Filter Ratings and Reviews
Filter 104 vetted Matillion reviews and ratings
Reviews (1-25 of 61)
March 05, 2020
Good at its core, but not a commercial dev tool
We are using Matillion in our global IT department to process incoming revenue data from hundreds of sources around the world.
- Processing large data
- Easy to use UI
- Simplified the building of complicated SQL
- GIT implementation is shocking
- Multi-developers is almost impossible to manage
- Variable handling is time consuming
- No telephone support
June 28, 2019
Excellent Entry-Level ETL/ELT Resource
We used Matillion on a small, 3-person team to manage ingestion and transformation for our data warehouse, pulling from numerous datastores and vendors to a common warehouse schema.
- Shallow learning curve - Matillion is powerful, but only as complicated as you need at a given time.
- Superb helpdesk and documentation.
- Jobs are translated directly to SQL which makes them easy to debug.
- Parsing of flat files is inconsistent depending on the source
- The product is constantly evolving (good!) but that means relearning parts of the interface every few months
- Support for a larger set of APIs and database engines would be helpful
We are currently using Matillion as our primary ETL/ELT tool both in our department and supporting others in the organization as well as introducing it to other Business units under the Cimpress brand. For us and others it primarily solves the need of loading and manipulating raw data from multiple sources and making it available in both Redshift and Snowflake. Additionally, the flexibility of the tool and its seamless integration with the complete AWS Services library allows us to perform many operational tasks that are completely unrelated to the ETL/ELT workload.
- Integrates very well with most, if not, all AWS services - Easy to get data from anywhere inside your AWS account into Redshift or Snowflake databases.
- It has many available integrations and datasource connectivity outside of AWS and other cloud service providers - Makes it easy to get data from many sources and systems in more traditional on or off premise technologies as well as vendor-specific data and application interfaces.
- Very intuitive, easy-to-learn-and-use interface with a large collection online tutorial articles and videos - makes for an extremely easy onboarding of users who may or may not have past experience with other similar tools.
- Limited selection of instance sizes - Larger organizations and groups my need to set up multilple instances depending on estimations on concurrent users.
- Lack of complete github, gitlab, or other source control integration - it has internal versioning but it is limited. Manual job exports currently don't lend well for useful DIFFs comparison when using those technologies.
- Needs more options for authentication and security - User creation is very manual and can be tedious and difficult to manage for larger installations.
iQuanti is a Digital Marketing organization, which drives strategic decisions based on a data-driven approach and for this we use Matillion as our ETL solution tool for our organization, which is consumed by our Data Management team. We have large numbers of the digital platform from which we report and analyze the performance. Matillion helps us to automate most of our reporting needs by providing connectors to digital platforms like Google Analytics, Google Adwords, Facebook, Bing, JIRA, Google BigQuery, and various Data source connectors like Postgres, SQL, MongoDB along with AWS support as SNS, S3 etc, which provides lots of flexibility in today's world.
- Supports a wide variety of digital platform connectors, which could be helpful for any industry working to automate any of their reporting needs.
- Support for AWS technologies adds to greater advantage.
- Takes up unique functionality provided by the database into account which is very helpful.
- Also provides direct SQL query feed-in option for any migration of existing solution.
- With the added functionality available in Matillion, the understanding to use complex features becomes challenging for a new development team.
- Updates are regularly provided by Matillion support team but then they fail to specify the release document, new features or updates carried out in each update.
- Validation failure in scheduled jobs is sometimes encountered without any reason or proper RCA.
- Complex user management flow.
Matillion is used in our Data Analytics and Data Engineering department. At the time we purchased it, we had no dedicated data engineers. We needed something powerful enough to handle more complex jobs, but simple enough that a semi-technical analyst could use. We were using a lot of different tools at the time and were evaluating a lot of different ETL solutions in the space.
Matillion provided a solution that was simple, easy to connect, and also provided Internet connectors to some of our most crucial systems, such as Salesforce and Jira. It has allowed us to consolidate much of our processes onto a single system that is easily understood.
Matillion provided a solution that was simple, easy to connect, and also provided Internet connectors to some of our most crucial systems, such as Salesforce and Jira. It has allowed us to consolidate much of our processes onto a single system that is easily understood.
