Dataflow vs Informatica PowerCenter

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

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
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Informatica PowerCenter is data integration software for enterprises of all sizes with connectors and metadata. It relies on robust business logic to deliver data on demand.

For cloud users, the vendor offers connectivity to Amazon S3, Microsoft Azure Blob Storage and Google Cloud Storage. It also reads data residing on mainframe systems using PowerCenterExchange and connects to big data platforms like Hadoop and HDFS.

The vendor enables businesses to move PowerCenter loads to the cloud. What's more, AI and automation can drive 95% of cloud upgrades if businesses opt for them. It scales to validate data quality, perform fuzzy matches and identify data anomalies in the cloud.

Pros
  • User-friendly
  • Wide range of connectors
  • Scalable and reliable
  • Robust security features
  • Integrates with Informatica suite
Cons
  • Steeper learning curve
  • Can be expensive
  • Performance issues with large datasets
  • Limited cloud app support
  • Complex to manage
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$1/250GB of Processed Data
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$5,000 Annually
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Tailored to your specific needs
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Product Assistance

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Knowledge Base
24/7 Live Support
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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. 
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  • Automate Workflows: Ditch the manual grind and leave the nitty-gritty to automation. Once set up, it performs ETL without needing the IT team.
  • Accelerate Insight: Make informed decisions based on hardcore data. Readymade connectors and a graphical UI speed up otherwise complex data integration tasks.
  • Reduce Costs: Cut the overheads due to bad data and grow your business with accurate and reliable insight. Minimize errors and related costs, thanks to its inherent data quality features. Reduce testing time by 50 to 90 percent with automation.
  • Improve Outcomes: Grab opportunities with confidence and turn them into profit-making ventures. Thanks to data quality checks, it pushes accurate and reliable data to downstream processes.
  • Streamline Decisions: Drive a data culture and encourage users to work independently with reports and dashboards. Promote decision-making across the organization.
  • Enhance Offerings: Deliver tailored products based on buyer needs and preferences. This approach combines information from several sources, giving a complete picture of how people perceive the product.
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  • 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. 
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  • Prototyping: Test data flows by building a preview version before creating the final workflow.
  • In-Production Testing: Identify issues early by checking data quality in production workflows.
  • Data Transformation: Interpret various data formats like XML, PDF, JSON and IoT data.
  • Live Analytics: Respond to market changes proactively using the latest insight, thanks to change data capture. Handles large volumes with dynamic partitioning and scalability.
  • Scalability: Handle growing data sizes, types, sources, projects and users without breaking into a sweat.
  • Collaboration: Gain visibility into where data originated and which processes it underwent before reaching the system. Analyze the possible impact of a decision before signing off on it. Keep business and IT teams on the same page with shared metadata.
  • Early Warning: Spot glitches and improper usage of resources early on. Get alerts when something goes wrong with data integration processes.
read more...

Product Ranking

#15

among all
ETL Tools

#20

among all
ETL Tools

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Analyst Rating Summary

94
96
93
100
78
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92
94
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Data Transformation
Metadata Management
Platform Security
Workflow Management
Data Delivery
Data Delivery
Data Quality
Metadata Management
Platform Security
Workflow Management

Analyst Ratings for Functional Requirements Customize This Data Customize This Data

Dataflow
Informatica PowerCenter
+ 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 0 100 100 100 94 91 100 0 100 0 25 50 75 100
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we're gathering data
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Analyst Ratings for Technical Requirements Customize This Data Customize This Data

we're gathering data
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User Sentiment Summary

Great User Sentiment 106 reviews
Great User Sentiment 773 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.

85%
of users recommend this product

Informatica PowerCenter has a 'great' User Satisfaction Rating of 85% when considering 773 user reviews from 4 recognized software review sites.

4.1 (31)
4.4 (83)
n/a
4.5 (41)
4.4 (59)
4.3 (557)
4.2 (16)
3.9 (92)

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

SelectHub research analysts have evaluated Informatica PowerCenter and concluded it earns best-in-class honors for Workflow Management.

Workflow Management Award

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.
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User-Friendly: Dragging and dropping fields and screen elements make daily data tasks simple, though building custom functions might require more effort.
Extensive Connectivity: Pre-made connectors seamlessly link to databases, files, cloud apps and legacy systems.
Data Quality Management: Built-in tools find and fix errors and duplicates in data, keeping it accurate and reliable.
Scalability: Works efficiently with massive volumes of data using parallel processing and optimization techniques, making it perfect for big businesses.
Data Security: Access controls, encryption and activity tracking protect sensitive information.
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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.
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Learning Curve: While it’s easy to perform basic tasks, mastering some advanced features and fixing complicated problems can be tricky.
Cost: It can be expensive, especially for smaller companies. Though small businesses don’t have vast data sizes, the deployment model and selected features can inflate the price.
Slowdown with Big Data: It might slow down with large data sizes. It’s advisable to ask for demos with high data loads.
Needs Tech Experts: Managing the infrastructure requires IT people and system administrators.
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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.

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Informatica PowerCenter has a reputation for easy adoption. The drag-and-drop interface makes it a breeze to move data around and connect to different sources.It seamlessly connects to cloud apps, databases, and legacy systems, saving businesses a ton of manual work. The wide range of connectors and smooth integration with other Informatica tools seem valuable enough to justify the cost.However, it isn't perfect. Compared to similar software, there’s a steeper learning curve, especially for advanced features. The cost can also be a sticking point, especially for businesses with small data loads.Performance is another area with mixed reviews. While PowerCenter has traditionally been reliable for everyday tasks, its performance can lag while handling massive amounts of data.

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Screenshots

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