Dataflow vs Integrate.io

Last Updated:

Our analysts compared Dataflow vs Integrate.io based on data from our 400+ point analysis of ETL Tools, user reviews and our own crowdsourced data from our free software selection platform.

Integrate.io Software Tool

Product Basics

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
read more...
Integrate.io (formerly Xplenty) is an ETL (Extract, Transform, Load) software helping businesses move data between various sources, clean and organize it, and deliver it to analytics platforms. It caters to companies needing to streamline dataflows for insights and reporting. Integrate.io shines with its user-friendly interface, pre-built connectors, and drag-and-drop functionality, making it accessible to non-technical users. Key features include data pipeline building, data transformation tools, and scheduling capabilities. Compared to peers, users praise Integrate.io's intuitive design, ease of use, and vast connector library. However, some mention limited data lineage tracking and high costs for larger data volumes. Pricing varies based on data volume and features needed, typically starting in the hundreds per month and scaling upwards. Consider your data needs and technical expertise when comparing Integrate.io to similar ETL solutions.

Pros
  • User-friendly interface
  • Drag-and-drop functionality
  • Vast connector library
  • Intuitive data mapping
  • Solid customer support
Cons
  • Limited data lineage tracking
  • High costs for larger data volumes
  • Occasional performance issues
  • Customization options can be limited
  • Error handling could be more robust
read more...
$1/250GB of Processed Data
Get a free price quote
Tailored to your specific needs
$15,000 Annually
Get a free price quote
Tailored to your specific needs
Small 
i
Medium 
i
Large 
i
Small 
i
Medium 
i
Large 
i
Windows
Mac
Linux
Android
Chromebook
Windows
Mac
Linux
Android
Chromebook
Cloud
On-Premise
Mobile
Cloud
On-Premise
Mobile

Product Assistance

Documentation
In Person
Live Online
Videos
Webinars
Documentation
In Person
Live Online
Videos
Webinars
Email
Phone
Chat
FAQ
Forum
Knowledge Base
24/7 Live Support
Email
Phone
Chat
FAQ
Forum
Knowledge Base
24/7 Live Support

Product Insights

  • 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. 
read more...
  • Integrate Business Data: Design sophisticated ETL pipelines by connecting to more than 140 files, data warehouses and systems like Amazon Redshift, Snowflake and Oracle, among others. Scales to add more integrations as they become available — no need to create code or manual data flows. 
  • Understand Market Trends: Integrate customer support data with social media, analytics and CRM systems. Prevent churn by personalizing customer outreach messages based on customer interests and lifecycle stage. 
  • Get Marketing Analytics: Build marketing campaigns and reports with enriched, personalized data. Enables targeted customer outreach by moving data to third-party platforms like CRM through reverse ETL. 
  • Boost Sales: Drive conversion with insights into the customer life cycle gleaned from real-time data. Avoid duplicate and out-of-date information by centralizing sales metrics. Integrates with Stripe, Mixpanel, Hubspot, Salesforce and Google Adwords. 
  • Scale Flexibly: Scale as the business grows, extend capabilities and customize as needed by connecting to RESTful services through APIs. 
  • Secure Data Pipelines: Control access permissions within the organization through SSO and 2FA. Enable 2FA on individual accounts from settings. Uses a time-based one-time-password scheme for the second-factor authentication. Request for SSO enablement and then sign in via enterprise identity provider. 
read more...
  • 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. 
read more...
  • Connectivity Library: Convey business information across locations through no-code and low-code connectors for data warehouses, cloud-based SaaS platforms and databases, including NoSQL data repositories. 
    • API Integrations: Secures the ETL process through seamless RESTful API integrations with more than 100 applications. Processes parallelly massive complex datasets; uses Hadoop MapReduce to distribute computing tasks over several server clusters. 
  • Data Replication: Replicates data in real time through change data capture (CDC) in collaboration with FlyData, a data migration system. Loads data securely to Amazon Redshift and Snowflake in a matter of minutes. 
  • Expressions: Use functions to manipulate the results output by component with the Expression Editor. Schedule packages and workflows with cron expressions. Schedule tasks around active and inactive hours, ensure that important packages are executed on time, and remove the need for manual input. 
  • Reverse ETL: Moves data from data warehouses to third-party systems, transforms it and makes it operational. Connects with other data stack components, eliminating the need to manually build API connectors. 
read more...

