Fivetran replicates applications, databases, events and files into a high-performance data warehouse, after a five minute setup. The vendor says their standardized cloud pipelines are fully managed and zero-maintenance. The vendor says Fivetran began with a realization: For modern companies using cloud-based software and storage, traditional ETL tools badly underperformed, and the complicated configurations they required often led to project failures. To streamline and accelerate…
$0.01
Matillion
Score 8.5 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
Sigma
Score 8.2 out of 10
N/A
Sigma Computing headquartered in San Francisco provides a suite of data services such as code free data modeling, data search and explorating, and related BI and data visualization services.
N/A
Pricing
Fivetran
Matillion
Sigma Computing
Editions & Modules
Starter
$0.01
per credit
Standard
$0.01
per credit
Enterprise
$0.01
per credit
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
Fivetran
Matillion
Sigma
Free Trial
Yes
Yes
Yes
Free/Freemium Version
No
No
No
Premium Consulting/Integration Services
No
Yes
No
Entry-level Setup Fee
Optional
No setup fee
Optional
Additional Details
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Billed directly via cloud marketplace on an hourly basis, with annual subscriptions available depending on the customer's cloud data warehouse provider.
Matillion requires a lot more initial setup effort and the resulting schemas are also much more "raw" data than the nicely cleaned schemas which Fivetran provides. Therefore it would also require more (manual) post-processing efforts compared to Fivetran. So the savings on time …
Fivetran is much easier to set up and maintain. Airbyte still had a degree of technical knowledge requirement that we didn't have the resources to commit. Fivetran allowed a non-technical employee to establish pipelines and immediately start using the data without having to …
Fivetran came well with the connectors' availability and updates with the source changes. We had an idea on data requirements in our case which helped us to work out on cost implication and take a decision for Fivetran as a data provider for our organization. These were 2 …
Fivetran is more intuitive and easier to use than code-based ETL/ELT tools. The data modelling Fivetran performs makes the data more usable more quickly. Fivetran's dbt support and integration is unique.
We evaluated Fivetran and Xplenty prior to choosing Matillion. Matillion was the only solution that easily satisfied our single-tenant to multi-tenant use cases.
If you take the time to build your own with Matillion, you will end up with a vastly better solution than Stitch and Fivetran. We ran Stitch and Fivetran side by side connected from our source DB to both RedShift and Snowflake and documented the performance results as well as …
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 …
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, …
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 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 …
Verified User
Manager
Chose Matillion
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 …
Matillion provided much more flexibility than the other products we tested, at a much lower price point. Other products, in my view, had a cleaner/simpler UI but I also felt that they offered much less functionality. A key design pattern we had to deliver was to perform delta …
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 …
Matillion easily integrates with Snowflake which is a huge selling point. It is also affordable fro the amount of data source connections that it comes with.
Matillion offers the unique capability of digital platform connectors (API connectors) and special functionality for Snowflake (which is our primary database). Also various sources including AWS S3, sFTP and various databases connection. In Pricing, the matillion option has …
We used Airflow for a year before switching to Matillion. We switched to Matillion because the Airflow servers were not stable, and we didn't have any in-house expertise that could manage the Linux OS which Airflow is built on. We were constantly frustrated by the fact that …
Matillion had out of the box support for most of the third party tools we have, plus it integrates well with our data warehouse platform. We found it to meet our use cases after a trial period. It's customizable when you know what you are doing. The use of global and local …
There's a number of systems not available to enter in here that we also took a look at: AWS Data pipeline, Airflow, Xplenty, etc. The reason we chose Matillion is for the balance of features (ability to connect into cloud data sources like Jira), a simple interface to put …
Matillion is the best alternative for on-premise ETL tools if you are looking for a cloud-only solution. None of the tools we evaluated provide same features offered by Matillion. Extensive technical documentation is available on the website which makes development easy and …
Sigma is definitely more user-friendly and has powerful built-in functions and capabilities for analyzing and visualizing data in a low-code fashion. It allows our non-technical users to jump in and use data to answer the questions they are asking without having to wait for …
microstrategy is too complex and expecting data warehouse in perfect Snowflake layout, otherwise all automated joins are getting messed up. Power BI requires to do tons of complex DAX transformations for even getting simple answers and has power limitations.
ease of implementation , easy to train the resources to get used to the tool as it has very user friendly user interface, the 14 days trial where sales and sigma technical team helped us understand the advantages and the helpline chat which is always helpful . licensing when it …
Easy entry tool that can grow with you as the data expertise and literacy grows within the org. Start up effort and cost is too high in the tools that are built for large corporate solutions.
