Based on 4 reviews and ratings
Matillion
Based on 154 reviews and ratings
Highlights
Alooma and Matillion provide data integration capabilities for businesses. Alooma, which was purchased by Google in 2019, provides a more comprehensive platform for integrating applications and systems, as well as data flows, while Matillion specializes as a point solution for data Extraction, Transformation, and Loading (ETL).
Alooma is an Integration Platform as a Service (iPaaS), which entails an architectural foundation for connecting applications and datastreams, in this case with a specialization in integrating data sources with Google’s BigQuery data warehouse. Matillion focuses on extracting data from an original source, or many original sources, formatting the data for the end user, and storing it in a data warehouse.
Features
Alooma and Matillion both have strong features tailored to their corresponding use cases.
Alooma provides features that span a wide range of data integration needs. In particular, Alooma offers mature data mapping capabilities, which grants flexibility when dataflows change and enable non-IT users. It also specializes in loading data into Google’s BigQuery data warehouse.
Matillion’s specialization as an ETL solution allows it to excel in this capacity. Users point to Matillion’s ability to transform data from a wide range of platforms and cloud-based environments. In particular, the product is ideal for integrating AWS applications and data warehouses.
Limitations
While both products have tailored strengths, each also has tradeoffs and limitations in their offerings.
While Alooma’s transformation and broader ETL capabilities are reliable, they can be overly simplistic. Some users have experienced limitations in the platform’s single-step transformation flow. Since it’s purchase by Google, it is also more limited outside of feeding data to Google’s BigQuery data warehouse.
In contrast, while Matillion specializes in cloud-based ETL into data warehouses, it may be more limited beyond this use case. It lacks some mapping features for non-IT users, and may not offer the same security features for more complex integrations beyond ETL into data warehouses. Some organizations may experience difficulties scaling their instances of the tool as well.
Pricing
Alooma provides limited pricing information, and exact figures are available by quote from the vendor. Some dated figures place average pricing between $1,000-$1,500 per month to use the service.
Matillion provides four packages for its ETL capabilities, priced per hour of usage. The “Medium” package, at $1.27/hr, provides 2 users and 6 environments. The “Large” package, at $2.74/hr, supports 5 concurrent users and 15 environments and clustering. The “XLarge” package, at $5.48/hr, supports 12 concurrent users and 35 environments. Enterprise package pricing is available by quote from the vendor.
Provided by the TrustRadius Research Team
Published on May 15, 2020
Feature Set Ratings
Cloud Data Integration
Alooma
Matillion
Pre-built connectors
Connector modification
Support for real-time and batch integration
Data quality services
Data security features
Monitoring console
Data Source Connection
Alooma
Matillion
Connect to traditional data sources
Connecto to Big Data and NoSQL
Data Transformations
Alooma
Matillion
Simple transformations
Complex transformations
Data Modeling
Alooma
Matillion
Data model creation
Metadata management
Business rules and workflow
Collaboration
Testing and debugging
feature 1
Data Governance
Alooma
Matillion
Integration with data quality tools
Integration with MDM tools
Attribute Ratings
- Matillion is rated higher in 1 area: Likelihood to Recommend
Likelihood to Recommend
Alooma
Matillion
Likelihood to Renew
Alooma
Matillion
Usability
Alooma
Matillion
Support Rating
Alooma
Matillion
Implementation Rating
Alooma
Matillion
Product Scalability
Alooma
Matillion
Likelihood to Recommend
Alooma

Matillion

Pros
Alooma
- Transformation of data before entering the data warehouse -- allows for a consistent/understandable schema for analytics teams.
- Adding of additional data sources: Alooma has a large library of pre-built, common 3rd party vendors for which data is often needed to be ETL-ed.

Matillion
- Matillion's UI makes it easier to understand the flow of data in your data pipeline.
- Custom Python scripts make it easier to manage and manipulate variables and also to create custom functions (e.g. we use one to post messages to Slack when jobs have failed/succeeded).
- Handling failures in processes is straightforward.

Cons
Alooma
- More tools on managing the data warehouse itself. Although great on the ingest and transformation of data, more help provided by the tool on managing the outputs would be welcome.
- Increase possibilities in the transformation step, rather than single event transformations.

Matillion
- The Python script component needs a friendlier window in which to edit your scripts. The script is not searchable, and tabbing is frustrating.
- The High Availability server configuration was not working for us. It was allowing duplicate jobs to run, and causing a lot of confusion in the scheduler. Love the idea, but the implementation fell short.
- Better alerting around queued jobs would be nice. Sometimes jobs start queuing and nothing runs. Usually this is the result of a badly written job, but it would be nice to get alerts.
Pricing Details
Alooma
General
Free Trial
—Free/Freemium Version
—Premium Consulting/Integration Services
—Entry-level set up fee?
Starting Price
—Matillion
General
Free Trial
Free/Freemium Version
—Premium Consulting/Integration Services
Entry-level set up fee?
Starting Price
—Likelihood to Renew
Alooma
Matillion
Usability
Alooma
Matillion
Support Rating
Alooma
Matillion
Implementation Rating
Alooma
Matillion

Alternatives Considered
Alooma

Matillion
Scalability
Alooma
Matillion
Return on Investment
Alooma
- Allowing for additional data sources to more automation capability around monitoring the business is a huge ROI

Matillion
- Load data in Snowflake with minimal efforts. Loading data from REST API's require few hours instead of days. Effective manpower utilization
- Upscale or downscale instance size on demand which makes it a cost-effective solution
- No need to maintain and upgrade infrastructure which means no separate operational cost and downtime
