You’re comparing Weld vs Alooma vs AWS Glue. Explore how they differ on connectors, pricing, and features. 
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| Feature | Weld | Alooma | AWS Glue |
|---|---|---|---|
| Core Platform | |||
| Price | $79 / 5M Active Rows | N/A (product retired; GCP service pricing applies) | $0.44 per DPU-hour (plus job runtime costs) |
| Free tier | No | No | Yes |
| Location | DK, (EU) | Sunnyvale, CA, USA (pre-acquisition) | AWS Global (multi-region) |
| Connectors & Sync | |||
| Connectors | 200+ | 100+ | 50+ |
| Extract data (ETL) | Yes | Yes | Yes |
| Sync to HubSpot, Salesforce, Klaviyo, Excel (reverse ETL) | Yes | No | No |
| Two-Way Sync | Yes | No | No |
| Transformations & AI | |||
| Transformations | Yes | Yes | Yes |
| AI Assistant | Yes | No | No |
| dbt Core Integration | Yes | No | Yes |
| dbt Cloud Integration | Yes | No | No |
| Governance & DevOps | |||
| Orchestration | Yes | Yes | Yes |
| Lineage | Yes | No | Yes |
| Version control | Yes | No | No |
| On-Premise | No | No | No |
| OpenAPI / Developer API | Yes | No | No |
| Integrations | |||
| Load to/from Excel | Yes | No | Via S3 CSV ingestion |
| Load to/from Google Sheets | Yes | No | No |
| Ratings | |||
| G2 rating | 4.8 | — | 4.1 |
Overview
Weld is a unified ELT and data activation platform that combines ingestion, modeling, transformations, orchestration, lineage, and reverse ETL in a single SaaS interface. With premium in-house–built connectors, an intuitive UI, and near real-time syncs, Weld enables both technical and non-technical users to create and manage data workflows efficiently. Weld also includes an AI assistant to support SQL modeling, generate transformations, and streamline repetitive tasks. Teams can ingest data from a wide range of sources—including marketing platforms, CRMs, databases, Google Sheets, Excel, and APIs—into their cloud data warehouse and activate it back into business tools.
Lineage, orchestration, and workflow features included by default
Handles large datasets and near real-time data sync
ELT and reverse ETL in one platform
User-friendly interface with minimal setup required
Flat, predictable monthly pricing model
200+ in-house–built, high-quality connectors
AI assistant for modeling and transformations
Some SQL knowledge is useful for advanced modeling
Optimized for cloud-warehouse workflows (Snowflake, BigQuery, Redshift, etc.)
Feature set is streamlined for modern ELT/activation use cases
A reviewer on G2 said:
Overview
Alooma (acquired by Google Cloud in 2019) was a streaming ETL platform that enabled real-time ingestion of data from various sources into BigQuery. It provided a visual pipeline editor to map, transform, and route data with minimal code, automatically handling schema changes and ensuring exactly-once delivery. While Alooma as a standalone product is retired, many of its features have been integrated into Google Cloud’s Dataflow and Pub/Sub pipelines.
Real-time streaming ETL with automatic schema drift handling.
Minimal coding: visual pipeline UI with built-in connectors to databases, Kafka, APIs, and SaaS apps.
Exactly-once delivery guarantees to BigQuery, eliminating duplicate data.
Standalone Alooma product is discontinued—functionality now lives in GCP services (e.g., Dataflow, Data Fusion).
Migrating legacy Alooma pipelines to GCP-native services requires rework, as UI and features differ from original Alooma.
Google Cloud’s Dataflow (Alooma integration):
“Alooma’s ease of connecting live streaming data sources directly into BigQuery with automated schema management was revolutionary for our real-time analytics.”
“Since Google integrated Alooma into its native services, the standalone product no longer exists, so new users must migrate to Dataflow or Data Fusion.”
Overview
AWS Glue is a fully managed, serverless ETL platform from AWS that automates data discovery, cataloging, and transformation using the Glue Data Catalog and Apache Spark (PySpark/Scala). It integrates tightly with AWS services such as S3, Redshift, RDS, and DynamoDB, and offers both batch and streaming ETL. Glue includes visual tools like Glue Studio and low-code data preparation via DataBrew, along with job scheduling, orchestration, and CloudWatch monitoring.
Serverless architecture—no infrastructure to manage; AWS automatically provisions compute (Spark-based).
Glue Data Catalog provides schema discovery, metadata storage, versioning, and integration with Athena and Redshift Spectrum.
Supports Python (PySpark) and Scala for complex ETL with transformation APIs.
Deep integration with AWS services including CloudWatch, IAM, S3 events, Step Functions, and Redshift.
Costs can be unpredictable for long-running or resource-intensive jobs due to DPU billing.
Debugging jobs can be challenging; logs spread across CloudWatch and Spark outputs.
Connecting on-prem or multi-cloud sources often requires additional networking configuration.
G2 Reviews:
“My team built a framework in AWS Glue to fetch data from multiple platforms and store it in S3 in the format we specified. It streamlined our integration and data collection.”
“It does not support XML file formats.”
Side-by-side
Weld’s interface is built for clarity and speed, enabling users with varying levels of technical experience to manage data pipelines and models efficiently. Its built-in lineage and orchestration tools provide transparency across workflows.
