Fivetran 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

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

• 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

• More detailed logging
• More flexible choices for time range over which records are synced
• More options for masking and excluding sensitive data

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

Just need to input connection info.

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

• Development cost have reduced for each connector
• The pay-per-use model is still not out their which requires lot of overhead cost

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