Fivetran vs. Hevo Data vs. IBM Data Replication

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.

Incentivized

It is of great help for unstructured data sources. The way Hevo Data flattens the high nested data is amazing. Schema management is also good by Hevo Data. The way it's tell about the data type and then we can identify any error in the model. Additionally, It is very easy to setup for any new user and once model is created then we do not have to worry about the script maintenance and updating the script daily.

Incentivized

IBM InfoSphere Data Replication replication allows work on the daily operation and analyzes the replicated data in real-time with no downgrade of performance. This happens thanks to the incremental data load using the log files instead of querying the database for any changes. It serves as an online backup of the database uploading the data to the cloud in real-time.

Incentivized

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

Incentivized

• Extract
• Load
• Transform
• Support

Incentivized

• Performance to detect changes using Incremental Data Delivery with logs.
• Update data in the cloud in real time.
• Saves data in clusters using Apache Hadoop

Incentivized

• 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

Incentivized

• Support - their support teams try to be helpful, but often miss the mark
• Error logging - we've run into a few issues debugging errors and limited support is provided by Hevo

Incentivized

• Better documentation and examples of Console and API.
• Integration with machine learning to analyze the data.
• Examples of how to replicate data from multiple sources and databases to one data lake.

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

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.

Incentivized

1. Cost efficient 2. Creation of automated pipeline 3. Can load data from multiple data sources 4. Updates data in near real-time - We were able to get near real time insights from the data model which we have created in hevo 5. It has good integration with different BI tools

Incentivized

• 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

Incentivized

• Comprehensive results from the data processing is a perfect approach from the tool.
• Authentic data models brings a high automation process and more productivity.
• Expansive data sourcing brings clarity and genuine results after inferential analytics.

Incentivized

• Analize the daily data in real time using data lakes and Machine Learning.
• Performance of the replication without affecting the relational database of the applications.
• Use of Apache Hadoop for data clustering improving performance.

Incentivized