Dataflow Reviews & Pricing

by Google | Founded 1998, Stanford, CA

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What is Dataflow?

Industry Specialties: Serves all industries

Dataflow, a streaming analytics software, ingests and processes high-volume, real-time data streams. Imagine it as a powerful pipeline continuously analyzing incoming data, enabling you to react instantly to insights. It caters to businesses needing to analyze data in motion, like financial institutions tracking stock prices or sensor-driven applications monitoring equipment performance. Dataflow's key benefits include scalability to handle massive data volumes, flexibility to adapt to various data sources and analysis needs, and unified processing for both batch and real-time data. Popular features involve visual interface for building data pipelines, built-in machine learning tools for pattern recognition, and seamless integration with other cloud services. Compared to similar products, user experiences highlight Dataflow's ease of use, cost-effectiveness (pay-per-use based on data processed), and serverless architecture, eliminating infrastructure management overheads. However, some users mention limitations in customizability and occasional processing delays for complex workloads.

Pros

Easy to use
Cost-effective
Serverless architecture
Scalable
Flexible

Cons

Limited customization
Occasional processing delays
Learning curve for complex pipelines
Could benefit from more built-in templates
Dependency on other cloud services

PRICE

COMPANY SIZE

DEPLOYMENT

PLATFORM

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User Sentiment i

Based on 106 reviews:

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#8

Dataflow is ranked #8 on the top 10 ETL Tools leaderboard based on a comprehensive analysis performed by SelectHub research analysts. Compare the leaders with our In-Depth Report.

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Dataflow Pricing

Based on our most recent analysis, Dataflow pricing starts at $1 (Per 250GB Of Processed Data).

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Starting From: $1
Pricing Model: Per 250GB of Processed Data
Free Trial: Yes, Request for Free

Training Resources

Dataflow is supported with the following types of training:

Documentation
In Person
Live Online
Videos
Webinars

Support

The following support services are available for Dataflow:

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Phone
Chat
FAQ
Forum
Help Desk
Knowledge Base
Tickets
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24/7 Live Support

Dataflow Benefits and Insights

Why use Dataflow?

Key differentiators & advantages of Dataflow

Reduce TCO: Manage seasonal and spiky task overloads by autoscaling resources as per the task load. Reduce batch-processing costs by using advanced job scheduling and shuffling techniques.
Go Serverless: Do away with operational overhead from data engineering tasks. Allow teams to focus on coding, instead of managing server clusters.
Integrate All Data: Replicates data from Google Cloud Storage into BigQuery, PostgreSQL or Cloud Spanner. Ingest data changes from MySQL, SQL Server and Db2.
Drive Analytics with AI: Build ML-powered data pipelines through support for TensorFlow Extended (TFX). Enables predictive analytics, fraud detection, real-time personalization and more.

Industry Expertise

Dataflow provides data integration to multiple clients in diverse industries globally. Some of these are software, retail, IT, internet, healthcare, financial services, banking, entertainment and manufacturing.

Dataflow Reviews

Average customer reviews & user sentiment summary for Dataflow:

106 reviews

86%

of users would recommend this product

Dataflow has a 'great' User Satisfaction Rating of 86% when considering 106 user reviews from 3 recognized software review sites.

Synopsis of User Ratings and Reviews

Based on an aggregate of Dataflow reviews taken from the sources above, the following pros & cons have been curated by a SelectHub Market Analyst.

Pros

Ease of use: Users consistently praise Dataflow's intuitive interface, drag-and-drop pipeline building, and visual representations of data flows, making it accessible even for those without extensive coding experience.
Cost-effectiveness: Dataflow's pay-as-you-go model is highly appealing, as users only pay for the compute resources they actually use, aligning costs with data processing needs and avoiding upfront infrastructure investments.
Serverless architecture: Users appreciate Dataflow's ability to automatically scale resources based on workload, eliminating the need for manual provisioning and management of servers, reducing operational overhead and streamlining data processing.
Scalability: Dataflow's ability to seamlessly handle massive data volumes and fluctuating traffic patterns is highly valued by users, ensuring reliable performance even during peak usage periods or when dealing with large datasets.
Integration with other cloud services: Users find Dataflow's integration with other cloud services, such as storage, BigQuery, and machine learning tools, to be a significant advantage, enabling the creation of comprehensive data pipelines and analytics workflows within a unified ecosystem.