Sigma is like a unique combination of Excel and Tableau, in my experience. It combines both the heavy-duty data wrangling and data manipulation abilities of Tableau with the quickness and ease of use that Excel is known for having. I have relied on Excel a lot less now for ad …
Sigma Computing was our top choice due to the ease of use of the platform for end users doing self-exploration of data. The structure of the platform for how datasets are created and access provisioned was much better than other products we considered. Sigma Computing may not …
Sigma's combination of key features from each of these with native dB access under a UI that makes collaboration easy has made it an easy choice for us.
Fivetran's business model justifies the use-case where we require data from a single source basically a lot of data but if the requirement is not on the heavier side, Fivetran comes to costly operation when compared to its peers. Otherwise, I'll recommend Fivetran for stability and update and seamless service provider.
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.
We were able to set up client-facing embedded reports with ease and security. The interface is not difficult to learn, although we may not be aware of or lack the necessary expertise to utilize more advanced features that would likely benefit us.
Matillion is brilliant at importing data -- it would be amazing to have more ways to export data, from emailed exports to API pushes.
Any Python that takes more than a few lines of code requires an external server to run it. It would be great to have more integration (perhaps in a connected virtual environment) to easily integrate customized code.
Troubleshooting server logs requires quite a bit of technical expertise. More human readable detailed error handling would be greatly appreciated.
Sigma Computing does not allow custom ordering of pivot fields in pivot tables easily
Sigma Computing lacks functionality for creating tables or sections that dynamically adjust to the browser window's height while maintaining a fixed height textbox at the bottom
Sigma Computing does not provide straightforward options for formatting totals in tables, such as renaming 'Total' to 'Average', 'Team Total', etc
Sigma Computing does not support searching by individual tab names within a workbook
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.
Sigma has helped us a lot and has become an integral part of our daily workflow. It would be difficult to switch to another platform and have to rebuild the numerous metrics and performance reports that we have already established
Very easy and intuitive to setup and maintain as there usually are not that many options. Very well documented (e.g. how to setup each connector, how the schema looks like, any specific features of this connector etc.). Also the operation is intuitive, e.g. you have status pages, log pages, configuration pages etc. for each connector.
We are able to bring on new resources and teach them how to use Matillion without having to invest a significant amount of time. We prefer looking for resources with any type of ETL skill-set and feel that they can learn Matillion without problem. In addition, the prebuilt objects cover more than 95% of our use cases and we do not have to build much from scratch.
It has a clean and modern interface. However, it is not completely intuitive. I think it would be better and easier to navigate with more Windows style drop down menus and/or tabls. There is a significant learning curve, but that may be due in part to the technical nature of this type of software tool.
It runs pretty well and gets our data from point A to point cluster quickly enough. Honestly, it's not something I think about unless it breaks and that's pretty rare.
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.
They are very friendly and informative. They are quick in resolving our queries and help us understand very minute things as well. They are quick in creating feature tickets based on our custom requirements, and they would also create a bug ticket if there is any discrepancy and get that checked on time.
We never seriously considered using anything else. Our data engineers had used Fivetran extensively in previous roles so when it came time to make a decision, there wasn't much of a process. They gladly signed the contract with Fivetran pretty quickly.
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 is essential to deciding which tool fits better. For the transformation part, dbt is purely (SQL-) code-based. So, it is mainly whether your developers prefer a GUI or code-based approach.
With Looker, to be effective, a substantial amount of coding & modeling needs to happen in LookML. Being another language to learn, users have to context switch again from at a minimum either SQL or Python into LookML. The concept of being able to source control, code review, and deploy your models is a plus though.
Tableau is the gold standard for data visualization, no question. Power users will be able to create dazzling content that Sigma won't necessarily be able to easily match. However, since development usually happens via an extract, helping other users troubleshoot is an arduous process. Trying to re-do or un-do all the transformations and calculations that cause a certain number is very difficult.
With Sigma, all the queries happen directly against Snowflake and you can see the query logs. The data modeling happens right in a tabular, spreadsheet-like manner, so within only a few minutes, substantial transformations can happen, with visualizations just a few more clicks away.
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.
Monitoring health of cloud platform has allowed the company to anticipate issues before they affect customers – Sigma prompted us building a canary monitoring process that provides customer container health.
Customer success has used an activity report to discover customers running runaway processes that they were unaware of, creating an alert to contact the customer and prevent an embarrassing situation.
Customer success uses the activity report to prompt conversations regarding increases or declines in behavior that led to increasing contract limits or addressing churn concerns.