Datameer vs. Fivetran

Datameer's workbooks include an intuitive GUI for maximum usability. Anyone with an analytical mind may easily compile data using the built-in worksheet capability without requiring much technological knowledge. Ad hoc reports may now be made and scheduled. In addition, I haven't had too much problem fixing things because I can get most of the answers I want there.

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

• It leverages scalability, flexibility and cost-effectiveness of hadoop to deliver an end-user focused analytic platform for big data without involvement of IT.
• It overcomes Hadoop`s complexity by providing GUI interface with pre-built functions across integration, analytics and data visualization .
• Excel feature is awesome for business users which is already provided by Datameer.
• Using datameer now user can do smart analytic using Decision Trees, Column dependency and recommendation.
• Recently HTML5 inclusion is making application to available on a wider range of devices, including the iPad and other mobile devices which does not support Flash.
• It can be used in premise or in a cloud computing environment.
• Wizard-based data integration designed for IT and business users to schedule and do transformation of large sets of structured, semi-structured and unstructured data without any knowledge of Hadoop ecosystem.

• 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

• Data visualization and graphics. Right now it's not very user friendly. Creating dashboards requires a lot of work that can be done by the application

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

Employees with intermediate SQL and Hive knowledge can generate reports faster than using Datameer . It does have visualization tool but I don't think it is anything that cannot be accomplished by importing the data in Excel

Easy to use for most things, starts to require some planning as your projects get more complex.

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

I have compared Datameer with the tools listed above and it blows them out of the water. Datameer is much more user friendly than these tools and is also a lot more powerful. Once the Q1 release comes out with drill down capabilities on the infographics, Datameer will surpass Tableau and Spotfire in visualization.

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

• Quick time to value. Transition from developer to learning Datameer is less than a day.
• Workspace is drag and drop, which makes it intuitive and easy to use.
• Speed of data transformations.
• Pulling data from various sources.
• Easy to schedule data pipelines.
• Tailor made for Snowflake and Redshift.
• Documentation is sometimes not updated quickly.
• Training videos are a bit long.

• 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