AWS Glue is a managed extract, transform, and load (ETL) service designed to make it easy for customers to prepare and load data for analytics. With it, users can create and run an ETL job in the AWS Management Console. Users point AWS Glue to data stored on AWS, and AWS Glue discovers data and stores the associated metadata (e.g. table definition and schema) in the AWS Glue Data Catalog. Once cataloged, data is immediately searchable, queryable, and available for ETL.
$0.44
billed per second, 1 minute minimum
Matillion
Score 7.8 out of 10
N/A
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.
$2.50
Pay as you go per user
Pricing
AWS Glue
Matillion
Editions & Modules
per DPU-Hour
$0.44
billed per second, 1 minute minimum
Developer: For Individuals
$2.50/credit
Pay as you go per user
Basic
$1000
per month 500 prepaid credits (additional credits: $2.18/credit)
Advanced
$2000
per month 750 prepaid credits (additional credits: $2.73/credit)
Enterprise
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Offerings
Pricing Offerings
AWS Glue
Matillion
Free Trial
No
Yes
Free/Freemium Version
No
No
Premium Consulting/Integration Services
No
No
Entry-level Setup Fee
No setup fee
No setup fee
Additional Details
—
Billed directly via cloud marketplace on an hourly basis, with annual subscriptions available depending on the customer's cloud data warehouse provider.
AWS Glue is a fully managed ETL service that automates many ETL tasks, making it easier to set AWS Glue simplifies ETL through a visual interface and automated code generation.
AWS Glue is easier to use and has more and better features compared to it. And more documentation and tutorials and labs are widely available on the internet about AWS Glue which in turn helps in easier implementation of the spark jobs. Auto scaling is an added advantage. It's …
The main reason we choose AWS Glue over Talend open studio 1) Does not support Spark 2) Run only on java 3) not really feasible solution for heavy workloads 4) most of the cases need customer support 5) no proper documentation is available
AWS Glue is a managed service. It was easier for us to integrate it into our stack since we are already an AWS shop. It saved us the headache of managing a 3rd part service.
The cataloging of data objects is the best in the case of AWS Glue. We use AWS Glue in all of our data pipelines to sync external and internal data sources and to automatically produce SQL-based ETL based on AWS Glue catalog objects. Integration with Amazon products is the …
Glue comes in form of a managed service. However, the AWS data pipeline puts additional responsibility to manage the infrastructure. We were not requiring fine-grained control of the hardware which the AWS data pipeline provides. We also want to park our data on DynamoDB. AWS …
We are already in AWS services, so AWS glue is the first choice for us. But for the comparison of ETL job making and process time, it's way faster for other services.
Glue is easier especially if you are already in AWS. It easily integrates to other AWS services. Compliments well with Amazon Athena, S3, and Lake Formation. Compared to Snowflake, it is also much much cheaper and you don't have to build outside AWS. Support is also good if you …
Matillion is cheaper and we really like the customer support of Matillion as well as lerning materials provided by Matillion were far better. They also made connectors for us for free while others were charging us for it.
Matillion gives great ability to connect to variety of sources and bring data into cloud data warehouse using connector based approach with which we can build complex transformation jobs which can do automated data fetches from your sources.
Matillion has better capabilities and better built-in elements that saves your time and efforts. also the connectivity across multiple data warehousing tool is better in Matillion. even the performance of the pipeline and the time required to create a particular pipeline is …
My manager selected Million based on his previous work experience. He believes it is easy to use and maintain, cheaper than competitors, and suitable for our use case.
The only other ETL tool I've used was SSIS. At first I thought Matillion seemed "kiddish" after using the polished Microsoft tool but now I think Matillion is easier and can do much more as it has so many built-in connectors etc. We selected Matillion at our job because of …
n/a -- joined the team after they already were established in Matillion. Have had brief looks at other ETL products but found nothing compelling enough to suggest a change.
