Matillion

Score8.5 out of 10

236 Reviews and Ratings

What is Matillion?

Matillion is a data pipeline platform used to build and manage pipelines. Matillion empowers data teams with no-code and AI capabilities to be more productive, integrating data wherever it lives and delivering data that’s ready for AI and analytics.

Categories & Use Cases

Media

Screenshot of Matillion's GUI, used to orchestrate jobs with control data flow functionality, automating the ETL process.

Screenshot of where structured and semi-structured data can be prepared to create clean data sets that can be used with any BI/reporting/visualization tool of choice. Matillion reads and combines data across a target warehouse external storage, such as S3 or Blob.

Screenshot of Matillion's self-validating components, sample and row counts. If a job does fail, the warehouse queue services available with Matillion can be used get an alert to a connected email or Slack account.

Screenshot of the SQL component used to run custom scripts from within Matillion. With hundreds of pre-built connectors out of the box, Matillion can handle complex transformation needs.

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Screenshot of Matillion's GUI, used to orchestrate jobs with control data flow functionality, automating the ETL process.

Simple transformations
Simple data transformations are calculations, data type conversions, aggregations and search and replace operations
Category average: 8.9
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

Complex transformations
Complex data transformations are data normalization, advanced data parsing, etc.
Category average: 7.4
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: 7.8

Souvendra Apiah View profile

Business Intelligence Solution Architect in Information Technology at Optimum Group (201-500 employees employees)

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

Muneeb Khan

Business Intelligence Developer in Information Technology at Pur and Simple (51-200 employees employees)

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

Verified User

Engineer in Engineering (201-500 employees employees)

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

Verified User

Consultant in Information Technology (1001-5000 employees employees)

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

Verified User

Manager in Finance and Accounting (201-500 employees employees)

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

Informatica Intelligent Data Management Cloud

Qlik Talend Cloud

Fivetran

Matillion

What is Matillion?

Categories & Use Cases

Media

Key Features

Top Performing Features

Simple transformations

Data model creation

Metadata management

Areas for Improvement

Complex transformations

Testing and debugging

Collaboration

Reviews

Use Cases and Deployment Scope

Pros

Cons

Return on Investment

Usability

Other Software Used

Use Cases and Deployment Scope

Pros

Cons

Return on Investment

Usability

Alternatives Considered

Other Software Used

Use Cases and Deployment Scope

Pros

Cons

Return on Investment

Usability

Other Software Used

Use Cases and Deployment Scope

Pros

Cons

Return on Investment

Usability

Alternatives Considered

Use Cases and Deployment Scope

Pros

Cons

Return on Investment

Usability

Other Software Used

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