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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 is great for data scientists, data engineers, and software developers working with big datasets, especially if they're already on Google Cloud. Users appreciate its ease of use for processing streaming events and building complex pipelines. Keep in mind that some users find the Python SDK less developed and debugging pipeline errors can be a challenge.
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We find that Dataflow is ideal for mid-size (101-1000 employees) to large enterprises (1001+ employees).
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Dataflow is designed to serve businesses across all industries looking to process and analyze data effectively.
Google Cloud Dataflow features
Supported Streaming AI and ML: Real-time data empowers AI/ML models with the latest information, enhancing prediction accuracy. Dataflow ML simplifies deployment and management of complete ML pipelines. We offer ready-to-use patterns for personalized recommendations, fraud detection, threat prevention, and more. Build streaming AI with Vertex AI, Gemini models, and Gemma models, run remote inference, and streamline data processing with MLTransform. Enhance MLOps and ML job efficiency with Dataflow GPU and right-fitting capabilities. |
Supported Advanced streaming use cases: Dataflow is a fully managed service that uses open source Apache Beam SDK to enable advanced streaming use cases at enterprise scale. It offers rich capabilities for state and time, transformations, and I/O connectors. Dataflow scales to 4K workers per job and routinely processes petabytes of data. It features autoscaling for optimal resource utilization in both batch and streaming pipelines. |
Supported Multimodal data processing: Dataflow enables parallel ingestion and transformation of multimodal data like images, text, and audio. It applies specialized feature extraction for each modality, then fuses these features into a unified representation. This fused data feeds into generative AI models, empowering them to create new content from the diverse inputs. Internal Google teams leverage Dataflow and FlumeJava to organize and compute model predictions for a large pool of available input data with no latency requirements. |
Supported Templates and notebooks: Dataflow has tools that make it easy to get started. Dataflow templates are pre-designed blueprints for stream and batch processing and are optimized for efficient CDC and BigQuery data integration. Iteratively build pipelines with the latest data science frameworks from the ground up with Vertex AI notebooks and deploy with the Dataflow runner. Dataflow job builder is a visual UI for building and running Dataflow pipelines in the Google Cloud console, without writing code. |
Supported Smart diagnostics and monitoring tools: Dataflow offers comprehensive diagnostics and monitoring tools. Straggler detection automatically identifies performance bottlenecks, while data sampling allows observing data at each pipeline step. Dataflow Insights offer recommendations for job improvements. The Dataflow UI provides rich monitoring tools, including job graphs, execution details, metrics, autoscaling dashboards, and logging. Dataflow also features a job cost monitoring UI for easy cost estimation. |
Supported Built-in governance and security: Dataflow helps you protect your data in a number of ways: encrypting data in use with confidential VM support; customer managed encryption keys (CMEK); VPC Service Controls integration; turning off public IPs. Dataflow audit logging gives your organization the visibility into Dataflow usage and helps answer the question “Who did what, where, and when?" for better governance. |
Supported Fully managed platform: Dataflow is a fully managed platform for batch and streaming data processing. It enables scalable ETL pipelines, real-time stream analytics, real-time ML, and complex data transformations using Apache Beam's unified model, all on serverless Google Cloud infrastructure. |
Supported Streaming ML made easy: Turnkey capabilities to bring streaming to AI/ML: RunInference for inference, MLTransform for model training pre-processing, Enrichment for feature store lookups, and dynamic GPU support all bring reduced toil with no wasted spend for limited GPU resources. |
Supported Optimal price-performance with robust tooling: Dataflow offers cost-effective streaming with automated optimization for maximum performance and resource usage. It scales effortlessly to handle any workload and features AI-powered self-healing. Robust tooling helps with operations and understanding. |
Supported Open, portable, and extensible: Dataflow is built for open source Apache Beam with unified batch and streaming support, making your workloads portable between clouds, on-premises, or to edge devices. |
Supported Apache Spark: Apache Spark is a data processing engine that was (and still is) developed with many of the same goals as Google Flume and Dataflow—providing |
Supported Amazon SageMaker: Amazon SageMaker is an alternative to Google Cloud Dataflow |
Supported Alteryx Designer: Alteryx Designer is an alternative to Google Cloud Dataflow |
Supported Altair RapidMiner: Altair RapidMiner is an alternative to Google Cloud Dataflow |
Supported IBM SPSS Statistics: IBM SPSS Statistics is an alternative to Google Cloud Dataflow |
Supported Databricks Data Intelligence Platform: Databricks Data Intelligence Platform is an alternative to Google Cloud Dataflow |
Supported Apache Kafka: Apache Kafka is an alternative to Google Cloud Dataflow |
Supported Amazon Kinesis Data Streams: Amazon Kinesis Data Streams is an alternative to Google Cloud Dataflow |
Supported Hadoop: Hadoop is an alternative to Google Cloud Dataflow |
Supported Akutan: Akutan is an alternative to Google Cloud Dataflow |
Supported Apache Beam: Apache Beam is an alternative to Google Cloud Dataflow |
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.