We selected Matillion primarily because of it's ability to connect to numerous data sources and easily create transformation jobs. While FiveTran does a better job managing and examining deltas, it is not easy to use and is very non user friendly. SSIS was not a good fit for …
Fivetran offers a managed service and pre-configured schemas/models for data loading, which means much less administrative work for initial setup and ongoing maintenance. But it comes at a much higher price tag. So, knowing where your sweet spot is in the build vs. buy spectrum …
We decided to move forward with Matillion because it was the best tool among tools that support both ingesting data from a source system to a target database and running transformation workflows on it afterwards. Fivetran and Airbyte only support data ingestion and we had our …
Cost and ease of use were better for our purposes. Matillion distinguishes itself from Fivetran and Snaplogic through its user-friendly design, no-code interface, in-depth transformation capabilities, allowing for complex data manipulations directly within the platform, …
The Matillion selection was not my decision. But I think it's a good enough choice. It is especially valuable that the team can learn Matillion easily and that the project can be understood by the entire team with the visual environment instead of complex ETLs.
Both the Databricks platform and Dbt Cloud are more powerful from the point of view of the development lifecycle and data use cases covered. They are also more complex and require specialized data engineering skills to be used. Matillion has a lower barrier of entry for small …
Removes most of the complexity around setting up and preparing things. If you could describe with words what needs to be done to move data from A to B, the implementation in Matillion would probably be the most similar in terms of simplicity of understanding what you are doing …
Matillion is a good tool for integrating multiple clouds. Informatica has been a market standard for many years, it provides multiple capabilities for data governance, data quality, etc. However, Informatica is pretty expensive compared to Matillion. Also, Matillion is more …
AWS Glue and Matillion are both software designed to help organizations extract and transform business data. AWS Glue is a data preparation tool, designed to help businesses prepare data for analysis, bypassing a data warehouse when possible. Matillion is a data integration tool designed to help businesses quickly pool together data from multiple sources such as SaaS applications.
Features
AWS Glue and Matillion both provide ETL features, but they also have a few unique features that set them apart from each other.
AWS Glue has support for data lakes, allowing businesses to prepare and integrate raw data and blob files with ease. Additionally, developers can create scripts to integrate data into AWS Glue that isn’t natively supported using Python or Scala. Lastly, AWS Glue helps businesses keep their business data compliant with regulatory guidelines including HIPAA and GDPR, making it a good choice for medical businesses.
Matillion provides built in support for over 40 SaaS applications. This makes Matillion a good choice for businesses with many applications they need to pull data from, particularly if they lack the resources or ability to make custom integrations. Additionally, Matillion provides features for data ingestion and business intelligence.
Limitations
Though AWS Glue and Matillion both help organizations transform data, they also have some limitations that are important to consider.
AWS Glue doesn’t provide built in integrations for SaaS application, though it is possible to build custom integrations. For businesses with limited resources or without a dedicated development team, AWS Glue may not provide enough support for data integration. Additionally, AWS Glue includes some analytics features, but ultimately provides limited business intelligence features.
Matillion offers support for SaaS applications, but doesn’t provide significant support for building out new integrations. Businesses with niche applications that aren’t covered by Matillion may consider other options. Additionally, Matillion offers support for ensuring regulatory compliance, but only for GDPR. Businesses storing medical data may prefer other options that also ensure HIPAA compliance.
Pricing
AWS Glue pricing depends on the needs of the business and the amount of data processes performed. Despite this, AWS does provide some pricing examples so businesses have an idea of what they might be spending, such as this: “$0.44 per DPU-Hour, billed per second, with a 1-minute minimum for each ETL job of type Python shell”. Businesses looking for specific pricing information can reach out to the vendor for a quote.
Matillion pricing similarly depends on the needs of the business, but it starts as low as $1.37 an hour and offers support for Amazon Redshift, Google BigQuery, Azure Synapse Analytics, and Snowflake.
