Matillion Data Productivity Cloud and AWS Glue compete in the data transformation and ETL market. Based on user feedback, Matillion appears to have a slight advantage due to its user-friendly nature and comprehensive features.
Features: Matillion offers powerful ETL features that streamline data transformation tasks. Its robust integration with Salesforce and Redshift is highly appreciated. Users commend its ease of setup and efficiency. AWS Glue shines with a comprehensive data catalog and seamless integration with AWS services. It's also valued for its scalability and automation capabilities, offering a good mix of performance and flexibility.
Room for Improvement: Users suggest Matillion could improve with more frequent updates to keep pace with third-party API changes and enhance database integration. There's also a call for better real-time processing capabilities and concurrency. For AWS Glue, users point out the need for improved documentation, a better drag-and-drop interface, and reduced setup complexity, despite its robust integration with various AWS services.
Ease of Deployment and Customer Service: Matillion provides a straightforward deployment experience in public cloud environments and is praised for its responsive and helpful customer support. While AWS Glue supports a wider range of deployments, including public, private, and hybrid clouds, its interface may present some initial challenges. However, AWS Glue's technical support is generally well-regarded, though Matillion's support is noted for its efficiency, offering a slight edge.
Pricing and ROI: Matillion employs a pay-as-you-go pricing model based on machine usage and team size, offering transparent billing and often leading to rapid ROI despite being perceived as moderate to expensive. AWS Glue also uses a pay-as-you-go model, with costs tied to Data Processing Units (DPUs). Though potentially costly for small businesses, it offers benefits in scalability and cost management. Both provide good ROI, but Matillion is noted for immediate operational savings.
I advocate using Glue in such cases.
Consequently, we adjusted our processes to use Matillion Data Productivity Cloud only for extraction and ingestion, while Snowflake handled all transformations and jobs.
Upgrades occur every four months, and new developments coincide with version updates.
They communicate effectively and respond quickly to all inquiries.
It can easily handle data from one terabyte to 100 terabytes or more, scaling nicely with larger datasets.
It is beneficial to upgrade jobs, and we conduct extensive testing in development before migrating to production.
The autoscale process works well, allowing the system to start another node automatically if the first machine reaches 80% capacity.
As a managed service, it reduces management burdens.
A more user-friendly and simpler process would help speed up the deployment process.
With AWS, I gather data from multiple sources, clean it up, normalize it, de-duplicate it, and make it presentable.
Learning the latest functionalities is crucial, and while challenging, it is a vital part of staying current and ensuring an efficient ETL process.
Connections to BigQuery for extracting information are complex.
AWS charges based on runtime, which can be quite pricey.
Costing depends on resource usage, and cost optimization may involve redesigning jobs for flexibility.
The smallest cost for a project is around €700, while the largest can reach up to €7,000 based on the scale of the usage.
Matillion Data Productivity Cloud offers discounts and special deals, especially when dealing with high-volume clients or fewer existing clients in specific regions, like Spain.
AWS Glue's most valuable features include its transformation capabilities, which provide data quality and shape for processing in ML or AI models.
For ETL, I feel the performance is excellent. If I create jobs in a standard way, the performance is great, and maintenance is also seamless.
I think if I'm working with big data, common languages like Python work quite nicely, which is advantageous.
Matillion Data Productivity Cloud is effective for ingest functions, particularly when moving information to Snowflake and performing many transformations.
AWS Glue is a serverless cloud data integration tool that facilitates the discovery, preparation, movement, and integration of data from multiple sources for machine learning (ML), analytics, and application development. The solution includes additional productivity and data ops tooling for running jobs, implementing business workflows, and authoring.
AWS Glue allows users to connect to more than 70 diverse data sources and manage data in a centralized data catalog. The solution facilitates visual creation, running, and monitoring of extract, transform, and load (ETL) pipelines to load data into users' data lakes. This Amazon product seamlessly integrates with other native applications of the brand and allows users to search and query cataloged data using Amazon EMR, Amazon Athena, and Amazon Redshift Spectrum.
The solution also utilizes application programming interface (API) operations to transform users' data, create runtime logs, store job logic, and create notifications for monitoring job runs. The console of AWS Glue connects all of these services into a managed application, facilitating the monitoring and operational processes. The solution also performs provisioning and management of the resources required to run users' workloads in order to minimize manual work time for organizations.
AWS Glue Features
AWS Glue groups its features into four categories - discover, prepare, integrate, and transform. Within those groups are the following features:
AWS Glue Benefits
AWS Glue offers a wide range of benefits for its users. These benefits include:
Reviews from Real Users
Mustapha A., a cloud data engineer at Jems Groupe, likes AWS Glue because it is a product that is great for serverless data transformations.
Liana I., CEO at Quark Technologies SRL, describes AWS Glue as a highly scalable, reliable, and beneficial pay-as-you-go pricing model.
Matillion Data Productivity Cloud features an intuitive graphical interface, seamless AWS integration, and efficient data management. Its tools streamline complex tasks for SFDC, RDS, Marketo, Facebook, and Google AdWords.
Matillion Data Productivity Cloud provides fast transformations with built-in verification, easy scheduling, and sampling. With automatic scalability and diverse data source support, it simplifies complex data tasks. Users benefit from cloud data warehousing and integrating data into Snowflake while appreciating its ease of use by non-technical teams. Enhancements can focus on frequent API adjustments, improved documentation, faster performance with less latency, and better error handling.
What are the key features of Matillion Data Productivity Cloud?
What benefits and ROI should users seek in reviews?
In industries such as technology, finance, and healthcare, Matillion Data Productivity Cloud is implemented to streamline ETL processes, optimize data pipeline construction, and enhance data migration efforts. It supports efficient data loading and integration between cloud and on-premises databases, aiding industries in managing data-driven projects.
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