow frees you from operational tasks like resource management and performance optimization.

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What companies use Google Cloud Data Fusion?
What companies use Google Cloud Dataflow?
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    What tools integrate with Google Cloud Data Fusion?
    What tools integrate with Google Cloud Dataflow?

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    What are some alternatives to Google Cloud Data Fusion and Google Cloud Dataflow?
    Apache Spark
    Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
    Kafka
    Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.
    Hadoop
    The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.
    Akutan
    A distributed knowledge graph store. Knowledge graphs are suitable for modeling data that is highly interconnected by many types of relationships, like encyclopedic information about the world.
    Apache Beam
    It implements batch and streaming data processing jobs that run on any execution engine. It executes pipelines on multiple execution environments.
    See all alternatives