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Matillion

Matillion

Overview

What is Matillion?

Matillion is a productivity platform for data teams.Matillion's Data Productivity Cloud helps data teams – coders and non-coders alike – to move, transform, and orchestrate data pipelines with the goal of empowering teams to deliver quality data at a speed…

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Recent Reviews

in my opinion, Meh

1 out of 10
March 29, 2024
Incentivized
We are moving away from Matillion to MWAA Airflow with dbt.
Maintaining source control in github is important to us.

We have used Matillion …
Continue reading

Matillion - Decent

6 out of 10
March 19, 2024
Incentivized
We use Matillion to schedule and run our ETL jobs. This helps our company to have accurate and timely data in order to make data-driven …
Continue reading
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Awards

Products that are considered exceptional by their customers based on a variety of criteria win TrustRadius awards. Learn more about the types of TrustRadius awards to make the best purchase decision. More about TrustRadius Awards

Popular Features

View all 11 features
  • Connect to traditional data sources (127)
    7.5
    75%
  • Simple transformations (129)
    6.6
    66%
  • Complex transformations (128)
    5.4
    54%
  • Testing and debugging (114)
    4.4
    44%

Reviewer Pros & Cons

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Pricing

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N/A
Unavailable

What is Matillion?

Matillion is a productivity platform for data teams. Matillion's Data Productivity Cloud helps data teams – coders and non-coders alike – to move, transform, and orchestrate data pipelines with the goal of empowering teams to deliver quality…

Entry-level set up fee?

  • No setup fee
For the latest information on pricing, visithttps://www.matillion.com/pricing

Offerings

  • Free Trial
  • Free/Freemium Version
  • Premium Consulting/Integration Services

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Features

Data Source Connection

Ability to connect to multiple data sources

7.2
Avg 8.3

Data Transformations

Data transformations include calculations, search and replace, data normalization and data parsing

6
Avg 8.2

Data Modeling

A data model is a diagram or flowchart that illustrates the relationships between data

6.8
Avg 8.1

Data Governance

Data governance is the practise of implementing policies defining effective use of an organization's data assets

8.2
Avg 8.3
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Product Details

What is Matillion?

Matillion is a productivity platform for data teams.

Matillion's Data Productivity Cloud helps data teams – coders and non-coders alike – to move, transform, and orchestrate data pipelines with the goal of empowering teams to deliver quality data at a speed and scale that matches the business’s data ambitions.

The vendor states enterprises including Cisco, DocuSign, Pacific Life, Slack, and TUI use Matillion to move, transform, and orchestrate their data for a wide range of use cases from insights and operational analytics, to data science, machine learning, and AI.

Native integration with popular cloud data platforms such as Snowflake, Databricks, Amazon Redshift and Google BigQuery lets data teams at every skill level automate management, refinement, and data delivery for every data integration need.


Matillion Features

Data Source Connection Features

  • Supported: Connect to traditional data sources
  • Supported: Connecto to Big Data and NoSQL

Data Transformations Features

  • Supported: Simple transformations
  • Supported: Complex transformations

Data Modeling Features

  • Supported: Business rules and workflow
  • Supported: Collaboration
  • Supported: Testing and debugging

Matillion Screenshots

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.

Matillion Technical Details

Deployment TypesSoftware as a Service (SaaS), Cloud, or Web-Based
Operating SystemsUnspecified
Mobile ApplicationNo
Supported CountriesGlobal
Supported LanguagesEnglish

Frequently Asked Questions

Reviewers rate Data model creation and Metadata management highest, with a score of 9.1.

