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Google Cloud Dataflow description
Google Cloud Dataflow is a fully managed, cloud-based service for processing large amounts of data. It's designed to handle both real-time data streams and large historical datasets. Its serverless approach means you don't have to manage infrastructure, and you pay only for the resources used. Dataflow is used for various tasks, including analyzing website traffic in real-time, powering machine learning models, and integrating data across different systems. It's built on open-source technology, making it adaptable to your existing systems.
What companies are using Google Cloud Dataflow?
Who is Google Cloud Dataflow best for
Google Cloud Dataflow simplifies large-scale data processing for real-time streams and batch datasets. Users praise its easy integration with Google Cloud and autoscaling, while some find the Python SDK and debugging challenging. Ideal for data professionals working with large datasets, especially within the Google Cloud ecosystem.
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Ideal for medium to large enterprises (101+ employees) seeking scalable data processing.
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Suitable for businesses across all industries handling large datasets and real-time analytics.
Google Cloud Dataflow features
Supported Dataflow ML simplifies deployment and management of complete ML pipelines, offering ready-to-use patterns for various use cases. Integrates with Vertex AI, Gemini models, and Gemma models for building streaming AI, running remote inference, and streamlining data processing. |
Supported Offers rich capabilities for state and time management, transformations, and I/O connectors. Scales to 4K workers per job and processes petabytes of data, with autoscaling for optimal resource use in batch and streaming pipelines. |
Supported Applies specialized feature extraction for each modality and fuses them into a unified representation for generative AI models to create new content. |
Supported Dataflow templates offer pre-designed blueprints for stream and batch processing. Vertex AI notebooks enable iterative pipeline building with data science frameworks, deployable with the Dataflow runner. Dataflow job builder provides a visual UI for building and running pipelines without coding. |
Supported Includes straggler detection, data sampling, Dataflow Insights with recommendations, a rich UI with job graphs and metrics, autoscaling dashboards, logging, and cost monitoring. |
Supported Offers data encryption with confidential VM support and customer-managed encryption keys (CMEK), VPC Service Controls integration, and audit logging for usage visibility. |
Supported Enables scalable ETL pipelines, real-time stream analytics, real-time ML, and complex data transformations using Apache Beam's unified model. |
We evaluate the sentiment that users express about non-functional aspects of the software
Value and Pricing Transparency
Ease of Use
Reliability and Performance
Scalability
Google Cloud Dataflow reviews
We've summarised 51 Google Cloud Dataflow reviews (Google Cloud Dataflow G2 reviews) and summarised the main points below.
- Easy to use for processing streaming events.
- Building complex streaming pipelines is simple and efficient.
- Real-time monitoring with key metrics (throughput, CPU, memory).
- Easy integration with data sources and sinks (Pub/Sub, Kafka, Spanner).
- Excellent autoscaling capabilities.
- Difficult to implement watermarks.
- Python SDK seems less evolved.
- Kafka integration for Python is not production-ready.
- Limited documentation and resources, especially for Python.
- Debugging can be challenging due to poor logging.
Google Cloud Dataflow pricing
The commentary is based on 6 reviews from Google Cloud Dataflow G2 reviews.
While Google Cloud Dataflow simplifies complex streaming pipelines and offers valuable features, some users find its pricing costly compared to alternatives like Apache Flink. Careful cost monitoring is recommended due to its complex pricing structure.
Users sentiment
See the Google Cloud Dataflow pricing page.
Google Cloud Dataflow alternatives
Amazon Kinesis Data StreamsReal-time data streaming for instant insights and scalable analysis.Read more
Google Cloud Dataflow FAQ
Google Cloud Dataflow is a fully managed data processing service for batch and stream data. It simplifies large-scale data tasks with its serverless approach, autoscaling, and unified programming model. Dataflow integrates with other Google Cloud services and offers cost-effective pricing.
What is Google Cloud Dataflow and what does Google Cloud Dataflow do?
Google Cloud Dataflow is a fully managed data processing service for batch and stream data. It simplifies large-scale data tasks with its serverless approach, autoscaling, and unified programming model. Dataflow integrates with other Google Cloud services and offers cost-effective pricing.
Google Cloud Dataflow integrates seamlessly with other Google Cloud services like Pub/Sub, Kafka, Spanner, Vertex AI, and Gemini models. It leverages Apache Beam's unified model for broader compatibility with various data sources and processing frameworks. This allows for building and deploying complex data pipelines across diverse systems.
How does Google Cloud Dataflow integrate with other tools?
Google Cloud Dataflow integrates seamlessly with other Google Cloud services like Pub/Sub, Kafka, Spanner, Vertex AI, and Gemini models. It leverages Apache Beam's unified model for broader compatibility with various data sources and processing frameworks. This allows for building and deploying complex data pipelines across diverse systems.
Top alternatives to Google Cloud Dataflow include Amazon Kinesis (Data Analytics, Data Streams), Apache Kafka-based Axual, SQL-focused Decodable, and Altair RapidMiner. These competitors offer similar data streaming and processing capabilities.
What the main competitors of Google Cloud Dataflow?
Top alternatives to Google Cloud Dataflow include Amazon Kinesis (Data Analytics, Data Streams), Apache Kafka-based Axual, SQL-focused Decodable, and Altair RapidMiner. These competitors offer similar data streaming and processing capabilities.
Yes, Google Cloud Dataflow is a legitimate and safe fully managed data processing service from Google. It's used by many businesses for real-time data streams and large datasets. Dataflow integrates well within the Google Cloud ecosystem.
Is Google Cloud Dataflow legit?
Yes, Google Cloud Dataflow is a legitimate and safe fully managed data processing service from Google. It's used by many businesses for real-time data streams and large datasets. Dataflow integrates well within the Google Cloud ecosystem.
Google Cloud Dataflow pricing is based on a pay-as-you-go model. There is no fixed monthly price. You are charged for the resources consumed, such as compute engine, storage, and data transfer. Contact Google Cloud for detailed pricing.
How much does Google Cloud Dataflow cost?
Google Cloud Dataflow pricing is based on a pay-as-you-go model. There is no fixed monthly price. You are charged for the resources consumed, such as compute engine, storage, and data transfer. Contact Google Cloud for detailed pricing.
Customer reviews on Google Cloud Dataflow mention helpful 24/7 support and a positive experience with the support team. However, some users find the documentation lacking, particularly for implementation and troubleshooting. Overall, user sentiment towards customer service appears generally positive.
Is Google Cloud Dataflow customer service good?
Customer reviews on Google Cloud Dataflow mention helpful 24/7 support and a positive experience with the support team. However, some users find the documentation lacking, particularly for implementation and troubleshooting. Overall, user sentiment towards customer service appears generally positive.
Reviewed by
Michal has worked at startups for many years and writes about topics relating to software selection and IT management. As a former consultant for Bain, a business advisory company, he also knows how to understand needs of any business and find solutions to its problems.
Tymon is a seasoned CTO who loves finding the perfect tools for any task. He recently headed up the tech department at Batmaid, a well-known Swiss company, where he managed about 60 software purchases, including CX, HR, Payroll, Marketing automation and various developer tools.