We performed a comparison between Matillion ETL and Snowflake based on real PeerSpot user reviews.
Find out what your peers are saying about Amazon Web Services (AWS), MuleSoft, Matillion and others in Cloud Data Integration."It is pretty user-friendly, even for people who aren't super technical."
"It takes less than five minutes to set up and delivers results. It is much quicker than traditional ETL technologies."
"It has improved the costs of managing my customer’s data."
"The new version with the Productivity Cloud is very simple. It's easy to use, navigate, and understand."
"Matillion ETL has great Git integration that is perfect and convenient to use."
"The most valuable feature of Matillion ETL is its ease of use. If you have had some experience with other solutions, such as Snowflake, the use of this solution will be simple."
"It has helped us to get onto the cloud quickly."
"The most valuable feature of Matillion ETL is the ETL. The solution is open-source which provides advantages, such as good performance and high efficiency. Additionally, it supports three data types which eliminates predefining the data, and we can write script models in Python."
"Data sharing is a good feature. It is a majorly used feature. The elastic compute is another big feature. Separating compute and storage gives you flexibility. It doesn't require much DBA involvement because it doesn't need any performance tuning. We are not really doing any performance tuning, and the entire burden of performance tuning and SQL tuning is on Snowflake. Its usability is very good. I don't need to ramp up any user, and its onboarding is easier. You just onboard the user, and you are done with it. There are simple SQL and UI, and people are able to use this solution easily. Ease of use is a big thing in Snowflake."
"My company wanted to have all our data in one single place and this what we use Snowflake for. Snowflake also allows us to build connectors to different data sources."
"Its performance is most valuable. As compared to SQL Server, we are able to see a significant improvement in performance with Snowflake."
"The most valuable feature of Snowflake is its performance. We can access the data quickly. Additionally, it handles structured and non-structured data."
"It is a very easy-to-use solution. It is user-friendly, and its setup time is very less."
"This solution has helped our organization by being easy to maintain and having good technical support."
"I like the fact that we don't need a DBA. It automatically scales stuff."
"Snowflake is faster than on-premise systems and allows for variable compute power based on need."
"The current version is a bit more limited because it's on a virtual machine, and everything executes on that one virtual machine."
"The cost of the solution is high and could be reduced."
"Unlike Snowflake which automatically takes care of upgrading to the latest version and includes additional features, with Matillion ETL we need to do this ourselves."
"The tool's lineage is very weak."
"Performance can be improved for efficiency, and it can be made faster."
"To complete the pipeline, they might want to include some connectors which would put the data into different platforms. This would be helpful."
"One of the features that's in development is data privacy in the cloud, along with further SAP integration. For connectivity to SAP systems."
"It can have multi-environment support. We should be able to deploy it in different environments. Its integration with SAP connection is not so nice, which should be improved. It can also support an on-prem database."
"They need to incorporate some basic OLAP capabilities in the backend or at the database level. Currently, it is purely a database. They call it purely a data warehouse for the cloud. Currently, just like any database, we have to calculate all the KPIs in the front-end tools. The same KPIs again need to be calculated in Snowflake. It would be very helpful if they can include some OLAP features. This will bring efficiency because we will be able to create the KPIs within Snowflake itself and then publish them to multiple front-end tools. We won't have to recreate the same in each project. There should be the ability to automate raised queries, which is currently not possible. There should also be something for Exception Aggregation and things like that."
"Snowflake could improve if they had an Operational Data Store(ODS) space."
"The solution should offer an on-premises version also. We have some requirements where we would prefer to use it as a template."
"In future releases, it can also support full unstructured data."
"It's difficult to know how to size everything correctly."
"The design of the product is easily misunderstood."
"The data science functionality could be improved in terms of the machine learning process."
"There are three things that came to my notice. I am not very sure whether they have already done it. The first one is very specific to the virtual data warehouse. Snowflake might want to offer industry-specific models for the data warehouse. Snowflake is a very strong product with credit. For a typical retail industry, such as the pharma industry, if it can get into the functional space as well, it will be a big shot in their arm. The second thing is related to the migration from other data warehouses to Snowflake. They can make the migration a little bit more seamless and easy. It should be compatible, well-structured, and well-governed. Many enterprises have huge impetus and urgency to move to Snowflake from their existing data warehouse, so, naturally, this is an area that is critical. The third thing is related to the capability of dealing with relational and dimensional structures. It is not that friendly with relational structures. Snowflake is more friendly with the dimensional structure or the data masks, which is characteristic of a Kimball model. It is very difficult to be savvy and friendly with both structures because these structures are different and address different kinds of needs. One is manipulation-heavy, and the other one is read-heavy or analysis-heavy. One is for heavy or frequent changes and amendments, and the other one is for frequent reads. One is flat, and the other one is distributed. There are fundamental differences between these two structures. If I were to consider Snowflake as a silver bullet, it should be equally savvy on both ends, which I don't think is the case. Maybe the product has grown and scaled up from where it was."
Matillion ETL is ranked 4th in Cloud Data Integration with 24 reviews while Snowflake is ranked 1st in Data Warehouse with 94 reviews. Matillion ETL is rated 8.6, while Snowflake is rated 8.4. The top reviewer of Matillion ETL writes "Efficient data integration and transformation with seamless cloud-native integration". On the other hand, the top reviewer of Snowflake writes "Good usability, good data sharing and elastic compute features, and requires less DBA involvement". Matillion ETL is most compared with Azure Data Factory, AWS Glue, SSIS, Informatica PowerCenter and Informatica Cloud Data Integration, whereas Snowflake is most compared with BigQuery, Azure Data Factory, Teradata, Vertica and Amazon Redshift.
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