Cons

Limited customization: Some users express constraints in tailoring certain aspects of Dataflow's behavior to precisely match specific use cases, potentially requiring workarounds or compromises.
Occasional processing delays: While generally efficient, users have reported occasional delays in processing, especially with complex pipelines or during periods of high data volume, which could impact real-time analytics.
Learning curve for complex pipelines: Building intricate Dataflow pipelines can involve a steeper learning curve, especially for those less familiar with Apache Beam concepts or distributed data processing principles.
Dependency on other cloud services: Dataflow's seamless integration with other cloud services is also seen as a potential drawback by some users, as it can increase vendor lock-in and limit portability across different cloud platforms.
Need for more built-in templates: Users often request a wider range of pre-built templates and integrations with external data sources to accelerate pipeline development and streamline common use cases.

Researcher's Summary:

Dataflow, a cloud-based streaming analytics platform, garners praise for its ease of use, scalability, and cost-effectiveness. Users, particularly those new to streaming analytics or with limited coding experience, appreciate the intuitive interface and visual pipeline building, making it a breeze to get started compared to competitors that require more programming expertise. Additionally, Dataflow's serverless architecture and pay-as-you-go model are highly attractive, eliminating infrastructure management burdens and aligning costs with actual data processing needs, unlike some competitors with fixed costs or complex pricing structures.

However, Dataflow isn't without its drawbacks. Some users find it less customizable than competing solutions, potentially limiting its suitability for highly specific use cases. Occasional processing delays, especially for intricate pipelines or high data volumes, can also be a concern, impacting real-time analytics capabilities. Furthermore, while Dataflow integrates well with other Google Cloud services, this tight coupling can restrict portability to other cloud platforms, something competitors with broader cloud compatibility might offer.

Ultimately, Dataflow's strengths in user-friendliness, scalability, and cost-effectiveness make it a compelling choice for those new to streaming analytics or seeking a flexible, cost-conscious solution. However, its limitations in customization and potential processing delays might necessitate exploring alternatives for highly specialized use cases or mission-critical, real-time analytics.

Key Features

Dataflow

Pipeline Authoring: Build data processing workflows with ML capabilities through Google’s Vertex AI Notebooks and deploy with the Dataflow runner. Design Apache Beam pipelines in a read-eval-print-loop (REVL) workflow.
- Templates: Run data processing tasks with Google-provided templates. Package the pipeline into a Docker image, then save as a Flex template in Cloud Storage to reuse and share with others.
Streaming Analytics: Join streaming data from publish/subscribe (Pub/Sub) messaging systems with files in Cloud Storage and tables in BigQuery. Build real-time dashboards with Google Sheets and other BI tools.
Workload Optimization: Automatically partitions data inputs and consistently rebalances for optimal performance. Reduces the impact of hot keys on pipeline functioning.
- Horizontal Autoscaling: Automatically chooses and reallocates the number of worker instances required to run the job.
- Task Shuffling: Moves pipeline tasks out of the worker VMs into the backend, separating compute from state storage.
Security: Turn off public IPs; secure data with a customer-managed encryption key (CMEK). Mitigate the risk of data exfiltration by integrating with VPC Service Controls.
Pipeline Monitoring: Monitor job status, view execution details and receive result updates through the monitoring or command-line interface. Troubleshoot batch and streaming pipelines with inline monitoring. Set alerts for exceptions like stale data and high system latency.

Dataflow Prime

Resource Allocation: Keeps data processing costs to a minimum by reducing job latency.
- Vertical Autoscaling: Scales vertically by adjusting the system’s compute capacity based on how much it’s used. Applies to streaming pipelines in Python.
- Right Fitting: Does away with resource wastage by creating stage-optimized resource pools. Applies to batch pipelines in Python and Java.
Smart Diagnostics: Tune pipeline performance, identify bottlenecks and view job status through visualization and automatic recommendations. Provides efficient data pipeline management based on service-level objectives.

Limitations

At the time of this review, these are the limitations according to user feedback:

Doesn’t provide more than 25 concurrent job runs for a Google Cloud project.
Doesn’t provide more than 125 concurrent jobs for an organization.
Can’t process more than 15000 monitoring requests per minute for a user.
Doesn’t retain batch jobs for more than 30 days.
Doesn’t provide data sharing across pipelines.
Some jobs might fail to load in the monitoring interface.

Suite Support

Go through product documentation, videos, use cases, guides, release notes and FAQs on the vendor’s website. Reach out to the Dataflow community through Stack Overflow or join the Slack community for issue resolution and answers to queries.

Product support is included in Google support packages.

Email: Not specified.

Phone: Not specified.

Training: Sign up to the Google Cloud platform to view product tutorials on the vendor’s documentation page. Third-party websites offer paid training courses.

Tickets: Free users can submit a support request through the UserVoice forum.

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