#1 most frequent
United States of America
64.2%1,000 installations of 1,558
What is Maia by Matillion?
Maia by Matillion is an autonomous data engineering platform designed to automate the lifecycle of data pipelines through AI-driven orchestration. The solution functions as an enterprise "digital workforce" that translates natural language requirements into production-ready DataPipelines, leveraging a Pushdown Architecture to execute transformations natively within cloud data warehouses.
Categories & Use Cases
Media
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Screenshot of Matillion's GUI, used to orchestrate jobs with control data flow functionality, automating the ETL process.
Top Performing Features
Simple transformations
Simple data transformations are calculations, data type conversions, aggregations and search and replace operations
Category average: 8.8
Data model creation
Ability to create and maintain data models using a graphical tool to define relationships between data
Category average: 8.4
Metadata management
Automated discovery of metadata with ability to synchronize and share metadata with other tools like Master Data Management
Category average: 7.5
Areas for Improvement
Integration with MDM tools
Integration with master data management tools to ensure data consistency across the organization
Category average: 7.2
Testing and debugging
Tool to debug and tune for optimal performance
Category average: 7.2
Collaboration
Collaboration is enabled by a shared repository of project information and metadata
Category average: 7.9
Who Buys & Uses Maia by Matillion
#2 most frequent
United Kingdom of Great Britain and Northern Ireland
6.0%94 installations of 1,558
#3 most frequent
Canada
4.6%71 installations of 1,558
Pros
- Extensive data connectors for diverse source integration
- Robust data transformation capabilities
- Intuitive user interface for simplified data processing
Cons
- Git integration stores job files as large JSONs, hindering version control
- API lacks robust features for auditing changes and accessing execution logs
- Billing model based on instance uptime rather than processing time
Streamlined ETL with Powerful No-Code Capabilities
Use Cases and Deployment Scope
We use Matillion to build, manage and control our data pipelines for our various data warehouses in Snowflake data cloud platform.
Pros
- Data connectivity between Source and Target Database
- Manage different ETL environments
- Job components for data transformation
Cons
- Handle automatic saving of Jobs differently. User should have the option to ssave or discard any changes made.
- External access to the execution log history in addition to see the logs from the UI.
Return on Investment
- Matillion ETL has allow us to build and deploy rapidly ETL workloads
- The tool is also very intuitive for ETL developer
- The diffrent pricing options are also very interesting
Usability
Other Software Used
Snowflake, Microsoft Power BI, Oracle Database
ETL made easy
Use Cases and Deployment Scope
I use Matillion to ingest data from API sources for our POS, Inventory management system, audits and Reviews platform. The business problems that I solve are relating to sales, finance, inventory count and audit scores. All the transformation for our raw data also occur inside Matillion before loading data into the data marts
Pros
- Custom API integration
- Transformation
- Orchestration
- Pipeline logs
Cons
- Change data capture
- Incremental data loading
- Custom API data types
Return on Investment
- Faster access to data marts
- Easier for handling change requests on data modelling
- Concurrent pipelines run for cost effectiveness
Usability
Alternatives Considered
Azure Data Factory and Fivetran
Other Software Used
Snowflake, Microsoft Power BI
Matillion review
Use Cases and Deployment Scope
We use Matillion ETL as an orchestrator, source ingestor and partially as a transformation tool. We utilize its connectors to query multiple sources, organize these pulls into neat interconnected flows and run initial transformations. Additionally, we use the native Git integration to have a proper development pipeline with the CI/CD flow.
Pros
- Easy to use orchestration and transformation components
- A good amount of connectors for any occasion
- Flexibility to create custom flows
Cons
- Niche limitations on some connectors
- Poorly documented connectors
- Lack of features in connection to the native Git integration
- No cache clearing options
Return on Investment
- Matillion was the reason behind our data driven company culture
- With Matillion we were able to fully utilize our data and even offer it as a service to our clients
- The cost of Matillion is offset by the convenience and need for always up-to-date data
Usability
Other Software Used
Microsoft Power BI, Snowflake, GitHub
Matillion - A great tool for building data pipelines
Use Cases and Deployment Scope
Matillion is used as the preferred ETL / ELT tool in our organization and has everything that we need for managing and maintaining our data warehouse in Snowflake. The built in connectors and integration with DevOps git makes pipeline creation very easy resulting in high productivity of Data Engineers. There is very detailed documentation available and Matillion support is very responsive. We have moved from a traditional on premise data warehouse to a cloud data warehouse and Matillion has made this transformation easy and sustainable.
Pros
- Hundreds of connectors for easy integration with data sources
- Integration with git repositories for version control and recovery of pipelines
- Really good customer support
Cons
- Major upgrades are frequent and require change freezes
- Post upgrades, certain things require reconfiguration and fixing
- Pay per hour is not the best billing mechanism as sometimes only a few minutes are needed to do some tasks
Return on Investment
- Easier access to data
- Improved productivity of Data Engineers
- Automated version control and documentation
- Faster data delivery
Usability
Alternatives Considered
SQL Server Integration Services, Apache Airflow, Alteryx Platform and Looker Studio
Matillion powers our BI stack
Use Cases and Deployment Scope
As a digital company we do have several different B2B and B2C systems. Mostly their data is about the same but as anywhere else, nothing is unified. We use Matillion in order to load all of the different source systems and transform and unify the data. It fully empowers our DWH based on AWS redshift.
Pros
- Using different type of sources
- Simplify SQL statements by providing an nice UI
- Team collaboration between the data engineers
Cons
- Template system
- Data inspection playground
Return on Investment
- Speeding up the process of having data available in the DWH
- Transparent overview for everyone what happens during the DWH build
Usability
Other Software Used
DBeaver, Tableau Desktop, Tableau Server
