Fivetran vs. Keras

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.

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Keras is quite perfect, if the aim is to build the standard Deep Learning model, and materialize it to serve the real business use case, while it is not suitable if the purpose is for research and a lot of non-standard try out and customization are required, in that case either directly goes to low level TensorFlow API or Pytorch

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• Easily connects to source data using delivered connectors
• Transforms data into standard models and schemas
• Has very good documentation to help quickly setup connectors

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• One of the reason to use Keras is that it is easy to use. Implementing neural network is very easy in this, with just one line of code we can add one layer in the neural network with all it's configurations.
• It provides lot of inbuilt thing like cov2d, conv2D, maxPooling layers. So it makes fast development as you don't need to write everything on your own. It comes with lot of data processing libraries in it like one hot encoder which also makes your development easy and fast.
• It also provides functionality to develop models on mobile device.

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• Very difficult to get connectors enhanced if a specific needed object is not supported by them
• Depending on the edition needed and the data volumes, can get quite expensive

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• As it is a kind of wrapper library it won't allow you to modify everything of its backend
• Unlike other deep learning libraries, it lacks a pre-defined trained model to use
• Errors thrown are not always very useful for debugging. Sometimes it is difficult to know the root cause just with the logs

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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.

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I am giving this rating depending on my experience so far with Keras, I didn't face any issue far. I would like to recommend it to the new developers.

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.

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Keras have really good support along with the strong community over the internet. So in case you stuck, It won't so hard to get out from it.

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.

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Keras is good to develop deep learning models. As compared to TensorFlow, it's easy to write code in Keras. You have more power with TensorFlow but also have a high error rate because you have to configure everything by your own. And as compared to MATLAB, I will always prefer Keras as it is easy and powerful as well.

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• It has been very positive in serving BI team with new source requests
• It has been OK at scaling to match as data volumes as source data size grows

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• Easy and faster way to develop neural network.
• It would be much better if it is available in Java.
• It doesn't allow you to modify the internal things.

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