Features
AWS Glue
Matillion
Data Source Connection
Comparison of Data Source Connection features of Product A and Product B
AWS Glue
-
Ratings
Matillion
8.3
Ratings
1% below category average
Connect to traditional data sources
00 Ratings
8.70 Ratings
Connecto to Big Data and NoSQL
00 Ratings
7.90 Ratings
Data Transformations
Comparison of Data Transformations features of Product A and Product B
AWS Glue
-
Ratings
Matillion
6.9
Ratings
17% below category average
Simple transformations
00 Ratings
7.50 Ratings
Complex transformations
00 Ratings
6.30 Ratings
Data Modeling
Comparison of Data Modeling features of Product A and Product B
AWS Glue
-
Ratings
Matillion
8.2
Ratings
3% above category average
Data model creation
00 Ratings
9.10 Ratings
Metadata management
00 Ratings
9.10 Ratings
Business rules and workflow
00 Ratings
8.30 Ratings
Collaboration
00 Ratings
7.20 Ratings
Testing and debugging
00 Ratings
7.40 Ratings
Data Governance
Comparison of Data Governance features of Product A and Product B
When the data which requires ETL has different formats, schema, and volume, this service suits them best. So, when the volume is not consistent (typical use-case of healthcare and online shopping), AWS Glue can be the prime choice. When the data is available in both batch and streaming mode, the developer needs to generate a separate codebase. This increases the source code management efforts. So, prefer to go with Glue when the nature of the data is the same (either batched or streamed).
Great: Need to query simpler APIs, or utilize well known services such as GSheets etc.? Matillion has got some of the best and easiest to use connectors out there. Not so great: Do you need have a competent CI/CD flow that you will be able to update / compare from Matillion as well as other sources at the same time? Good luck, you will need to be extra careful, as you might have to have a deeper dive into your servers Terminal each time you have a git conflict.
After data cleansing, the team also implemented the best practices for using AWS platform services as a Data Lake, such as job bookmarking for AWS Glue jobs, proper delimiter for the AWS Glue crawlers, partitioning in AWS S3, and transformation to parquet file for compression and faster querying time in Amazon Athena.
Data modernization through combining data from multiple sources into a functioning datasets, rebuilding DW, and resctructuring data sources.
Aims to lessen customer complaints, eliminate manual data extraction requests via SR from different data sources, and Increase accuracy, consistency and speed up reconciliation process.
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.
Matillion is easy to use and flexible to debug. Performance are good and support is giving us a good service level. There are still some technical points to be developed more (such as SAP extraction). but easy flows are really fast to be developed. We are also using a tool for migration from other tools, and it is useful as Matillion is producing XML code.
Easy tasks are really easy, and complex tasks are still possible. With prior knowledge of general data warehousing principles and experience with other data transformation tools, it's straightforward to get familiar with and use Matillion. I initially used minimal external support from a partner for some more complex tasks but very soon could work entirely independently with Matillion.
Amazon responds in good time once the ticket has been generated but needs to generate tickets frequent because very few sample codes are available, and it's not cover all the scenarios.
Overall, I've found Matillion to be responsive and considerate. I feel like they value us as a customer even when I know they have customers who spend more on the product than we do. That speaks to a motive higher than money. They want to make a good product and a good experience for their customers. If I have any complaint, it's that support sometimes feels community-oriented. It isn't always immediately clear to me that my support requests are going to a support engineer and not to the community at large. Usually, though, after a bit of conversation, it's clear that Matillion is watching and responding. And responses are generally quick in coming.
The cataloging of data objects is the best in the case of AWS Glue. We use AWS Glue in all of our data pipelines to sync external and internal data sources and to automatically produce SQL-based ETL based on AWS Glue catalog objects. Integration with Amazon products is the other advantage.
We selected Matillion primarily because of it's ability to connect to numerous data sources and easily create transformation jobs. While Fivetran does a better job managing and examining deltas, it is not easy to use and is very non user friendly. SSIS was not a good fit for our team and required a significant amount of attention and server management that we did not want to invest in.
We're using Matillion on EC2 instances, and we have about 20 projects for our clients in the same instance. Sometimes, we're struggling to manage schedules for all projects because thread management is not visible, and we can't see the process at the instance level.
Time savings -- we could custom code nearly everything Matillion does, but it would take days/weeks instead of minutes/hours.
There's a bit of a learning curve to truly unlock Matillion's potential, and that can be frustrating for some new users, but once you get over that curve, the possibilities are endless.
It allows us to centralize the hundreds of way to bring data in, so that even if you have to troubleshoot what someone else wrote, it's easy to jump in and understand what is happening.