The most common users of Matillion are from Mid-sized Companies (51-1,000 employees).
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Comparisons

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Reviews and Ratings

(211)

Attribute Ratings

Reviews

(1-25 of 130)
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Score 8 out of 10
Vetted Review
Verified User
Incentivized
We use Matillion to transfer data between our production PostgreSQL databases and our Snowflake data warehouse. This robust integration ensures efficient data migration and significantly enhances our data management processes. By leveraging Snowflake's advanced analytics and storage capabilities, we then gain deeper insights and make more informed strategic business decisions, driving overall growth.
  • Excellent GUI to build data flows
  • Flows backed up to GitHub for versioning
  • Once set up it's full automated and so far no failures
  • Perhaps some modernisation of the interface
  • More granular cost reporting
It's built to do the job we need: to extract data from standard databases, transform it as we need, and load it into a data warehouse. The ETL process is posisble to use by non enginners and ensures our data is accurate and up-to-date, untimately enabling us to set it once and leave the processes to run automatically .
Score 3 out of 10
Vetted Review
Verified User
Incentivized
We used Matillion for a little more than a year to ingest and historize data coming from SaaS tools and internal databases to a Snowflake datawarehouse. This data was then further transformed to build business actionnable data assets modeling the performance and operations of the company at large. Those assets help stakeholders across the business in taking data driven decisions
  • Salesforce native connector to ingest its data to Snowflake
  • Postgres native connector to ingest its data to Snowflake
  • Workday connector to ingest its data to Snowflake
  • In my experience, no code user interface is painful to use
  • In my experience, tool works for very simple use cases but doesn't support implementing complex workflows
  • In my experience, no testing capabilities
  • In my opinion, has the downsides of a SaaS (cost, low flexibility) and the downsides of self hosted (has to be hosted on customer infrastructure, handle updates...)
  • In my experience, unreactive customer service
  • In my experience, doesn't scale to high volumes of data
  • In my opinion, high maintenance costs of no code pipelines
Matillion is well suited for very straightforward data ingestion use cases from a system for which Matillion has a native connector or a simple REST API. It can prove useful in a team with low engineering skills thanks to its no code interface.
It is not appropriate for more complex use cases and teams which have engineering skills.
Diana Pilzer | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User
Incentivized
Matillion powers our team of 2 data engineers to manage the database of our entire company. Without Matillion there is no way only 2 people would be able to do this. It helps us be more productive and get to the projects we really need instead of spending all our time deep in legacy code for every single process update.
  • Scheduled jobs
  • Transforming Data
  • Customer Support
  • API ingestion
  • Change management / historic change log
Great for simple data transfers and formatting. Not so good with data that constantly changes or requires a more dynamic approach.
Score 8 out of 10
Vetted Review
Verified User
Incentivized
We have a data and analytics team at the tech company. We're building a data warehouse/datamarts on Snowflake to produce business-critical reports for our clients. We use Matillion as an ETL tool and have some Matillion jobs to transform the source data into a presentable target for our logical data model.
  • easy to develop ETL or ELT.
  • Possibility of creating templates with its strong parametric structure.
  • ready-made components.
  • Matillion support.
  • APIs.
  • poor git integration.
  • Having some bugs (eg, the Table creation component doesn't support the CURRENT_DATE() function).
  • Expensive
Matillion is very easy to learn and develop quickly. It also allows complex orchestration on a visual platform. It has many ready-made components, and it is easy to develop new user components. Although Git integration needs some further development, it enables teamwork in the same project with the "Versions."
Score 4 out of 10
Vetted Review
Verified User
Incentivized
Matillion ETL has been used in my company for a few years to run data ingestion and transformation pipelines that populate the main analytical DWH and send data to other systems like Salesforce or the internal customer support tools. In particular, millions of ingestion components are used to integrate with various external APIs, and Matillion Redshift transformations are used to reshape the data in a data model that makes sense for analytics.
  • Ingestion of data from popular systems (Salesforce, Trustpilot.
  • Orchestration of jobs with complex decision flows (retries, parameterized jobs, conditional flows).
  • Error notifications.
  • Git integration is limited and cumbersome.
  • GUI-based data transformation makes it very hard to apply good engineering practices to data pipelines.
  • Web-based development (there is no offline version) introduces a single point of failure for developers' interaction with the platform.
Matillion is ok for orchestrating pipelines requiring complex control flows, like retries, parameterized jobs, conditional branches, etc. I have good experience in using it to launch docker containers on Docker Swarm or Kubernetes, delegating the data-heavy lifting to the application running in the container. Another good experience is with integration components offered out of the box: Google Spreadsheet ingestion, Salesforce ingestion reverse ETL and others. These save a lot of development time devoted to implementing the custom connector. Instead, I wouldn't recommend using the data transformation components, which generate SQL code starting from GUI-based configuration: they make it very hard to maintain the transformation logic (find and replace are impossible, DRY is hard).
Score 7 out of 10
Vetted Review
Verified User
Incentivized
We use Matillion mainly as an orchestration tool to stage data from multiple sources and some light transformation. We also use it to export data to S3 leveraging Snowflake.
The GUI is intuitive, and the web interface helps to be up and running very quickly.
We have some issue related to the resources needed for some jobs, there's no visibility of the system resources used or auto-balancing of some activity to avoid the server to crash.
We don't like the billing mechanism being a cost based on the server CPUS because we host the server so we are already paying for it.
We would prefer a billing mechanism decoupled from the server resources for many reasons including the fact that it crashes on some jobs due to memory issue and upgrading the server would double the bill instantly for the same jobs. It doesn't scale naturally.
Few years ago when we started using it, it was a great player in the cloud ELT world, today it is suffering that while the interface is web, the engine itself is still monolithic and static, hard to migrate and move to a new machine.

