What I like about Alooma
“Alooma’s ease of connecting live streaming data sources directly into BigQuery with automated schema management was revolutionary for our real-time analytics.”
Comparing Alooma with Estuary and Weld
You’re comparing Alooma vs Estuary vs Weld. Explore how they differ on connectors, pricing, and features. 
Loved by data teams from around the world
Weld vs Alooma vs Estuary
| Weld | Alooma | Estuary | |
|---|---|---|---|
| Connectors | 200+ | 100+ | 200+ |
| Price | $99 / 5M Active Rows | N/A (product retired; GCP service pricing applies) | $0.50/GB consumed + per-connector fee |
| Free tier | |||
| Location | EU | Sunnyvale, CA, USA (pre-acquisition) | New York, NY, USA |
| Extract data (ETL) | |||
| Sync to HubSpot, Salesforce, Klaviyo, Excel (reverse ETL) | |||
| Transformations | |||
| AI Assistant | |||
| On-Premise | |||
| Orchestration | |||
| Lineage | |||
| Version control | |||
| Load to/from Excel | |||
| Load to/from Google Sheets | |||
| Two-Way Sync | |||
| dbt Core Integration | |||
| dbt Cloud Integration | |||
| OpenAPI / Developer API | |||
| G2 rating | 4.8 | — | 4.8 |
Overview
Alooma in Short
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.
Pros
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.
Cons
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.
Reviews & Quotes
Google Cloud’s Dataflow (Alooma integration):
What I dislike about Alooma
“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
Estuary in Short
Estuary Flow is a real-time ETL/ELT and data integration platform for both batch and streaming pipelines. It provides sub-100ms latency using Change Data Capture (CDC), supports automated schema evolution, and allows users to build entire pipelines with low- or no-code connectors in minutes. It can target data warehouses (e.g., Snowflake, BigQuery), BI tools, and operational systems for analytics, operations, and AI use cases.
Pros
Purpose-built for real-time CDC and streaming ETL with sub-100ms latency.
Automatic schema evolution with exactly-once delivery guarantees.
200+ no-code connectors for databases, SaaS apps, and message queues.
Flexible deployment: public cloud, private cloud, or self-hosted (BYOC).
Cons
Premium pricing model ($0.50/GB consumed + connector fees) can be expensive for small teams.
Still growing connector catalog; niche or very new APIs may require custom work.
Smaller community compared to older open-source tools, meaning fewer community-built resources.
Reviews & Quotes
Estuary Pricing Page:
What I like about Estuary
“Estuary’s real-time, no-code model is magical—getting data instantly with minimal effort and near-zero pipeline maintenance. Plus, their support is fantastic.”
What I dislike about Estuary
“Pricing can be high for lower-volume teams, and some less-common connectors are still in development, which limits immediate use cases for niche sources.”
Overview
Weld in Short
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
Lineage, orchestration, and workflow features
Ability to handle large datasets and near real-time data sync
ETL + reverse ETL in one
User-friendly and easy to set up
Flat monthly pricing model
200+ connectors (Shopify, HubSpot, etc.)
AI assistant
Cons
Requires some technical knowledge around data warehousing and SQL
Limited features for advanced data teams
Focused on cloud data warehouses
Reviews & Quotes
A reviewer on G2 said:
What I like about Weld
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.”
Feature-by-Feature Comparison
Feature
Alooma
Estuary
Weld
Feature
Alooma
Estuary
Weld
Ease of Use & Interface
Side-by-side
Alooma
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.
Estuary
Estuary’s UI is intuitive: users can add connectors, configure CDC streams, and specify destinations in a few clicks. Complex transformations can be written in SQL or TypeScript directly in the Flow editor, but most tasks are handled via no-code connectors.
Weld
Weld is highly praised for its user-friendly interface and intuitive design, which allows even users with minimal SQL experience to manage data workflows efficiently. This makes it an excellent choice for smaller data teams or businesses without extensive technical resources.