Product Ranking

#15

among all
ETL Tools

#26

among all
ETL Tools

Find out who the leaders are

Analyst Rating Summary

94
87
93
57
78
87
92
96
Show More Show More
Data Transformation
Metadata Management
Platform Security
Workflow Management
Data Delivery
Platform Security
Data Sources and Targets Connectivity
Data Transformation
Data Quality
Metadata Management

Analyst Ratings for Functional Requirements Customize This Data Customize This Data

Dataflow
Integrate.io
+ Add Product + Add Product
Data Delivery Data Quality Data Sources And Targets Connectivity Data Transformation Metadata Management Platform Capabilities Workflow Management 93 78 92 100 100 0 100 57 87 96 91 86 0 67 0 25 50 75 100
80%
20%
0%
60%
0%
40%
58%
25%
17%
85%
0%
15%
86%
0%
14%
96%
0%
4%
100%
0%
0%
91%
0%
9%
100%
0%
0%
88%
0%
12%
we're gathering data
N/A
we're gathering data
N/A
we're gathering data
N/A
we're gathering data
N/A
we're gathering data
N/A
we're gathering data
N/A
100%
0%
0%
70%
0%
30%

Analyst Ratings for Technical Requirements Customize This Data Customize This Data

we're gathering data
N/A
we're gathering data
N/A
we're gathering data
N/A
we're gathering data
N/A
we're gathering data
N/A
we're gathering data
N/A
100%
0%
0%
100%
0%
0%

User Sentiment Summary

Great User Sentiment 106 reviews
Great User Sentiment 153 reviews
86%
of users recommend this product

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

87%
of users recommend this product

Integrate.io has a 'great' User Satisfaction Rating of 87% when considering 153 user reviews from 4 recognized software review sites.

4.1 (31)
4.3 (115)
n/a
4.59 (17)
n/a
4.6 (16)
4.4 (59)
4.1 (5)
4.2 (16)
n/a

Awards

SelectHub research analysts have evaluated Dataflow and concluded it earns best-in-class honors for Data Transformation and Workflow Management.

Data Transformation Award
Workflow Management Award

we're gathering data

Synopsis of User Ratings and Reviews

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.
Show more
User-Friendly Interface: Visual, drag-and-drop interface makes it accessible to users of all technical levels.
Vast Connector Library: Connects to a wide range of popular data sources and destinations, eliminating the need for custom coding.
Intuitive Data Mapping: Easily map fields between sources and destinations with a visual interface, streamlining the data transformation process.
Solid Customer Support: Responsive and helpful customer support team assists with any issues that arise.
Scalability: Handles large data volumes and complex data flows, making it suitable for growing businesses.
Show more
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.
Show more
Limited Data Lineage Tracking: Lacks robust tracking of data lineage, making it challenging to trace data's journey from source to destination for auditing and compliance purposes.
High Costs for Larger Data Volumes: Pricing can become expensive for businesses with high data volumes, potentially limiting its affordability.
Occasional Performance Issues: Users have reported occasional slowdowns or lags, especially when dealing with large data sets or complex workflows.
Customization Options Can Be Limited: The pre-built connectors and functionalities might not cater to all specific needs, requiring workarounds for unique use cases.
Error Handling Could Be More Robust: Improved error reporting and troubleshooting tools would enhance user experience, making it easier to identify and resolve issues.
Show more

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.

Show more

Integrate.io shines amongst ETL tools for its user-friendliness, boasting a visual interface and vast connector library that empowers both technical and non-technical users. This accessibility is a major draw compared to competitors requiring coding expertise, saving time and resources. However, users also cite limitations in data lineage tracking and its higher cost for larger data volumes. While competitors like **Product A** may offer more granular control and customization, users praise Integrate.io's intuitive approach for everyday ETL tasks. This balance between ease of use and functionality is key for businesses needing a streamlined data flow solution without extensive technical overhead. On the other hand, those needing in-depth lineage tracking or handling massive datasets might find competitors like **Product B** more suitable despite their steeper learning curve. Ultimately, user reviews suggest Integrate.io excels in providing an accessible and efficient ETL solution for businesses prioritizing user-friendliness and a wide range of pre-built connectors. If robust data lineage tracking or affordability for massive data volumes are paramount, exploring alternative ETL tools might be necessary.

Show more

Screenshots

Top Alternatives in ETL Tools


AWS Glue

Azure Data Factory

Cloud Data Fusion

DataStage

Fivetran

Hevo

IDMC

Informatica PowerCenter

InfoSphere Information Server

Integrate.io

Oracle Data Integrator

Pentaho

Qlik Talend Data Integration

SAP Data Services

SAS Data Management

Skyvia

SQL Server

SQL Server Integration Services

Talend

TIBCO Cloud Integration

Related Categories

Head-to-Head Comparison

WE DISTILL IT INTO REAL REQUIREMENTS, COMPARISON REPORTS, PRICE GUIDES and more...

Compare products
Comparison Report
Just drag this link to the bookmark bar.
?
Table settings