Apache Hadoop vs. IBM Db2 Big SQL

Apache Hadoop (and its subsequent add-ons) are well-suited to larger, unstructured data flows, such as aggregation of web traffic or advertising. Geospatial algorithms and their outputs are well-suited for this kind of aggregation as structuring that data is challenging, but leaving it unstructured and performing queries as-needed is a better fit for most business models. With the advent of data science, I would expect Hadoop fits a LOT of their initial outputs quite well.

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

• HDFS is reliable and solid, and in my experience with it, there are very few problems using it
• Enterprise support from different vendors makes it easier to 'sell' inside an enterprise
• It provides High Scalability and Redundancy
• Horizontal scaling and distributed architecture

Incentivized

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

Incentivized

• Hadoop is a batch oriented processing framework, it lacks real time or stream processing.
• Hadoop's HDFS file system is not a POSIX compliant file system and does not work well with small files, especially smaller than the default block size.
• Hadoop cannot be used for running interactive jobs or analytics.

Incentivized

• Cloud readiness.
• Ease of implementation.

Incentivized

Hadoop is organization-independent and can be used for various purposes ranging from archiving to reporting and can make use of economic, commodity hardware. There is also a lot of saving in terms of licensing costs - since most of the Hadoop ecosystem is available as open-source and is free

Great! Hadoop has an easy to use interface that mimics most other data warehouses. You can access your data via SQL and have it display in a terminal before exporting it to your business intelligence platform of choice. Of course, for smaller data sets, you can also export it to Microsoft Excel.

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

We went with a third party for support, i.e., consultant. Had we gone with Azure or Cloudera, we would have obtained support directly from the vendor. my rating is more on the third party we selected and doesn't reflect the overall support available for Hadoop. I think we could have done better in our selection process, however, we were trying to use an already approved vendor within our organization. There is plenty of self-help available for Hadoop online.

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

Hadoop is a complex topic and best suited for classrom training. Online training are a waste of time and money.

I feel that this is a highly reliable and scalable solution computing technology that is highly capable of processing large data sets across multiple servers and thousands of machines in a well-defined and distributed manner. Apache Hadoop can automatically scale up the number of servers and machines that are needed to process, store, and analyze data sets. It also handles explosions in data with big data technology. Apache Hadoop is good at handling all node failures as well.

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

• As it was open source makes it popular choice for handling large chuck of datasets
• It was free earlier but now it’s licensed but still enterprise is a fine tuned version which makes it easier for new users and administrators to use it
• Our investment is worth every single penny.
• Initial cost is more as you might need to hire administrators to setup the cluster and make them in scalable. But once done it’s pretty easy

Incentivized

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

Incentivized