- Cloud connectivity: It makes pulling data from cloud services like Salesforce super simple and easy to bring into a data warehouse
- ETL Orchestration: The drag and drop interface makes it easy to compose new orchestration layers in our ETL. It's something that does not require a Data Engineer to complete.
- Enterprise integration: It was really easy to configure into our LDAP system, and that makes administering the box really easy.
- Variety of Data sources: It is pretty easy to bring data into Matillion to process into the data warehouse.
- The Gui provides other non-functional visual elements to mark up the job. This is great for team members to communicate complicated parts of the ETL or to otherwise label parts of their ETL.
- Matillion has no clustering ability. For particularly large jobs or large data sources, processing can take a long time and it does not have the ability to map-reduce, like Spark.
- The output is limited to Redshift. Often times we would want to drop a Parquet or Avro file into s3 as the output of our ETL.
- We often get OOM errors and other server related constraints. We need to be very careful about how our jobs are scheduled in order to make everything work well.
- It is not clear from the documentation how to organize work in Matillion. Between environments, projects, and jobs in a project, we've had to organize in a way to accommodate for Matillion's limitations, which doesn't allow us to organize our jobs in a way that makes sense for us.
May 23, 2019
Ease of Use and Reusability for Excellent ROI
We use Matillion to bring together data from multiple sources - our application, Salesforce, Qualtrics, etc. - into a central data warehouse for use throughout the business in reporting, both internally and externally. Matillion performs the ETL that transforms this raw data into a structure in Redshift useful for reporting.
- It integrates well with Amazon Web Services, like S3 and Redshift
- It makes good use of Redshift to perform ETL quickly
- The ability to parameterize ETL jobs with variables makes it possible to get a lot of reuse from ETL jobs
- Integration with source control is a challenge; we had to roll our own solution to pull our Matillion jobs via its API into files we could add to source control
- It can be a challenge to avoid conflicts when multiple people are developing jobs in the same project
- It's only available on Redhat flavors of Linux (e.g. Amazon Linux, Redhat, CentOS)
May 10, 2019
Matillion ETL for Amazon Redshift
I met with Matillion team at AWS Summit 2018 at Sydney, Australia. At that time we were using legacy ETL for our Redshift Data Warehouse.
We were trying to adopt Continuous Integration/Continuous Delivery framework for our ETL and found it to be challenging for our environment. It was time to look for something new and say goodbye to our bellowed legacy ETL. So we had a conversation with Matillion team and quickly decided to do a POC. We've evaluated couple of other tools in the past but nothing came close to Matillion.
First of all, setup was a breeze. The migration was quick and painless, the system is very easy to work with. Almost more importantly, the Matillion team is very friendly and always willing to help, their support is outstanding! Matillion has dozens of native connectors out of the box, so we had no problem with integrations at all. In fact, migration to Matillion allowed us to drop couple of third party legacy ETL components required for AWS infrastructure.
Python scripts component allows to use Boto3 and hundreds of other libraries, so practically there is no limit of where you can use Matillion.
We were finally able to build full scale CI/CD pipeline. Our setup uses combination of JIRA, Bitbucket and Bamboo build server and it works like a charm, thanks to Matillion'™s REST API.
- Works very well with Redshift and integrates with other AWS Services, such as S3, SNS or SQS for example
- Has scripting components like Python using Boto and any other libraries. Additional libraries need s to be installed on Matillion EC2 instance
- Plenty of data sources out of the box, the rest can be pulled via API
- Automatic validation of database objects and components
- Easy to install
- Excellent integration with CI/CD
- Minor: Changes to the ETL can only be reviewed in Matillion GUI rather than true source code diff, i.e. Bitbucket
May 08, 2019
Matillion: Fast ELT with a whole lot more
We use Matillion to load millions of rows of data each and every day. We got our warehouse up and running using Matillion in just a few months. We use it for all our reporting and have built out our commissions processes using this tool. We are able to connect data from multiple sources into one coherent location that makes our reporting so much easier.
- Matillion is an ELT tool rather than an ETL tool. This means that it's using the database engine to manipulate data. Much faster than a traditional ETL tool where speed is lost in the movement of data.
- Building Transformation Jobs. The flow-chart style developer interface makes it simple and easy to build jobs. But underlying that is the ability to create complex SQL and/or Python components which make just about anything you can think of possible.