We will be looking for other tools that have the creation of the data workflows and the actual scheduling/execution decoupled so that you can use a central hub to plan and create the logic and then decide in which region/sesrver to run them without having to worry about a full server being installed in every region.

Docker/kubernetes comes to mind, but implemented and managed effortlessly behind the scenes. We don't want to deal with it, just use the tool.
  • Web interface is good enough
  • Set of built in components available for orchestration/transformation
  • Integration with target database (Snowflake for us)
  • Static and monolithic, it will show its limits when running multiple concurrent jobs.
  • Github and versioning implementation is messy and broken. Don't use it.
  • There's not way to see/query the system resources, just wait for a server to crash due to out of memory. An admin panel would be appreciated + some env variables with updated info.
  • API implementation is cumbersome and limited.
  • There's no concept of hub and worker engine, everything happens of the same server (designing workflows and executing them). Having separate light ETL engines to run job could be better. (sort of docker/kubernetes/lambda functions).
  • Handling of variables is limited especially for returned values from sub components.
  • Some components could return more metadata at the end of their execution instead of the standard one.
  • Billing is badly designed not taking into account that the server is hosted by the client. Expensive.
  • We had several issue with migration where starting a new instance was required and then migrating the content. It was painful and time consuming also have to deal with support and engineering team on Matillion side.
  • CDC doesn't work as expected or it is not a mature product yet.
If the target dwh is one of the big players it could be a good option.
Can retrieve data from multiple different sources and handle them internally.
Expensive and being hosted by the client there's also the infrastructure burden of maintaining/paying for the server.

Considering the resources needed hence the license cost that scales with them (despite the fact that you host and pay for them already) I wouldn't suggest the tool to a small company and, once you are big enough you probably want to jump on bigger more mature tools.

Matillion is a nice niche player with some nice to have feature that are probably suited for a mid-size company with some money available to pay for the license but still a small infrastructure that just require one/two Matillion servers installed.

A global company with multi regions needs will drown under the burden of handling/updating/maintaining all the servers independently and pay for the cost of each one of them.
March 29, 2024

in my opinion, Meh

Score 1 out of 10
Vetted Review
Verified User
Incentivized
We are moving away from Matillion to MWAA Airflow with dbt.
Maintaining source control in github is important to us.

