Adverity vs. Fivetran

If you're a marketing organization that wants to own your data architecture, get away from hours of manual excel work, and build a flexible and scalable BI practice for yourself and your clients, Adverity is your solution. Especially if you don't have a dedicated analytics team or current database solution. If you have access to a wide variety of API's and a fully staffed team to custom develop new connectors in under 30 days for no incremental cost, you're probably best in-housing

Incentivized

[Fivetran is] very well suited when you are using popular and common data sources, such as the major ad platforms, and SaaS platforms such as Salesforce. If the majority of your data sources are custom internal applications or databases, may be less value as you aren't leveraging the delivered connectors.

Incentivized

• Customizable dashboards, that you could change and make unique to your specific need / business.
• Combining a range of data sets into one view.
• Easily sharable insights, that you can share internally or also with clients.

Incentivized

• Simplified ETL from a wide range of data sources
• Stable and painless data pipeline
• Granular control over what parts of the data source are loaded

Incentivized

• Adverity is running into some issues being able to custom-develop API connectors at scale/speed. This is mostly because of their rapid growth and incredible core product. If you need custom development, be prepared to use one of the many easy-to-use workarounds Adverity offers for endpoints they don't have a direct integration with. Don't worry - you'll get your data either way.

Incentivized

• Doesn't include support for a Kinesis stream as a data source so couldn't be used for some use cases under consideration
• Doesn't support the use of "BEFORE DELETE" triggers
• No support for serverless Aurora

Incentivized

Adverity is easy to understand and use, even for users who are less technical. Reports and dashboards are easy to send out, and back-end users can easily navigate workspaces for creating new data fetches.

Incentivized

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.

Incentivized

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.

Incentivized

Previously I had never really used a tool like this, only using software such as GA to look at any insights and review performance. So I think that the combined view of having all of the data sets (Which in my role I wouldn't always particularly have access to), was really useful. As an individual who was optimising our clients websites it meant that I was really able to prioritise what was important, but also have a great view of absolutely all activity that was happening

Incentivized

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 places where Fivetran out-performed, other vendors.

Incentivized

• Scale - the biggest advantage has been the ability to extract, transform, and load data for 150+ clients with a three-person team without a software or data engineering team to support. Adverity would easily allow us to scale up to double or triple the client load if needed.
• Templates - Another advantage related to scale, Adverity allows us to run a templated approach to extraction, transformation, and also data visualization and reporting so that onboarding is very fast. Typical clients with up to 15 data sources can be onboarded in as little as a couple of hours.

Incentivized

• Saved a lot of manual development days (unable to quantify)
• Accelerated the time to add a new source to the data warehouse a lot

Incentivized