- AWS integration. The integration with AWS allows a multitude of possibilities such as automatically kicking off jobs when a file is loaded in S3 or sending a notification of job success or failure via SNS.
- Matillion has some limited capabilities when interacting with other databases outside of Redshift and RDS. Getting data from other databases is pretty easy. Putting data back into databases other than Redshift and RDS is more limited. That said, it's specifically built to load Redshift and that it does well.
- There are times when I get disconnected from the server. This may happen once or twice a day. Nothing is lost and it's a simple matter of logging back in. It's not my internet either as my coworkers in different areas of the country experience the same thing. I have seen some improvement in some of the later releases.
We use Matillion as the new ETL tool for building Data infrastructure for the company. Our Data team is limited to 2 Matillion users but the Data infrastructure will be used by the BI team to help all areas of the business. So far, we've used it for various data loading tasks but are starting to use for the new Data Warehouse processes we'll be building.
- Data Mapping - it is really good at recognizing the source and destination data types and does a great job at converting data types.
- User-Friendly Interface - development is very easy within the tool and very intuitive for Data experts.
- Troubleshooting - for the most part, it gives informative messages when errors occur and it is extremely useful to see data transformation 'in-flight' through data sampling.
- Deployment to other Environment - this is not a smooth process and needs improvements. Better integration with code repository and smooth deployment of changes is needed (for example, just exporting variable default values is very clunky).
- Documentation - for the version of Matillion we are using, we can't export documentation for our jobs. Since this is the level of functionality we need, it sounds unreasonable that to get a couple of more features (one of them being Documentation), we would need to upgrade to a higher level of license that would cost twice as much).
- Running queries on other databases - although we primarily want to load data into Redshift, we sometimes need to run quick queries as part of the process against non-Redshift databases (that are not straight loads from the DB to Redshift). Although we can do this through Python, it would be a lot more helpful to be able to do this within Matillion components.
Matillion is a fundamental part of our ETL pipeline creation and management. It is the solution we have chosen that enables myriad external data sources (APIs, CSVs, data lakes etc.) to be ingested into our data warehouse. As a tool for its specific purpose, it is primarily used by the Data Science team, but the resulting data is disseminated to multiple departments via dashboards and reports across the whole company.
- It provides a simple and intuitive visual interface for ETL creation and management that is also highly flexible and extensible i.e. integrating custom Python scripts
- It makes complex, advanced workflows, e.g. updating, scheduling, error handling & reporting, simple with pre-existing drag-n-drop "feature blocks"
- It makes the creation of a clear, hierarchical architecture across all pipelines simple and easily replicable by all members of the team
- Matillion's support team is highly responsive and always do their best to address issues as quickly as possible
- Whilst the pre-existing "feature blocks" do make creating new API integrations simple, fine tuning the integration or ironing out minor bugs requires conversations with Matillion support (however, they are always very quick to respond and have always fixed our issues)
- Minor feature request: It would be nice to have a way to pass the output of a Python script directly to a subsequent SQL "block"
April 17, 2019
Matillion has made complex ETL of massive quantities of variably structured data approachable for us.
Our company, Clutch, ingests, transforms and otherwise manipulates massive quantities of retail customer data every day. Because even retailers in the same verticals each bear different processes, structures and even attributes within their data, we needed a tool to help us rapidly develop ETL and we needed one tailored to working with advanced columnar data and storage, such as Amazon's Redshift column store database and S3 cloud data storage. While we're cost sensitive, we also didn't want to reinvent the wheel.
More importantly, we wanted to avoid a cloud-based product that tried to act like traditional enterprise ETL tools like Informatica or SSIS, only to find ourselves using clunky, complicated and undiscoverable browser-based tools.
Finally, we are a shop that often 'fixes tires on a moving car', and so the tool we chose needed to be intuitive, the support behind it responsive and amazing. Matillion has served these needs and more, quickly becoming a go-to part of most of our data solutions, and becoming increasingly visible in other areas as well, such as reporting on cloud-based systems like Salesforce and JIRA.
More importantly, we wanted to avoid a cloud-based product that tried to act like traditional enterprise ETL tools like Informatica or SSIS, only to find ourselves using clunky, complicated and undiscoverable browser-based tools.
