Azure Data Lake Storage vs. IBM Db2 Big SQL

Azure Data Lake storage is well suited for applications/use cases within organizations where capturing and storing large amounts of data in any format is required, primarily for storing and processing purposes. It's an easy and cost-effective cloud solution for your application data. The ability to integrate with other Azure Services like Azure Databricks and Azure Data Factory is superb.

Incentivized

IBM Db2 is a legacy database and is primarily great for supporting certain legacy applications. It's simply not as competitive as many solutions on the market now.

Incentivized

• Azure Data Lake Storage is extremely scalable. It allows us to scale up or down endlessly based on what we need including replication.
• In terms of security, Azure Data Lake Storage fits our requirements really well as we can monitor and encrypt seamlessly. We can also assign permissions through roles and grant network-level access.
• Due to the fact that it can scale, we are able to monitor the cost of storage and any given time and make financial decisions about our infrastructure based on how small or big we want to scale.

Incentivized

• data storage
• data manipulation
• data definitions
• data reliability

Incentivized

• I'd like to see a better cross-platform native client. Azure Data Explorer is fine, but it's far from the "SSMS" kind of experience SQL Server users are used to.
• Listing a large number of file is somewhat problematic and slow. Using the native C# library, running directly on an Azure VM, it can take several hours to list just a couple million files.
• Switching from V1 to V2 requires the creation of a new Storage Account and that's pretty inconvenient.

Incentivized

• Cloud readiness.
• Ease of implementation.

Incentivized

IBM DB2 is a solid service but hasn't seen much innovation over the past decade. It gets the job done and supports our IT operations across digital so it is fair.

Incentivized

IBM did a good job of supporting us during our evaluation and proof of concept. They were able to provide all necessary guidance, answer questions, help us architect it, etc. We were pleased with the support provided by the vendor. I will caveat and say this support was all before the sale, however, we have a ton of IBM products and they provide the same high level of support for all of them. I didn't see this being any different. I give IBM support two thumbs up!

Incentivized

The Azure Data Lake solution is designed for organizations that want to take advantage of big data. It provides a data platform that can help developers, data scientists, and analysts store data of any size and format and perform all types of processing and analytics across multiple platforms and programming languages. It can work with your existing solutions, such as identity management and security solutions. It also integrates with other data warehouses and cloud environments. It can be useful for organizations that need the above softwares.

Incentivized

MS SQL Server was ruled out given we didn't feel we could collapse environments. We thought of MS-SQL as more of a one for one replacement for Sybase ASE, i.e., server for server. SAP HANA was evaluated and given a big thumbs up but was rejected because the SQL would have to be rewritten at the time (now they have an accelerator so you don't have to). Also, there was a very low adoption rate within the enterprise. IBM DB2 Big SQL was not selected even though technically it achieved high scores, because we could not find readily available talent and low adoption rate within the enterprise (basically no adoption at the time). We ended up selecting Exadata because of the high adoption rate within the enterprise even though technically HANA and Big SQL were superior in our evaluations.

Incentivized

• The cost can be high for more advanced work. In some cases, for instance, time limits and lab runtimes may be too short if you are too slow to learn what is explained as you go along.
• promote flexible team communication. You can create different spaces for different teams, and share files and tasks.

Incentivized

• Performance gains were positive.
• Finding resources on the street with knowledge at the time was hard.

Incentivized