Comparing Google Cloud Dataflow with Pentaho Data Integration and Weld



What is Google Cloud Dataflow
Pros
- Unified batch + streaming model via Apache Beam SDK (Java/Python).
- Serverless autoscaling with dynamic work rebalancing for cost and performance optimization.
- First-class integration with GCP services: Pub/Sub, BigQuery I/O connectors, Cloud Storage, Spanner, etc.
- Built-in exactly-once processing semantics and windowing capabilities for streaming ETL.
Cons
- Steep learning curve if unfamiliar with Apache Beam’s abstractions (PCollections, DoFns, pipelines).
- Monitoring and debugging streaming pipelines can be complex—metrics and logs often require cross-referencing.
- Cost can rise quickly for large-scale streaming (billed per vCPU-second and memory). Efficient pipeline tuning is critical.
Cloud Dataflow Documentation:
What I like about Google Cloud Dataflow
Dataflow’s unified model for batch and streaming simplifies pipeline development—write once and choose your execution mode. Autoscaling and dynamic work rebalancing ensure efficient resource use.
What I dislike about Google Cloud Dataflow
Debugging streaming jobs can be challenging; understanding Apache Beam semantics is essential. Costs can spike if pipelines aren’t carefully tuned.
What is Pentaho Data Integration
Pros
- Open-source (Community Edition) with no licensing costs; Enterprise Edition provides additional features and support.
- 150+ connectors (databases, cloud storage, big data, files, NoSQL) and flexible step-based transformations.
- Graphical Spoon interface for visual ETL job design; transformations can be previewed and tested in real-time.
- Support for clustered execution (Carte server) for parallel processing and higher throughput.
Cons
- Community Edition lacks advanced features (lineage, data quality, enterprise monitoring), requiring Enterprise Edition for production readiness.
- Performance can suffer with very large data volumes if not properly tuned (Java memory, clustering).
- User interface and user experience are dated compared to newer cloud-native ETL tools.
Pentaho Data Integration Overview:
What I like about Pentaho Data Integration
PDI’s free community edition and Spoon GUI allow rapid ETL prototyping; its step library is extensive, and clustering support is solid for scale.
What I dislike about Pentaho Data Integration
Limited data quality features and slower development speed compared to modern cloud ETL. Community support can be slow for fixes.
What is Weld
Pros
- Premium quality connectors and reliability
- User-friendly and easy to set up
- AI assistant
- Very competitive and easy-to-understand pricing model
- Reverse ETL option
- Lineage, orchestration, and workflow features
- Advanced transformation and SQL modeling capabilities
- Ability to handle large datasets and near real-time data sync
- Combines data from a wide range of sources for a single source of truth
Cons
- Requires some technical knowledge around data warehousing and SQL
- Limited features for advanced data teams
A reviewer on G2 said:
What I like about Weld
First and foremost, Weld is incredibly user-friendly. The graphical interface is intuitive, which makes it easy to build data workflows quickly and efficiently. Even with little experience in SQL and pipeline management, we found that Weld was straightforward and easy to use. What really impressed me, however, was Weld's flexibility. It was able to handle data from a wide variety of sources, including SQL databases, Google Sheets, and even APIs. The solution also allowed us to customize my data transformations in a way that best suited my needs. Whether I needed to clean data, join tables, or aggregate data, Weld had the necessary tools to accomplish the task. Weld's performance was also exceptional. I was able to run large-scale ETL jobs quickly and efficiently, with minimal downtime via a Snowflake instance and visualization via own-hosted Metabase. The solution's scalability meant that I could process more data without any issues. Another standout feature of Weld was its support. I never felt lost or unsure about how to use a particular feature, as the support team was always quick to respond to any questions or concerns that I had. Overall, I highly recommend Weld as an ETL solution. Its user-friendliness, flexibility, performance, and support make it an excellent choice for anyone looking to streamline their data integration processes. I will definitely be using Weld for all my ETL needs going forward.
What I dislike about Weld
Weld is still limited to a certain number of integrations - although the team is super interested to hear if you need custom integrations.
Google Cloud Dataflow vs Pentaho Data Integration: Ease of Use and User Interface
Google Cloud Dataflow
Dataflow pipelines are defined programmatically in Java or Python (Apache Beam). There is no drag-and-drop UI; developers use the Cloud Console or CLI to monitor, but pipeline creation and debugging happen in code and SDKs.
Pentaho Data Integration
Pentaho’s Spoon GUI uses a canvas paradigm: drag "steps" onto a transformation, connect them, and configure. While powerful, it can feel clunky, especially for very complex flows with many steps.
