Matillion vs. ThoughtSpot

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.

Incentivized

Well suited for my clients for a Macro view of business insights, credit risk, ESG insights, etc. However, I think ThoughtSpot needs to make exploring underlying data easier, adding a feature where you can click on charts and filters will help users and especially business users to easily understand the granularities as well.

Incentivized

• 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.

Incentivized

• Beautiful visualizations. The visuals are distinct, clean, and easy to discern from one another.
• Intelligent querying functionality. When looking to manipulate the data, the search function makes it easy to manipulate the features in the data, along with aggregating them in the way you'd like.
• Embedding! It has been a smooth process thus far for our product & technical teams to work with ThoughtSpot and bring it into our product.

Incentivized

• I think a non-CLI approach to installing and uninstalling python libraries would be nice. I mean, it isn't difficult to install a python library via Linux or CLI, but I imagine most companies don't feel comfortable allowing Matillion users to go on a virtual server and installing it themselves. Requirement.txt file for installing libraries would be simple, and maybe that could also be used to uninstall libraries as well......or maybe the library gets automatically downloaded if it is imported into a python script but the library doesn't exist.
• Python Component is lacking very much in terms of UI. It would be unrealistic for me to suggest Matillion build its own EDI, but it would be nice if a python component could connect to a local IDE. Right now, if you want to write any decent length python code, you are going to be stuck copying and pasting your code from your local IDE.
• It is also very difficult to debug in Matillion because you can't set breakpoints. Local IDE integration can resolve that.
• I would like to have more templates to copy from for certain simplistic scenarios. For instance, a template for a job that fails which sends an AWS SNS with the Job name, component it failed on, and the error message. It wasn't as simple as I thought it would be to figure out and having to use a shared job for such circumstances can be painful because you have to export a bunch of variables.
• There should be a drop down list for Global Matillion variables as it is difficult to remember at times.

Incentivized

• Ability to build a single dashboard tile containing the main value, a sparkline, and a percent change over time
• Easy way to build a user experience that allows for selecting different metrics to trend from a drop-down on a multi-axis graph

Incentivized

With the current experience of Matillion, we are likely to renew with the current feature option but will also look for improvement in various areas including scalability and dependability. 1. Connectors: It offers various connectors option but isn't full proof which we will be looking forward as we grow. 2. Scalability: As usage increase, we want Matillion system to be more stable.

Incentivized

I give it just waiting because passport is brilliant and it has helped our organisation In advancing to the next stage in the age of AI. It has allowed or non-tech people to better service and clients in a cost-effective way. George port has allowed us to create new products for us and for our clients increasing our revenue streams and reducing clients churn

Incentivized

It has been easy to train new employees who don't have previous experience with Matillion. It is quite self-explanatory. There are quite a few things that can be done with Python, however, we have not really looked into this feature much but likely will do in the future. Mostly, it is drag and drop of components and environments can be set up so easy to set up connections as well.

Incentivized

The tool is easy to use if you know what you are doing and looking for. I know as they work towards improvements and simple language it will become even easier, but as of now they are doing a great job but there is room for improvement.

Incentivized

it's available unless there is a server or system update etc. sometimes the timing of this is bad (for example during a month end close)

Incentivized

It does what it is supposed to. Would be nice to have a bit more insights

Incentivized

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.

Incentivized

I give it this meeting because the team is not only help able to help us in the current solutions but also amazing and taking feedback and feeding it back to their development team which includes more products and features into ThoughtSpot

Incentivized

inhouse in-person training. Took a bit to long to get the basics.

Incentivized

poor instructions

Incentivized

We were able to control on access and built various enviroment for implementation

Incentivized

Understand use case and model and design accordingly

Incentivized

Matillion ran circles around Stitch and Striim both in functionality, setup, and performance. There was no real comparison. Fivetran massively outperforms Matillion in pretty much every facet of the production from setup, maintenance, visibility, and usability. It already has the ability to connect any data source to a destination regardless of database type. Why we chose Matillion over Fivetran is that, for our current needs, Matillion provides us with the functionality that we need and a much more competitive price for a smaller company.

Incentivized

We also explored Tableau Ask Data. Tableau is our standard for BI in our organization. We want to use the smallest amount of tools in our company to have the best adaption. ThoughSpot will fill a few gaps that we have with our current set up and will also enhance out offering for our employees in the transition of being more data driven within in near future

Incentivized

I have been able to connect Matillion to AWS Aurora Databases, MySQL databases, Rest APIs, Files in AWS S3, etc. Being able to load all of that disparate data into one datalake has made data mining and reporting a lot simpler. I wish everything could be implemented as easily as Matillion.

Incentivized

Because it is very reliable, inside the situation, we need strong internet connection to access a lot of data but easily never had any downtime except during the upgrades

Incentivized

• Saving us time reduces our need for headcount
• Allows us to collaborate on data-eng pipelines in a transparent way with non-technical stakeholders, ensuring accuracy and continuity
• Allows us a single platform to log and manage all of our pipelines to pin-point where something failed and why
• Always has a solution to very common data engineering tasks, i.e., real-time data, connectors, pre-built workflows, etc.

• Positive ROI immediately seen for first POC.
• Saved an immense amount of time for a sole analyst on a team by enabling the end-users to ask their own questions instead of always turning to her, allowing her to focus on other priorities.
• Immediate answer to question on second POC that led to personnel changes.

Incentivized