We have used Matillion in the past for:
Replication - Copy from postgres, load to s3, perform transformation in redshift
Running python scripts
S3 data transfers - bucket to bucket


  • graphical user interface
  • Moving around widgets
  • options for out of box operations
  • connections
  • source control maintenance (sync w/ github)
  • poor logging, in my experience, can't see clearly what error is if something fails
  • in my experience, difficult to connect with outside tooling
Maybe for someone just starting out
Score 9 out of 10
Vetted Review
Verified User
Incentivized
We use Matillion ETL (extract, transform, and load) with Bigquery to transform customer data to a standard format. There are 100+ sources and file formats are different. We use Matillion to transform these files and apply business logic to create and store it in a standard format which can be used by downstream and we deal with terra bytes of data on a daily basis.
  • The jobs logging UI is very unique and helps in easy debugging
  • It has a proper hierarchical structure. One can easily organise projects and related pipelines
  • Access control and sharing necessary access is easy and quick
  • I have seen good performance even with complex pipelines
  • More features should be available with Git integration such as passing environment variables, schedules from git
  • Need improvement in parallelism of job runs
  • Sometimes cancelling a job gets stuck which can be improved
Matillion is well suited for building simple and complex pipelines. If you are a developer, it is a really great choice to look at sample data previews and detailed logs. It can understand the tasks with minimal configurations. One can easily import or export tasks and can code independently without affecting others work with 'manage version' option. Git configuration and Matillion APIs also available
Score 7 out of 10
Vetted Review
Verified User
Incentivized
Matillion is used to build the data pipeline on the Google Cloud platform. There are multiple use cases: 1. Data is received in GCS storage and picked by Matillion based on the active sensors and on a specific schedule. Data is transformed and then loaded into big query tables. 2. Data is read from HTTP endpoints, transformed using Matillion, and then loaded into big query tables. 3. The data quality framework is built on top of Matillion to apply some business checks.
  • Matillion has a rich transformation library. It provides multiple functionalities, such as join, group by, pivot, various sources, and sinks.
  • It provides the security capability as well. All the credentials can be securely stored in Matillion.
  • Reusable templates can be built which reduces the redundancy.
  • Time to production is very minimal.
  • There is a scope of making it more developer friendly in terms of reusability.
  • Many configurations required to set up the Matillion pipeline can be reduced significantly.
  • In built python executable is quite slow.
It is very well suited for ETL on the cloud. Whenever there is something that can be accomplished with no code or little code, Matillion is a good tool. However, if your pipeline requires a lot of customizations, Matillion should be avoided.
Score 9 out of 10
Vetted Review
Verified User
Incentivized
We use Matillion as our ETL/ELT tool to sync analytical data from various sources and tools to our data warehouse.
  • Easy to use interface - even for users without data engineering background.
  • Many native components that do not require any "programming".
  • Powerful generic profile creation to easily extract data from any API.
  • Minor topic: adjusting a generic API Query profile can be a bit tricky. It would be great if we had proper EDIT functionality/flow.
Our team consists of analytical and technical-minded people who do not have a deep background in data engineering. Matillion enables us to manage all ETL/ELT jobs independently without dedicated engineering resources.
March 19, 2024