Google Cloud Dataflow vs Pentaho Data Integration: Pricing Transparency and Affordability
Google Cloud Dataflow
Charges for each pipeline based on vCPU-second, memory, and persistent disk usage. Streaming jobs are billed continuously. Without careful optimization (autoscaling, batching), costs can escalate. However, for high-throughput workloads, serverless autoscaling can be cost-effective versus self-managed clusters.
Pentaho Data Integration
The free Community Edition is attractive for experimentation. Enterprise Edition pricing is usage-based and includes support, lineag, and more; typically suited for mid-sized to large organizations.
Google Cloud Dataflow vs Pentaho Data Integration: Comprehensive Feature Set
Google Cloud Dataflow
Features include: Batch & streaming unified model, windowing & triggers, exactly-once semantics, dynamic work rebalancing, and data-driven autoscaling. Supports FlexRS (spot pricing for batch) and integration with Dataflow SQL for SQL-based pipelines.
Pentaho Data Integration
PDI features: GUI-based transformation designer, job orchestration, data cleansing, lookups, joins, scripting (JavaScript, PDI’s built-in “User Defined Java Expression”), logging, clustering, and integration with Pentaho BI for reporting. Lineage and monitoring in Enterprise.
Google Cloud Dataflow vs Pentaho Data Integration: Flexibility and Customization
Google Cloud Dataflow
Users write custom transforms (ParDo, Map, GroupBy), can integrate UDFs, and use side inputs. Complex workloads requiring custom logic (stateful processing, custom connectors) are fully supported via Beam SDK. Cloud features like VPC, IAM, and KMS integrate security.
Pentaho Data Integration
Users can embed Java, JavaScript, or invoke external scripts. PDI’s open architecture allows custom plugins for new steps/connectors. The code is open-source, so full extensibility is available, though it requires Java development.
Summary of Google Cloud Dataflow vs Pentaho Data Integration vs Weld
Weld | Google Cloud Dataflow | Pentaho Data Integration | |
---|---|---|---|
Connectors | 200++ | 30+ | 150+ |
Price | $99 / Unlimited usage | Per vCPU-second ($0.0106/vCPU-minute) + RAM and storage; streaming pipelines incur additional costs | Community Edition: Free; Enterprise Edition: Custom pricing |
Free tier | No | No | Yes |
Location | EU | GCP Global (multi-region) | Santa Clara, CA, USA (Hitachi Vantara HQ) |
Extract data (ETL) | Yes | Yes | Yes |
Sync data to HubSpot, Salesforce, Klaviyo, Excel etc. (reverse ETL) | Yes | No | No |
Transformations | Yes | Yes | Yes |
AI Assistant | Yes | No | No |
On-Premise | No | No | Yes |
Orchestration | Yes | No | Yes |
Lineage | Yes | No | Yes |
Version control | Yes | No | Yes |
Load data to and from Excel | Yes | Yes | Yes |
Load data to and from Google Sheets | Yes | No | Yes |
Two-Way Sync | Yes | No | No |
dbt Core Integration | Yes | No | No |
dbt Cloud Integration | Yes | No | No |
OpenAPI / Developer API | Yes | No | No |
G2 Rating | 4.8 | 4.5 | 4.1 |
Conclusion
You’re comparing Google Cloud Dataflow, Pentaho Data Integration, Weld. Each of these tools has its own strengths:
- Google Cloud Dataflow: features include: batch & streaming unified model, windowing & triggers, exactly-once semantics, dynamic work rebalancing, and data-driven autoscaling. supports flexrs (spot pricing for batch) and integration with dataflow sql for sql-based pipelines. . charges for each pipeline based on vcpu-second, memory, and persistent disk usage. streaming jobs are billed continuously. without careful optimization (autoscaling, batching), costs can escalate. however, for high-throughput workloads, serverless autoscaling can be cost-effective versus self-managed clusters. .
- Pentaho Data Integration: pdi features: gui-based transformation designer, job orchestration, data cleansing, lookups, joins, scripting (javascript, pdi’s built-in “user defined java expression”), logging, clustering, and integration with pentaho bi for reporting. lineage and monitoring in enterprise. . the free community edition is attractive for experimentation. enterprise edition pricing is usage-based and includes support, lineag, and more; typically suited for mid-sized to large organizations. .
- Weld: weld integrates elt, data transformations, and reverse etl all within one platform. it also provides advanced features such as data lineage, orchestration, workflow management, and an ai assistant, which helps in automating repetitive tasks and optimizing workflows.. weld offers a straightforward and competitive pricing model, starting at $99 for 2 million active rows, making it more affordable and predictable, especially for small to medium-sized enterprises..