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Comparing Google Cloud Dataflow with Mozart Data and Weld

Carolina Russ
Carolina Russ6 min read
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What is Google Cloud Dataflow

Google Cloud Dataflow is a fully managed stream and batch processing service based on Apache Beam. It enables users to write ETL pipelines in Java or Python, which Dataflow executes on Google’s serverless infrastructure with autoscaling. It integrates natively with Pub/Sub, BigQuery, Cloud Storage, and other GCP services for end-to-end data processing.

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.
Read full review

What is Mozart Data

Mozart Data is a managed data stack provider that bundles ETL (using embedded Fivetran/Portable connectors), a fully managed Snowflake warehouse, and dbt-based transformations under one subscription—aiming to get teams from zero to insights in under an hour without coding.

Pros

  • Out-of-the-box Snowflake data warehouse with connectors and dbt transforms in one package.
  • 150+ connectors (via embedded Fivetran + Portable) configured behind the scenes so you don’t manage separate tools.
  • Very fast onboarding—your data stack is live in under an hour without any code.
  • Dedicated customer support and onboarding assistance (Mozart Assist) helps users set up and maintain pipelines.

Cons

  • Pricing includes both warehouse usage and data volume (Monthly Active Rows), so costs rise with scale—often more expensive than self-managed ELT at high volumes.
  • Less flexibility for bespoke connector logic—if a connector is missing, you must submit a request and wait for their team.
  • Smaller community and fewer third-party tutorials compared to standalone tools like Airbyte or dbt.

Mozart Data Reviews (G2):

What I like about Mozart Data

Mozart Data gave us a turnkey stack with Snowflake, connectors, and transformations all configured. We were running dashboards in under a week without DevOps overhead.

What I dislike about Mozart Data

Costs can escalate quickly with high data volumes, and adding niche connectors often requires a request to their team (no self-serve).
Read full review

What is Weld

Weld is a powerful ETL platform that seamlessly integrates ELT, data transformations, reverse ETL, and AI-assisted features into one user-friendly solution. With its intuitive interface, Weld makes it easy for anyone, regardless of technical expertise, to build and manage data workflows. Known for its premium quality connectors, all built in-house, Weld ensures the highest quality and reliability for its users. It is designed to handle large datasets with near real-time data synchronization, making it ideal for modern data teams that require robust and efficient data integration solutions. Weld also leverages AI to automate repetitive tasks, optimize workflows, and enhance data transformation capabilities, ensuring maximum efficiency and productivity. Users can combine data from a wide variety of sources, including marketing platforms, CRMs, e-commerce platforms like Shopify, APIs, databases, Excel, Google Sheets, and more, providing a single source of truth for all their data.

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.
Read full review

Google Cloud Dataflow vs Mozart Data: 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.

Mozart Data

Mozart Data abstracts away infrastructure: users pick sources via a web UI, configure destinations, and their warehouse and pipelines spin up automatically. Minimal learning curve for non-technical teams.

Google Cloud Dataflow vs Mozart Data: 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.

Mozart Data

Mozart’s bundled pricing (data volume + warehouse compute) starts at ~$1,000/month for small usage, which can be competitive for teams that value time saved over cost. However, high-volume users may find it pricier than DIY stacks.

Google Cloud Dataflow vs Mozart Data: 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.

Mozart Data

Includes managed Snowflake, automated ETL connectors (via Fivetran + Portable), a dbt transformation layer, and monitoring dashboards. Supports scheduling, incremental loads, and basic orchestrations without separate tools.

Google Cloud Dataflow vs Mozart Data: 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.

Mozart Data

While Mozart Data handles most common use cases seamlessly, it limits custom code in pipelines. Advanced users can still bring their own SQL or dbt models, but building new connectors requires raising a request—no self-serve SDK.

Summary of Google Cloud Dataflow vs Mozart Data vs Weld

WeldGoogle Cloud DataflowMozart Data
Connectors200++30+150+
Price$99 / Unlimited usagePer vCPU-second ($0.0106/vCPU-minute) + RAM and storage; streaming pipelines incur additional costsStarts around $1,000/mo (includes Snowflake + ETL up to 250k MAR)
Free tierNoNoYes
LocationEUGCP Global (multi-region)San Francisco, CA, USA
Extract data (ETL)YesYesYes
Sync data to HubSpot, Salesforce, Klaviyo, Excel etc. (reverse ETL)YesNoNo
TransformationsYesYesYes
AI AssistantYesNoNo
On-PremiseNoNoNo
OrchestrationYesNoYes
LineageYesNoNo
Version controlYesNoNo
Load data to and from ExcelYesYesYes
Load data to and from Google SheetsYesNoYes
Two-Way SyncYesNoNo
dbt Core IntegrationYesNoYes
dbt Cloud IntegrationYesNoNo
OpenAPI / Developer APIYesNoNo
G2 Rating4.84.54.6

Conclusion

You’re comparing Google Cloud Dataflow, Mozart Data, Weld. Each of these tools has its own strengths:

  • Google Cloud Dataflowfeatures 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. .
  • Mozart Dataincludes managed snowflake, automated etl connectors (via fivetran + portable), a dbt transformation layer, and monitoring dashboards. supports scheduling, incremental loads, and basic orchestrations without separate tools. mozart’s bundled pricing (data volume + warehouse compute) starts at ~$1,000/month for small usage, which can be competitive for teams that value time saved over cost. however, high-volume users may find it pricier than diy stacks. .
  • Weldweld 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..
Review the detailed sections above—connectors, pricing, feature set, and integrations—and choose the one that best matches your technical expertise, budget, and use cases.

Want to try a better alternative? Try Weld for free today.