Finally, we are a shop that often 'fixes tires on a moving car', and so the tool we chose needed to be intuitive, the support behind it responsive and amazing. Matillion has served these needs and more, quickly becoming a go-to part of most of our data solutions, and becoming increasingly visible in other areas as well, such as reporting on cloud-based systems like Salesforce and JIRA.
- Matillion (for Redshift) understands working with Redshift, and it's components adapt well to changing aspects of extraction, transformation, and loading.
- Because Matillion is pay for actual use, we only pay for the uptime of our instance. This is way more powerful than we expected, as we're able to maintain task-specific Matillion instances that are brought online to handle workloads, then taken down when not in use.
- Matillion's support is probably the best there is, for any software. You're never asked the proverbial 'is the machine on' type questions, they always get back to you faster than expected, and the people conducting the support are definitely not reading scripts. They know what you're trying to do because they've probably done it.
- Matillion often surprises us with it's flexibility and adaptability to tasks, even some you'd normally consider being outside the purview of traditional ETL.
- While ETL usually involves fetching, moving, changing and loading/unloading data, sometimes destroying it is required, but Matillion is understandably light in these capabilities. For example, when we process data arriving via SFTP, once we're done, we like to move the files in question to somewhere indicating so, like a processed area, or even a failed area. It would be great if Matillion offered a way to do things like this, even if not a 'delete file', perhaps a 'move file' where you must provide a source/destination.
- A way to flush queued tasks without rebooting.
- Better task logging/communication features e.g. alerts, etc.
Within the Business Analytics team, Matillion was introduced as a new ELT tool after team extension. In that time, we had four active BI developers and the main need was a centralized project repository that will make the daily workload easier. We are using Matillion for development and maintenance of the data warehouse and integration of more than 20 data sources.
- Tailor-made for Redshift, including most of the features like Spectrum.
- Cloud-based solution with centralized project repository, easy team collaboration.
- Built-in scheduling and monitoring, everything in one place.
- Awesome support, the short time from raising a question to the solution providing.
- Documentation is not always updated in time when new stuff is implemented.
- Some connectors already implemented still have some bugs which make them useless for our use case.
- Small fine tuning still missing, in terms of covering all use cases of some connectors.
It is being used across all of our internal data to bring data together for reporting. This includes
Sales, Marketing, Billing, Support and ustomer activity. We have processes that run every minute as well as batch processes that run every day.
Sales, Marketing, Billing, Support and ustomer activity. We have processes that run every minute as well as batch processes that run every day.
- Quick access to Google Sheets data.
- Utilizes SQL well.
- Supports custom API data sources.
- Pricing is by server size not # of data sources or volume.
- Source control integration is archaic and not implemented with teams in mind.
- Has some performance issues related to memory issues.
- Documentation is lacking and there is no real training available.
Matillion for Snowflake is used by our Data Engineering team for developing data flows for extracting and transforming data into our Snowflake data lake. The data lake is available to the entire organization for analysis and decision support. We needed an ETL tool for batch processing for replacement of Airflow.
- Ease of use.
- Suitable for developers from junior to senior.
- Connects to a variety of data sources and platforms.
- Matillion has many tools available for transforming data.
- Does not integrate easily into the source code control system.
- The small instance needs more concurrent user connections. Two is too few, and moving to the next instance size does not make sense in our development environment.
- Matillion for Snowflake does not have a Dynamo DB connector.
May 24, 2019
Matillion has met the needs of our data warehousing and ETL better than we had exptected
Matillion is being used by Xyngular to ETL data from multiple environments into an AWS Redshift database. We also use it to pull data files from FTP and load them into the database. It also allows us to be very flexible on the scheduling of the jobs.
- Loading an FTP file into the database.
- Transforming data before loading it into the database.
- Flexible scheduling of jobs.
- Straight data copy from one database to another.
- When I make changes to a job and add fields to what is being pulled, I have to drop the entire list and repopulate it.
- Honestly, the first thing is the one part I have had issues with.
May 23, 2019
Great product and great customer support
We currently use Matillion for Redshift as our ELT tool for data retrieval, aggregation, and management. It is currently being used by multiple departments in our organization, including our data team and operations team. Having used Apache AirFlow, Matillion is a breath of fresh air. It is easy to use, easy to implement with a variety of technologies, and the support system is quick and convenient.