Ease of Use & Interface
Side-by-side
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.
Estuary’s UI is intuitive: users can add connectors, configure CDC streams, and specify destinations in a few clicks. Complex transformations can be written in SQL or TypeScript directly in the Flow editor, but most tasks are handled via no-code connectors.
Weld is highly praised for its user-friendly interface and intuitive design, which allows even users with minimal SQL experience to manage data workflows efficiently. This makes it an excellent choice for smaller data teams or businesses without extensive technical resources.
Pricing & Affordability
Side-by-side
Alooma
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.
Estuary
While Estuary provides a 10 GB/month free tier and a 30-day trial, its consumption-based pricing ($0.50/GB + connector fees) can become costly at scale. Teams processing hundreds of GBs per month should budget accordingly.
Weld
Weld offers a straightforward and competitive pricing model, starting at $79 for 5 million active rows, making it more affordable and predictable, especially for small to medium-sized enterprises.
Pricing & Affordability
Side-by-side
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.
While Estuary provides a 10 GB/month free tier and a 30-day trial, its consumption-based pricing ($0.50/GB + connector fees) can become costly at scale. Teams processing hundreds of GBs per month should budget accordingly.
Weld offers a straightforward and competitive pricing model, starting at $79 for 5 million active rows, making it more affordable and predictable, especially for small to medium-sized enterprises.
Feature Set
Side-by-side
Alooma
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.
Estuary
Key features include real-time CDC (sub-100ms latency), batch and streaming pipelines, automated schema evolution, and in-stream or post-load transformations via SQL/TypeScript or dbt. It also supports Kafka-compatibility and private storage for data replay.
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.
Feature Set
Side-by-side
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.
Key features include real-time CDC (sub-100ms latency), batch and streaming pipelines, automated schema evolution, and in-stream or post-load transformations via SQL/TypeScript or dbt. It also supports Kafka-compatibility and private storage for data replay.
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.
Flexibility & Customization
Side-by-side
Alooma
Users could write custom JavaScript transforms or Python UDFs for complex logic. The platform managed infrastructure, but custom connectors required Eloqua code or support.
Estuary
Estuary allows custom TypeScript transforms in-stream or SQL in-destination. Pipelines can be managed via CLI (flowctl) and integrated into CI/CD. While most connectors are no-code, custom connectors can be built using the open-source Flow SDK.
Weld
Weld offers advanced SQL modeling and transformations directly within its platform with the help of AI, providing users with unparalleled control and flexibility over their data. Leveraging its powerful AI capabilities, Weld automates repetitive tasks and optimizes data workflows, allowing teams to focus on getting value and insights. Additionally, Weld's custom connector framework enables users to build connectors to any API, making it easy to integrate new data sources and tailor data pipelines to meet specific business needs. This flexibility is particularly beneficial for teams looking to customize their data integration processes extensively and maximize the utility of their data without needing external tools.
Flexibility & Customization
Side-by-side
Users could write custom JavaScript transforms or Python UDFs for complex logic. The platform managed infrastructure, but custom connectors required Eloqua code or support.
Estuary allows custom TypeScript transforms in-stream or SQL in-destination. Pipelines can be managed via CLI (flowctl) and integrated into CI/CD. While most connectors are no-code, custom connectors can be built using the open-source Flow SDK.
Weld offers advanced SQL modeling and transformations directly within its platform with the help of AI, providing users with unparalleled control and flexibility over their data. Leveraging its powerful AI capabilities, Weld automates repetitive tasks and optimizes data workflows, allowing teams to focus on getting value and insights. Additionally, Weld's custom connector framework enables users to build connectors to any API, making it easy to integrate new data sources and tailor data pipelines to meet specific business needs. This flexibility is particularly beneficial for teams looking to customize their data integration processes extensively and maximize the utility of their data without needing external tools.
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