Azure Databricks vs. IBM DataStage

Centralised notebooks are out directly into production. This can lead to poorly engineered code. It is very good for fast queries and our data team are always able to provide what we ask for. It is a big cost to our business so it is important it runs efficiently and returns on our investment.

Incentivized

DataStage is somewhat outdated for an ETL. I guess that's what makes it a bit lagged behind its competitors. It can be used for data processing, sure, but its performance seems to be lagging behind or quite slow given the server it is running from. I won’t depend on this application if it's handling a lot of mission-critical banking and business data.

• Data Processing and Transformations based on Spark
• Delta Lakehouse when clubbed with an external cloud storage
• Governance using Unity Catalog to unify IAM
• Delta Live Tables is a product, which although relatively newer, has a great potential with the visuals of a pipeline.

Incentivized

• Connect to multiple types of data-sources including Oracle, Teradata, Snowflake, SQl Server.
• Powerful tool to load large volumes of data.
• Transformation stages allow us to reduce the amount of code needed to create ETL scripts.
• Allow us to synchronize and refresh data as much as needed.

Incentivized

• Intuitive interface
• Ease of use
• Providing FAQ or QRGs

Incentivized

• Technical support is a key area IBM should improve for this product. Sometimes our case is assigned to a support engineer and he has no idea of the product or services.
• Provide custom reports for datastage jobs and performance such as job history reports, warning messages or error messages.
• Make it fully compatible with Oracle and users can direct use of Oracle ODBC drivers instead of Data Direct driver. Same for SQL server.

Incentivized

The developers are able to switch between Python and SQL in the Notebook which allows the collaboration of SQL analyst and Data scientist. The integration of Mosaic AI allows users to write complex codes in natural languages. Unity catalog has centralized the security and governance features and simplified the process of maintaining it

Incentivized

Because it is robust, and it is being continuously improved. DS is one of the most used and recognized tools in the market. Large companies have implemented it in the first instance to develop their DW, but finding the advantages it has, they could use it for other types of projects such as migrations, application feeding, etc.

Incentivized

It could load thousands of records in seconds. But in the Parallel version, you need to understand how to particionate the data. If you use the algorithms erroneously, or the functionalities that it gives for the parsing of data, the performance can fall drastically, even with few records. It is necessary to have people with experience to be able to determine which algorithm to use and understand why.

Incentivized

IBM offers different levels of support but in my experience being and IBM shop helps to get direct support from more knowledgeable technicians from IBM. Not sure on the cost of having this kind of support, but I know there's also general support and community blogs and websites on the Internet make it easy to troubleshoot issues whenever there's need for that.

Incentivized

I have found Azure Databricks to be much better than Snowflake for handling bigger, diverse data types. Snowflake is much simpler and better for smaller warehousing. The real time processing is much better in Azure Databricks and we have much more language options. Snowflake is more expensive but simpler to use. Both are great for different needs.

Incentivized

With effective capabilities and easy to manipulate the features and easy to produce accurate data analytics and the Cloud services Automation, this IBM platform is more reliable and easy to document management. The features on this platform are equipped with excellent big data management and easy to provide accurate data analytics.

Incentivized

• The support team is amazing, they help you at every stage of the projects, from sales to delivery.
• On a framework level, it has had an amazing impact and has reduced the clients overall data platform costs by a staggering 65%
• There has been a 40% Manual work requirement on average for the clients when they move to Azure Databricks Data Platform

Incentivized

• It’s hard to say at this point, it delivers, but not quite as I expected. It takes a lot of resources to manage and sort this out (manpower, financial).
• Definitely, I don’t have the exact numbers, but given the data it processes, it is A LOT. So props to the developer of this application.
• Again, based on my experience, I’d choose other ETL apps if there is one that's more user-friendly.