Fivetran vs. IBM StreamSets

[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

Because real-world sources often change (new fields get added, formats get tweaked, etc.), StreamSets helps detect and adapt to those "schema drifts" or changes automatically, or with minimal manual intervention. That makes pipelines more resilient and significantly reduces the maintenance burden. Therefore, data sets with constantly changing sources/formats are great for StreamSets.

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

• it connects to many data sources and helps catch issues early with built-in alerts and monitoring tools
• it supports real-time and batch processing, handles data drift well, and makes pipeline debugging easier with the updated UI

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

• Where the person's skillsets in data analysis is not of an expert.
• Data monitoring and analysis.
• Customer data for better customer acquisition

Incentivized

IBM Stream sets has been a wonderful addition to our technology stack. It has helped in some of our initiatives such as data engineering, data integration for not only external customers but also for internal purposes. The tool has also helped on our use cases related to streaming data. Moving to another tool would require significant amount of work and time.

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

because i think that overall the solution is having a positive impact on the business, it allows multiple benefits in simplification of the tasks and is capable of doing multiple process that are usually done by a combination of man and systems, reducing the time and effort required to have the data.

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

Streamsets support has improved a lot in the last couple of years. We had some challenges in the beginning with support, but now the quality of the support and the responsiveness to tickets are better. We have contacted support multiple times when it came to scenarios where the system was slow or the output as not as we expected

Incentivized

I was not involved in the implementation

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

Before, we were using Informatica since most of our applications were running on on-prem servers. Later, when we started moving to the cloud, we tried Informatica Cloud, but it's more useful for batch-oriented than streaming. That's why one of our tech architects suggested IBM StreamSets for our real-time data streaming. During the POC stage, we were happy that the data streaming was way better with IBM StreamSets compared to the Informatica Cloud way of doing.

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

• Reduced manual handling, cutting down operational costs for our team.
• It also accelerated our time to Insight, which has eventually led to faster decision making.
• Data quality is improved.

Incentivized