Databricks Data Intelligence Platform vs. IBM StreamSets

If you need a managed big data megastore, which has native integration with highly optimized Apache Spark Engine and native integration with MLflow, go for Databricks Lakehouse Platform. The Databricks Lakehouse Platform is a breeze to use and analytics capabilities are supported out of the box. You will find it a bit difficult to manage code in notebooks but you will get used to it soon.

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

• There is databricks community, which is a free version. It is available for beginners to have an easy start with a big data platform. It does not have every feature of the full version but is still adequate for extremely new coders.
• There are many resourceful training elements that are available to developers, data scientists, data engineers and other IT professionals to learn Apache Spark.

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

• Connect my local code in Visual code to my Databricks Lakehouse Platform cluster so I can run the code on the cluster. The old databricks-connect approach has many bugs and is hard to set up. The new Databricks Lakehouse Platform extension on Visual Code, doesn't allow the developers to debug their code line by line (only we can run the code).
• Maybe have a specific Databricks Lakehouse Platform IDE that can be used by Databricks Lakehouse Platform users to develop locally.
• Visualization in MLFLOW experiment can be enhanced

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 it is an amazing platform for designing experiments and delivering a deep dive analysis that requires execution of highly complex queries, as well as it allows to share the information and insights across the company with their shared workspaces, while keeping it secured.

in terms of graph generation and interaction it could improve their UI and UX

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

One of the best customer and technology support that I have ever experienced in my career. You pay for what you get and you get the Rolls Royce. It reminds me of the customer support of SAS in the 2000s when the tools were reaching some limits and their engineer wanted to know more about what we were doing, long before "data science" was even a name. Databricks truly embraces the partnership with their customer and help them on any given challenge.

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

Databricks is a true all-in-one platform, and at the time of implementation, it had more features available to us, making it a clear choice over Snowflake. Moving our workloads from local computing to the servers in Databricks gave our start-up staff a great quality of life boost.

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

• ROI for us has been tremendous. Time to market by processing raw data in our big data infrastructure has been pretty fast.
• Non engineers can easily use Databricks, hence helping business customers.
• Thousands of different data combinations can easily be joined and used by our data teams.

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