- Easy to setup and provision
- Expert customer support
- Can take some getting used to
- Inability to update/install Python libraries
Currently driving the ETL /ELT processes to load a Redshift Data warehouse. We have 5+ data sources that include on-premise/cloud databases, CRM and marketing tools that are feeding the data warehouse. Matillion is also used to automate various marketing processes.
- Cloud-based solution
- Seamless integration with Redshift
- Minimum scripting required
- Good feature documentation
- Easy to learn/good tech support and tutorials
- Installation/setup
- Better out of the box support for CRM tools like Zendesk would be helpful.
Matillion is being used in our company as an ELT tool. We load data into it from various sources, such as Salesforce, NetSuite, etc. We are exploring more such options, from there we can pull the data into matillion. Once in matillion, we transform this data using various components provided by the matillion tool to modify the data as we need. Then, once all the modification are in place we load the data into Snowflake which is our data warehouse
- Data extraction is really quick and hassle-free.
- Transformation is very easy to perform as it provides an elaborate list of components that can be used to create and optimize your query.
- Scheduling is very manageable and easy to monitor and review.
- UI is very user-friendly, every component and sub-component can be understood by the help option provided as a hyperlink.
- There can definitely be some improvements w.r.t the NetSuite orchestration component. We have had lots of trouble connecting it to Matillion during POC.
- Although the UI is quite user-friendly there is room for much improvement.
- There should be requirement specific customized training before a company starts working with Matillion. We got general training which definitely benefited us, I just think a more project-specific training would have been more useful
We're exploring Matillion as an ELT tool when we migrate our workloads to the cloud. This software is not in production yet as we're still exploring. Once accepted, this software will be used in our Information Management department. This business problem that is addressed by this software is ELT.
- Drag n Drop to build orchestration and transformation jobs
- Simplicity
- Pay as you Go
- Ability to scale up and down
- Minimal code compared to the competition
- Cloud based
- Ability to migrate jobs created for one platform to other easily (for e.g. from Redshift to Snowflake).
- There is a scope of improving developer productivity by enhancing the user interface. Sometimes the UI is confusing.
- Some times the orchestration and transformation job diagrams become very complex. Need to come up with design patterns for proper diagram preparation.
We are using Matillion within the Analytics department. We had a core need to be able to move data from multiple single-tenant client database sources into a proper data warehouse (AWS Redshift) for analytical reporting.
- Easy drag and drop logic/control functions.
- Ability to script (in Python) when out of the box components are not enough.
- ELT vs ETL allows for super fast transformations done directly in Redshift.
- We have had issues with out-of-memory errors when Matillion is up and running for a long time. For this reason, we've implemented an automated monthly restart job which works around this issue.
- We do a lot of "reverse ETL" processing. For certain use cases we need to run extracts out of the analytical data warehouse, massage the data, then move it back to our transactional databases for certain operational tasks. Although it is possible with certain components in Matillion, there could be more enhancements to those components to make life easier for some tasks.
We are using it as the backbone for various projects we do for clients who possess large amounts of semi-structured data that they currently cannot use effectively. We ingest and transform their data using Matillion, storing it in Snowflake and building reports on it using Tableau. These projects are what we primarily use Matillion for. We're looking into using it across our organization to help manage data like our financials and other organizational metadata that we use to make business decisions.
- Very intuitive/easy to learn and teach
- Great documentation
- Easy to spin up and get going
- Version control is minimal. Tough to form a process for collaborative development around it.
- More thorough documentation around best practices specifically would be good.
- Sometimes the tool freezes up when a large number of processes are running.
Matillion is used by the Data Engineering team to load data into Amazon Redshift and to implement data transformations that produce the reporting layer of the data warehouse. The reporting layer is used across the organisation from everything from customer analytics to financial reporting.
- Loading data into Redshift using bulk load utilities.
- ELT makes use of Redshift's MPP architecture.
- Very good UI, and intuitive to use.
- Choice of Python for custom code is good as it's an easy language.
- Connectors to streaming solutions like Kafka would be a good addition.
- Some legacy component connectors are not available.