Matillion - Decent

Score 6 out of 10
Vetted Review
Verified User
Incentivized
We use Matillion to schedule and run our ETL jobs. This helps our company to have accurate and timely data in order to make data-driven decisions. Matillion runs and organizes almost all of our data.
  • Scheduling ETL jobs
  • Third party API connection components
  • Not enough third party API integrations
  • No version control easily usable
Matillion is well suited for the non-technical data user, because it has out of the box third party API integrations. It is well suited for scheduling daily jobs to run. It is less appropriate when you want to store your ETL code in an environment that has version control and QA sign-off
Score 7 out of 10
Vetted Review
Verified User
Incentivized
Matillion ETL is used to extract, transform and load data into the Data Warehouse in Snowflake from data sources like Oracle databases and external CSV files. Matillion is used both for managing, scheduling and controlling the ETL processes as well as the data loading and transformations.
  • Executing Snowflake scripts
  • Oracle Database Connection and Data Retrieval
  • Low Code programming by setting properties of the different components
  • Parameter Passing between jobs
  • New components for supporting other programming languages like R
  • Upgrade Send Email component with more features
  • Upgrade GIT Integration features
Data loading from Oracle Databases to Snowflake Data Transformation within Snowflake Database
Score 8 out of 10
Vetted Review
Verified User
Incentivized
Matillion perfectly solves the issue of multiple data sources from different technology stacks and formats that need orchestrating into meaningful views of the data to then allow proper data mining. This then means we can provide the insights and trends the individual and group leadership needs in order to run the business in an adaptive and agile way. The technical team also appreciates the care given to building a UX that is easy to follow compared to other ETL tools used in the past.
  • Combines multiple data sources easily
  • Reports and alerts issues effectively
  • Customer service of professional
  • Has been super stable for us over the last 2+ years
  • Billing is confusing
  • UX is good but isn't the best on the market
  • Detailed help sometimes hard to find
Very flexible and scaleable to a point but I wonder if there are limits with larger data sets and a higher number of data sources. We are a technical team but less technical people may prefer a more wizard-based approach. Splitting out billing across multiple clients doesn't look possible. (Snowflake can do this)
Score 10 out of 10
Vetted Review
Verified User
Incentivized
We use Matillion as an ELT tool to take data from MongoDB and transfer it into Snowflake.

We have over 50 tables being transferred daily and several are multi millions of records and tens or gigabytes.
  • Extracts Nested JSON
  • Had good DB support for multiple products.
  • Runs in Azure where your Cloud is (may be).
  • Upgrade process is sometimes quirky with no updates listed when there are clearly newer versions.
  • Support is sometimes long winded and multiple people have to get involved.
  • Docs could be expanded as not enough installed base to make forums or Google results useful in many cases.
Very well suited to JSON documents and un-nesting arrays / objects.

Easy to get started and self learn, which is important.
Callum O'Connor | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Incentivized
We needed a way of empowering people with data in a no-code way. Data is a bottleneck and everyone needs it, but how do we give them this data and enable them to use it effectively? We launched ‘Data as a Product’ - A package of tools and resources that exposes raw data to people and allows them to manipulate it with ease. Matillion is the key tool in this package that allows people to manipulate data at the speed of thought with no code. Matillion’s no-code design, intuitive interface, and collaboration centric architecture allowed us to get people set up and transforming data in a matter of minutes with very little support from the Data team.
  • We leveraged Matillion’s no-code principals to make data manipulation easy for our internal customers. People who don't know how to use SQL no longer need to. Everything in Matillion is self-explained with no or little coding.
  • We connected Matillion to our data warehouse to allow people to read raw data, transform it, then write results back to their sandbox databases. The drag and drop component design allowed customers to create complex data models at the speed of thought without any risk to production data.
  • With sharing capabilities between projects enabled, everyone was able to help each other when questions arose which instilled a strong sense of collaboration and community.
  • The new DPC version of Matillion uses Git principals like Commit, Push, Merge etc. This is perfectly fine for engineers, but for our use-case it means our customers will need to understand a basic level of Git. It would be great if they had an auto-commit-push setting which does it all for them.
Matillion lowers the bar for entry into the world of data transformation. it's low-code design, simple drag'n'drop visualisation and SQL code generator makes data transformation easy and educational for those starting off in the world of data. For refactoring, we’re able to take existing complex SQL models, re-create and visualise them in Matillion, change the design, then use the SQL output from Matillion to create new data models in our repos.
Matillion has completely changed the way we serve data to our internal customers at the company; we've exported capability and empowerment rather than allowing ourselves to get swamped with tickets and requests for every possible data question. It's a great tool for getting people to self-serve their own answers to questions about their data.
Score 10 out of 10
Vetted Review
Verified User
Incentivized
We use Matillion as the main ETL tool for our Data warehouse. Our daily jobs extract and transform data from sources such as Dynamics CRM, several APIs, and tracking data from our websites.
  • ETL
  • API Integrations
  • Speed of processing
  • Improved data quality monitoring
  • Maybe the UI could do with a makeover, it feels a bit old (but still nice though :-) )
  • Easier integration with Slack, etc.
Matillion provides a very intuitive way of handling all types of ETL processes. There are tons of built-in components for data sources, such as Google products, where Sheets is a great example that we use for manual data input. And with ease, you can set up API query/extract profiles to get data from all types of API.
February 07, 2024

