Comparing dlt (Data Load Tool) with Google Cloud Dataflow and Weld
Carolina Russ6 min read
What is dlt (Data Load Tool)
dlt is an open-source Python library for building data pipelines with a code-first approach. It provides pre-built connectors for many common data sources and handles schema inference, incremental loading, and retry logic automatically. Developers define pipelines in Python, making it highly flexible and embeddable in any orchestration environment.
Pros
- Open-source and free to use
- High flexibility and control via Python code
- 60+ pre-built connectors with automatic schema evolution
- Built-in incremental loading and state management
- Embeddable in any orchestration (Airflow, Prefect, cron, etc.)
Cons
- No graphical UI—code-first, so not accessible to non-developers
- Requires engineering effort to deploy and schedule (no managed SaaS)
- Limited built-in transformations compared to dedicated ETL tools
- Monitoring and observability must be built around code (no native dashboard)
- Smaller community and support compared to more established tools
A reviewer on Medium:
What I like about dlt (Data Load Tool)
dlt is lightweight, customizable, and removes a lot of the boilerplate around API ingestion. With just a few lines of Python, we were able to create robust pipelines that handle schema changes and incremental loads seamlessly.
What I dislike about dlt (Data Load Tool)
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.
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.
dlt (Data Load Tool) vs Google Cloud Dataflow: Ease of Use and User Interface
dlt (Data Load Tool)
dlt has no graphical interface—pipelines are defined in Python code, making it easy for developers comfortable with code but inaccessible to non-technical users.
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.
dlt (Data Load Tool) vs Google Cloud Dataflow: Pricing Transparency and Affordability
dlt (Data Load Tool)
As an open-source library, dlt is free to use. Users only pay for the infrastructure required to run pipelines, making it highly affordable compared to paid SaaS solutions.
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.
dlt (Data Load Tool) vs Google Cloud Dataflow: Comprehensive Feature Set
dlt (Data Load Tool)
dlt provides core pipeline features: connector library, schema inference, incremental loading, and state management. It supports major destinations (Snowflake, BigQuery, Redshift, PostgreSQL, Databricks) and allows in-Python transformations or dbt integration.
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.
dlt (Data Load Tool) vs Google Cloud Dataflow: Flexibility and Customization
dlt (Data Load Tool)
Because pipelines are written in Python, dlt offers unmatched customization—developers can fetch from any API, implement custom logic, and integrate with any orchestration or monitoring framework. This flexibility requires engineering investment but allows tailor-made solutions.
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.
Summary of dlt (Data Load Tool) vs Google Cloud Dataflow vs Weld
| Weld | dlt (Data Load Tool) | Google Cloud Dataflow | |
|---|---|---|---|
| Connectors | 200+ | 60+ | 30+ |
| Price | €99 / unlimited usage | Free (open-source) | Per vCPU-second ($0.0106/vCPU-minute) + RAM and storage; streaming pipelines incur additional costs |
| Free tier | No | No | No |
| Location | EU | DE | GCP Global (multi-region) |
| Extract data (ETL) | Yes | Yes | Yes |
| Sync data to HubSpot, Salesforce, Klaviyo, Excel etc. (reverse ETL) | Yes | Yes | No |
| Transformations | Yes | Yes | Yes |
| AI Assistant | Yes | No | No |
| On-Premise | No | Yes | No |
| Orchestration | Yes | No | No |
| Lineage | Yes | No | No |
| Version control | Yes | Yes | No |
| Load data to and from Excel | Yes | No | Yes |
| Load data to and from Google Sheets | Yes | No | No |
| Two-Way Sync | Yes | No | No |
| dbt Core Integration | Yes | No | No |
| dbt Cloud Integration | Yes | No | No |
| OpenAPI / Developer API | Yes | Yes | No |
| G2 Rating | 4.8 | 4.5 |
Conclusion
You’re comparing dlt (Data Load Tool), Google Cloud Dataflow, Weld. Each of these tools has its own strengths:
- dlt (Data Load Tool): dlt provides core pipeline features: connector library, schema inference, incremental loading, and state management. it supports major destinations (snowflake, bigquery, redshift, postgresql, databricks) and allows in-python transformations or dbt integration.. as an open-source library, dlt is free to use. users only pay for the infrastructure required to run pipelines, making it highly affordable compared to paid saas solutions..
- 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. .
- 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..