We've deployed Matillion at several customers across many business units to orchestrate data movement and transformation from source systems to Redshift and Snowflake. It is a great solution for a customer looking for a cloud-based application to handle enterprise-scale data engineering tasks.
- Push-down ELT design.
- Simple purchasing, pricing, setup, and usage.
- Powerful design patterns, and connectors.
- Additional connectors to source systems are always needed.
- HA product engineering could be better.
We are using Matillion to centralize our data to a new data warehouse. Our primary use case is pulling data from a relational database (MySQL). I've been able to implement three different flavors of data sourcing, the two primary types being a full table load and incremental data loading of changed data into Slowly Changing Dimension and Fact tables in the DW. The performance of Matillion combined with Snowflake is astoundingly fast. We are also able to hit APIs to Zendesk and Hubspot easily to round out our integration with other SaaS vendors in our stack.
- Push down query performance with Snowflake.
- The ability to hit any API using Python.
- A robust offering of pre-built connectors to databases, APIs, and other SaaS vendors.
- No user community site for experienced developers to share their patterns and help grow the dev community
- Documentation can get stale or be changed without notice.
- Several aspects of the product are not user-friendly, and if implemented by an experienced product/UX person it would make the product easily 2x to 3x better.
- No ability to vote on what features are in the pipeline.
Matillion is used by the BI team as our ELT tool. It's not used by the rest of the organization, but they get the benefits.
It helps us keep logic all in one place, and is a foundation piece of our data pipeline. It also allows us to quickly work with data and get it into the format required for data purposes.
It helps us keep logic all in one place, and is a foundation piece of our data pipeline. It also allows us to quickly work with data and get it into the format required for data purposes.
- Speed to connect to data sources; the power of ELT tools is in pre-built integrations.
- Support services are excellent and very quick to respond
- My devs enjoy using it. It has an easy simple GUI and allows for repeatable processes.
- It's very hard to version control out of your system. If I connect to Git it's impossible to tell the changes that we have made. It's the same for most GUI tools, but I would love to solve that.
- Documentation is sometimes slow to keep pace with the changes in the tool, meaning confusion or lost time.
- It's not clear what integrations are coming next.
- Changing things across multiple workflows is quite involved and doesn't easily allow us to make changes across our entire ELT section.
- We had to build all logging ourselves. Matillion tells us easily that a job failed, but it's hard to extract in an automated way why it failed.
Matillion Scorecard Summary
Feature Scorecard Summary
About Matillion
Matillion is data transformation for cloud data warehouses. According to the vendor, only Matillion is purpose-built for Amazon Redshift, Snowflake, and Google BigQuery enabling businesses to achieve new levels of simplicity, speed, scale, and savings.
Quickly develop custom Transformation jobs by combining Filters, Joins, Aggregates, Calculators, Ranks, as well as more complex transformations such as Rankings, window calculations, and change-detection by dragging and dropping these onto a canvas GUI. Use Orchestration features to automate your ETL workflows with scheduling, notifications and alerting, and control flow.
Once your jobs are built, run them with confidence with component level validation and data sampling. For any custom or unique needs, Matillion also has Bash and Python Script components giving you the ultimate extensibility and flexibility.
Shared Job templates, Dynamic and Grid variables, and Version control help your development teams share resources across your Matillion instance.
Enterprise customers benefit from additional auditing and permissions, automatic job documentation, data lineage and more. Learn more about enterprise features here.
Each product comes with a set of warehouse specific features that help you get the most out of the platform. See our Product Feature pages on matillion.com for more information.
Categories: Data Integration
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Pricing
- Has featureFree Trial Available?Yes
- Does not have featureFree or Freemium Version Available?No
- Has featurePremium Consulting/Integration Services Available?Yes
- Entry-level set up fee?No
Billed directly via cloud marketplace on an hourly basis, with annual subscriptions available depending on the customer's cloud data warehouse provider.
For the latest information on pricing, visit https://try.matillion.com/matillion-a-general-page-demo/
Matillion Support Options
| Free Version | Paid Version | |
|---|---|---|
| Forum/Community | ||
| FAQ/Knowledgebase | ||
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Matillion Technical Details
| Operating Systems: | Unspecified |
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| Mobile Application: | No |
| Supported Countries: | Global |
| Supported Languages: | English |