Matillion Review

Score 8 out of 10
Vetted Review
Verified User
Incentivized
We use Matillion for loading data from various sources into Snowflake Data Lake. We have data in various source systems such as SQL servers, many SAAS applications with proprietary databases, Quickbooks, files, and so forth. It is important to have data from all of these sources in the data lake for integrated reporting for the business.
  • ELT - Extract Load and Transform
  • Mostly a direct copy of data into the destination is handled very well
  • Managing secrets and all connections being handled using parameters/variables
  • Some connections are not straightforward to set up
  • Upgrades can get a bit complicated and require a backup instance to be created
  • Ability to email data files
It is well suited for direct data transfer from source to destination. Emailing success/failures of jobs is a bit complicated. Also, upgrades are quite frequent which do take time and testing and setting up backup instances. We always have to remember to keep the backup instance off and remember to delete it after the upgraded version has been tested.
Matthew Belo | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Incentivized
We replicate data between PostgreSQL and Snowflake for most of our core business operations. We were using various home-grown techniques for doing that which took a very long time to complete due to the growing size of the data. We turned to Matillion for help and started first with their original CDC product that was part of the ETL server. That could not handle the volume that we pushed, so we switched to their developing CDC product in Data Loader.
  • Provides seamless, end-to-end replication
  • Works tirelessly with the customer if there are any issues
  • The customer service team needs to improve interaction with the customer
  • Provide documentation to current customers on new features that are added so that we don't have to find out either ourselves or have to search through the FAQ pages
The current evolution of their CDC offering is where it should have been in the first place. That offering provides replication direction from the PG database to SF without any intermediate steps. The previous version had too many steps: ready from PG, write to S3, run ETL to push to SF, and there were opportunities for missed transactions. Where they need to grow for us is to allow end-to-end replication from PG-to-PG, SF-to-PG, and SF-to-SF. Those are handled by competitors, so it would help them close the gap.
Score 8 out of 10
Vetted Review
Verified User
Incentivized
Matillion has played a significant role in streamlining your ETL (Extract, Transform, Load) processes and has resulted in substantial time savings for your team. Automating data processing across different domains like Media Data, Sponsorship Valuation, and Consumer Research has enhanced efficiency and productivity.The reduction in processing time by 600 hours is a noteworthy achievement, allowing team to focus on more strategic tasks and potentially accelerating the pace of decision-making within the organization.Moreover, leveraging Matillion to create data products and explore new offerings demonstrates the platform's versatility in supporting innovative solutions and helping organizations derive more value from their data.
  • Drag and Drop
  • Data Transformation
  • Various Pre Built Connectors
  • Easy understanding
  • Pricing
  • BQ Connections
  • Less BQ Functionality
Strengths:Cloud Processing Efficiency:Matillion's strength in cloud processing aligns well with its design for integration with cloud data platforms. It's optimized for handling large-scale data transformations and processing in cloud environments.Time Savings for Data Engineers:The tool's efficiency and user-friendly features for data engineers contribute to time savings, allowing the team to focus on more strategic and complex tasks rather than spending excessive time on routine ETL processes.Challenges:Slower Excel File Processing:The noted challenge with slower Excel file processing highlights an important consideration. Tools optimized for cloud data processing might face limitations when dealing with certain local or non-cloud file formats.Complexity for Non-Tech Users:The perceived difficulty for non-tech users aligns with the typical trade-off in ETL tools. While Matillion provides a visual interface, the complexity of ETL processes may still present a learning curve for users without a technical background.
Score 9 out of 10
Vetted Review
Verified User
Incentivized
As a fast prototyping POC tool for end users of data and in production etl.
  • Easy to learn.
  • Easy to show complex calcs to stakeholders.
  • Data lineage.
  • Billing model.
  • Git integration.
  • Metadata management.
Complex ETL pipelines, specifically for those with limited prior experience. Fast-changing input datasets or schema.
Score 8 out of 10
Vetted Review
Verified User
Incentivized
We use Matillion as our main tool for all ETL/ELT processing. Our DWH target DB is Snowflake. We extract data from different sources; ex: SQL Server, BOX, S3, SAP HANA.
We also use Matillion to execute Python scripts. We are working currently to configure Matillion to connect to Github.
  • Executing Python scripts
  • Data Lineage
  • Easy to search of objects included in our project
  • Creating job templates
  • Jobs metadata to be available in Snowflake
  • Error messages some times are not so clear
  • Job Optimization recommendation
Matillion is a very easy to use tool, does not require much experience in the tool to start using it, so juniors and new joiners can start working with it easily.
Matillion has so much components that fits majority of our business needs.
A mising point in Matillion is the metadata to be available in Snowflake.
Score 10 out of 10
Vetted Review
Verified User
Incentivized
We get a lot of different files from different partners/vendors. We also have multiple databases that all need to be centralized. We use Matillion to read in the data from these different files and databases into a DataLake. That datalake then serves the rest of our company from reporting to billing.
  • File Imports
  • Large Scale Data Manipulation
  • Database Synchronization
  • File Manipulation After Processing
  • Built-in Error Reporting
  • Versioning
Matillion is great any time ETL or ELT is needed. I've now used Matillion in 2 different companies and would have no problem recommending others to use it as well. The ease of setting up schedules to just take care of data imports and manipulation is incredible. IT has also been an incredible tool at bringing disparate databases together on a schedule so that I never have to think about it.
September 07, 2023

