Comparing dlt (Data Load Tool) with Qlik Replicate 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 Qlik Replicate
Qlik Replicate (formerly Attunity) is a change data capture (CDC) and replication platform that moves data in real-time from databases, mainframes, and cloud sources into data warehouses, data lakes, and analytics platforms. It provides a graphical UI to configure replication tasks, automated schema change handling, and supports a wide range of sources (Oracle, SQL Server, DB2, MongoDB) and targets (Snowflake, Redshift, BigQuery, Kafka).
Pros
- High-performance CDC with minimal source impact; supports heterogeneous sources and targets.
- Automated schema change handling—table/column additions in source auto-reflected in target.
- GUI-based configuration for tasks, monitoring dashboards, and robust error handling.
- Cloud-native or on-prem installations; integrates with Qlik’s broader ecosystem (e.g., Qlik Sense).
Cons
- No built-in ELT/transformations—only replication. Users need a separate tool for data transformations.
- Enterprise pricing (per-core licensing) can be high, particularly for large-scale replication across many tables.
- Learning curve for setting up advanced replication scenarios (e.g., multi-target replication, filters).
Qlik Replicate Documentation:
What I like about Qlik Replicate
Replicate’s CDC capabilities ensure minimal latency and zero-impact on source databases. Schema changes in the source are automatically captured and propagated to targets.
What I dislike about Qlik Replicate
Licensing is expensive, and it’s focused solely on replication (no transformations). For broader ETL, additional tools are needed.
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 Qlik Replicate: 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.
Qlik Replicate
The Qlik Replicate UI provides wizards to create replication tasks quickly, monitors latency and throughput, and auto-detects schema changes. Setup for common CDC tasks is straightforward, but advanced filtering and tuning require expertise.
dlt (Data Load Tool) vs Qlik Replicate: 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.
Qlik Replicate
The licensing model is per-engine/core, often starting at $50k+/year for smaller environments. While expensive, the high reliability and low-latency replication justify cost for mission-critical use cases.
dlt (Data Load Tool) vs Qlik Replicate: 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.
Qlik Replicate
Features: CDC-based replication, automated schema drift handling, support for 100+ sources/targets (databases, mainframes, cloud), multi-target replication, and basic transformations (e.g., data type conversions). No deep transformation engine.
dlt (Data Load Tool) vs Qlik Replicate: 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.
Qlik Replicate
Users can configure advanced mapping rules, filters, and transformations (limited) via the UI or JSON configs. For deeper transforms, integrate with Qlik Compose or third-party ETL. Qlik Replicate can be automated via CLI and REST API.
Summary of dlt (Data Load Tool) vs Qlik Replicate vs Weld
| Weld | dlt (Data Load Tool) | Qlik Replicate | |
|---|---|---|---|
| Connectors | 200+ | 60+ | 100+ |
| Price | €99 / unlimited usage | Free (open-source) | Subscription/perpetual license (custom quotes; six-figure enterprise costs) |
| Free tier | No | No | No |
| Location | EU | DE | King of Prussia, PA, USA (Qlik HQ) |
| Extract data (ETL) | Yes | Yes | No |
| Sync data to HubSpot, Salesforce, Klaviyo, Excel etc. (reverse ETL) | Yes | Yes | No |
| Transformations | Yes | Yes | No |
| AI Assistant | Yes | No | No |
| On-Premise | No | Yes | Yes |
| Orchestration | Yes | No | Yes |
| Lineage | Yes | No | No |
| Version control | Yes | Yes | No |
| Load data to and from Excel | Yes | No | No |
| 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 | Yes |
| G2 Rating | 4.8 | 4.7 |
Conclusion
You’re comparing dlt (Data Load Tool), Qlik Replicate, 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..
- Qlik Replicate: features: cdc-based replication, automated schema drift handling, support for 100+ sources/targets (databases, mainframes, cloud), multi-target replication, and basic transformations (e.g., data type conversions). no deep transformation engine. . the licensing model is per-engine/core, often starting at $50k+/year for smaller environments. while expensive, the high reliability and low-latency replication justify cost for mission-critical use cases. .
- 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..
