Azure Stream Analytics vs. IBM StreamSets

Data enrichment is effectively done in stream analytics also checking the values with different functionality like windowing and group by clause is effectively working.

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

• Routing of data from multiple inputs to multiple output
• You create your own user define function.
• Intermediate query is working very effectively.

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

• Code competency is not that much effective
• Ml models can't be integrated with stream analytics

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

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

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

Azure Stream Analytics is easy to implement and also to integrate compare to other services like iot analytics

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

• Very nice roi while using it.
• Multiple integration is the best functionality

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