A Necessary tool

Score 7 out of 10
Vetted Review
Verified User
Incentivized
The main issue at innova is manual process. With matillion we implemented automated data flow for disposal data to Google BigQuery

currently
We have near 50 automated jobs that runs every 5am and finished at 5.30am.

Also we matillion for extract data from HubSpot, zoom, brighspace, PostgreSQL, MySQL, cloud storage.

  • Integration
  • Performance
  • Job developing ( if you have complex jobs, design the flow can be expensive)
  • Cost
  • Flexibility
Performance is nice, but if you need creating a complex pipeline, you required long hours for developing the job can be really expensive, every minute is billed in design phase.

On the other hand. Matillion help us for integrate data sources like netsuite, HubSpot and others.

Score 8 out of 10
Vetted Review
Verified User
Incentivized
They come in far cheaper than other brands with the same level of support and functionality. When they deliver on the cloud based offering this will be a big step forward. Matillion has become an indispensable asset in our organization's data workflow. It elegantly tackles our complex data integration and transformation needs, streamlining processes that were once time-consuming. Our work with Matillion will continue to thrive and provide our business value going forward.
They are partnering with Snowflake which will be a huge step forward in the Data Engineering space. Downsides getting an agent setup On-Prem wasn't even an option and with other vendors this was easy.
Score 9 out of 10
Vetted Review
Verified User
Incentivized
Matillion is used to transform data from organizations transactional systems, sales and marketing systems and logistics. Data is transformed to create data assets that are easily consumed by users to take critical business decisions and also report operations and trend analytics. Matillion is also used to feed data into other systems that do not currently provide direct connectivity from various other applications. Matillion is also used to create and support a hub/spoke model for data assets that we have for self service reporting within different business units
  • Transformation of data
  • Efficient SQL query generation passed down to DB engine
  • Structured and unstructured data handling options
  • Data exploration and data transformation option availability
  • Monitoring and reporting on errors
  • Expansive API profile options
  • Data quality testing / monitoring options
  • Extended grid iterator limits
It does a great job in data transformations with easy to understand and follow steps, if done correctly. It allows you to check data samples within each step and allows to analyze what query is going to be passed down to the engine to cater for performance improvements. Performance of going through millions of rows of data is quite amazing. So far we have had minimal down time and maintenance windows.
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