Alooma’s web-based pipeline builder allowed users to drag-and-drop connectors for streaming or batch data, apply transformations, and route data to BigQuery with just a few clicks. The interface auto-generated SQL when possible.
Glue Studio offers a visual job builder, while more advanced workflows require writing PySpark or Scala code. The AWS console can feel complex for users unfamiliar with AWS services.
Side-by-side
Weld’s interface is built for clarity and speed, enabling users with varying levels of technical experience to manage data pipelines and models efficiently. Its built-in lineage and orchestration tools provide transparency across workflows.
Alooma’s web-based pipeline builder allowed users to drag-and-drop connectors for streaming or batch data, apply transformations, and route data to BigQuery with just a few clicks. The interface auto-generated SQL when possible.
Glue Studio offers a visual job builder, while more advanced workflows require writing PySpark or Scala code. The AWS console can feel complex for users unfamiliar with AWS services.
Side-by-side
Weld offers a simple and predictable pricing model starting at $79 for 5 million active rows. This flat, usage-transparent structure makes budgeting straightforward for small and medium-sized teams.
No longer available as a separate product. Users adopt equivalent GCP services (Dataflow, Data Fusion) which have pay-as-you-go pricing under the GCP pricing model.
Pricing is based on DPU-hours used by ETL jobs. Short jobs can be inexpensive, but longer Spark workloads or high-concurrency environments can increase costs without careful tuning.
Side-by-side
Weld offers a simple and predictable pricing model starting at $79 for 5 million active rows. This flat, usage-transparent structure makes budgeting straightforward for small and medium-sized teams.
No longer available as a separate product. Users adopt equivalent GCP services (Dataflow, Data Fusion) which have pay-as-you-go pricing under the GCP pricing model.
Pricing is based on DPU-hours used by ETL jobs. Short jobs can be inexpensive, but longer Spark workloads or high-concurrency environments can increase costs without careful tuning.
Side-by-side
Weld provides ELT ingestion, SQL-based transformations, reverse ETL activation, data lineage, orchestration, and workflow management in a single platform. Its AI assistant accelerates modeling and transformation tasks.
Alooma supported real-time ingestion from Kafka, databases (MySQL, PostgreSQL), logs, REST APIs, and SaaS apps, with built-in transformations (masking, enrichment). It automatically handled schema changes, and could write to BigQuery partitions.
AWS Glue includes the Glue Data Catalog, PySpark/Scala ETL jobs, Glue Studio for visual development, DataBrew for low-code data preparation, Glue Workflows for orchestration, and support for both batch and streaming ETL.
Side-by-side
Weld provides ELT ingestion, SQL-based transformations, reverse ETL activation, data lineage, orchestration, and workflow management in a single platform. Its AI assistant accelerates modeling and transformation tasks.
Alooma supported real-time ingestion from Kafka, databases (MySQL, PostgreSQL), logs, REST APIs, and SaaS apps, with built-in transformations (masking, enrichment). It automatically handled schema changes, and could write to BigQuery partitions.
AWS Glue includes the Glue Data Catalog, PySpark/Scala ETL jobs, Glue Studio for visual development, DataBrew for low-code data preparation, Glue Workflows for orchestration, and support for both batch and streaming ETL.
Side-by-side
Users can model data using SQL enhanced by Weld’s AI assistant, automate workflows, and build custom connectors to any API. This provides strong flexibility for teams that want to tailor integrations and transformations within one platform.
Users could write custom JavaScript transforms or Python UDFs for complex logic. The platform managed infrastructure, but custom connectors required Eloqua code or support.
Glue supports custom PySpark ETL scripts, additional Python libraries, event-based triggers, and integration with Lambda and Step Functions. Local development is possible but limited compared to full Spark environments.
Side-by-side
Users can model data using SQL enhanced by Weld’s AI assistant, automate workflows, and build custom connectors to any API. This provides strong flexibility for teams that want to tailor integrations and transformations within one platform.
Users could write custom JavaScript transforms or Python UDFs for complex logic. The platform managed infrastructure, but custom connectors required Eloqua code or support.
Glue supports custom PySpark ETL scripts, additional Python libraries, event-based triggers, and integration with Lambda and Step Functions. Local development is possible but limited compared to full Spark environments.
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CUSTOMER STORIES
“One of the biggest impacts has been unlocking new ways to buy media. Before, we didn’t have the data to back up strategic decisions – now we do.”
“Before Weld, we had to rely on custom Python scripts and manual processes that were time-consuming and error-prone.”
“We’re still a team of three, but we’re often doing far more than the equivalent of three full-time employees. That’s down to how we're able to leverage systems, data, and processes.”
“We’ve always been a KPI-driven company. But we wanted to scale that mindset across every team member, every team, every decision.”
“Before Weld, our data infrastructure was limited and we relied heavily on Excel files and custom Python scripts.”
“Weld has saved us a ton of time, from not having data ready to having a fully functional data warehouse and connectors.”
“By integrating Weld, Amazon S3, and Databricks, Soundboks built a modern data pipeline that automates data ingestion, improves reporting, and provides up-to-date visibility into sales performance”
“We didn’t have a good data setup, so we lacked the business insights we needed. Weld has allowed us to set up a structured data infrastructure and access insights quickly.”
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