- Simple and efficient for building complex streaming pipelines.
- Real-time monitoring with key metrics.
- Easy integration with data sources and sinks like Cloud Pub/Sub, Kafka, and Cloud Spanner.
- Autoscaling support.
- Difficult to implement watermarks.
- Python SDK seems less evolved. Kafka integration for Python is not ready for production.
- Poor documentation makes implementation problematic.
- Difficult to debug pipeline errors.
- Long wait times to spin up virtual machines.
Google Cloud Dataflow pricing
The commentary is based on 6 reviews from Google Cloud Dataflow G2 reviews.
We find that Dataflow's pricing is a common concern, with some users finding it expensive compared to alternatives like Apache Flink. However, others appreciate its cost-effectiveness for specific tasks, especially when factoring in the managed service and serverless capabilities.
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 that excels at handling large datasets, both in real-time and historically. We find it especially useful for tasks like stream analytics, machine learning, and data integration, thanks to its serverless nature and open-source foundation.
What is Google Cloud Dataflow and what does Google Cloud Dataflow do?
Google Cloud Dataflow is a fully managed data processing service that excels at handling large datasets, both in real-time and historically. We find it especially useful for tasks like stream analytics, machine learning, and data integration, thanks to its serverless nature and open-source foundation.
We find that Google Cloud Dataflow seamlessly integrates with other Google Cloud services like BigQuery, Cloud Pub/Sub, and Cloud Storage. It also supports open-source tools like Apache Beam and Kafka, enhancing flexibility and interoperability with existing systems.
How does Google Cloud Dataflow integrate with other tools?
We find that Google Cloud Dataflow seamlessly integrates with other Google Cloud services like BigQuery, Cloud Pub/Sub, and Cloud Storage. It also supports open-source tools like Apache Beam and Kafka, enhancing flexibility and interoperability with existing systems.
We find that Google Cloud Dataflow's main competitors include Amazon Kinesis Data Analytics, Amazon Kinesis Data Streams, Amazon Kinesis, Axual, and Decodable. These alternatives offer similar real-time data processing capabilities.
What the main competitors of Google Cloud Dataflow?
We find that Google Cloud Dataflow's main competitors include Amazon Kinesis Data Analytics, Amazon Kinesis Data Streams, Amazon Kinesis, Axual, and Decodable. These alternatives offer similar real-time data processing capabilities.
Yes, Google Cloud Dataflow is a legitimate service from Google Cloud. It's a robust, fully managed data processing service suitable for various data-intensive tasks, though some users find certain features, like watermark implementation and debugging, challenging.
Is Google Cloud Dataflow legit?
Yes, Google Cloud Dataflow is a legitimate service from Google Cloud. It's a robust, fully managed data processing service suitable for various data-intensive tasks, though some users find certain features, like watermark implementation and debugging, challenging.
We couldn't find specific pricing information for Google Cloud Dataflow. It's likely based on usage, so reaching out to Google Cloud directly for a quote is recommended.
How much does Google Cloud Dataflow cost?
We couldn't find specific pricing information for Google Cloud Dataflow. It's likely based on usage, so reaching out to Google Cloud directly for a quote is recommended.
Based on the reviews, some users praise Google Cloud's helpful customer support and 24/7 availability. However, other users mention difficulties with the user interface, network speed, documentation, and instance availability. Therefore, experiences with Google Cloud customer service appear to be mixed.
Is Google Cloud Dataflow customer service good?
Based on the reviews, some users praise Google Cloud's helpful customer support and 24/7 availability. However, other users mention difficulties with the user interface, network speed, documentation, and instance availability. Therefore, experiences with Google Cloud customer service appear to be mixed.
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.
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