Databricks Data Intelligence Platform vs. IBM InfoSphere Information Server vs. Oracle Exadata

Medium to Large data throughput shops will benefit the most from Databricks Spark processing. Smaller use cases may find the barrier to entry a bit too high for casual use cases. Some of the overhead to kicking off a Spark compute job can actually lead to your workloads taking longer, but past a certain point the performance returns cannot be beat.

Incentivized

Information Server is extremely useful to replace manual developments that require a lot of coding effort. It significantly increases the productivity of the initial development and the future maintenance of the processes since it has a visual development environment with self-documentation.

Oracle Exadata is well-suited for environments where massive performance for Oracle databases is required. Storage indexes reduce the unnecessary I/O. Smart Flash
Cache accelerates random reads/writes.

Our OLTP application demands very high concurrency. Multi-node Exadata provides high availability and zero downtime during DB patching. It comes with lots of built-in automations, so it reduces many routine tasks for sysadmins, like network, storage, and VM configuration, and it also reduces many Oracle DBA tasks, like Oracle software installation, patching, and upgrades.

Incentivized

• Process raw data in One Lake (S3) env to relational tables and views
• Share notebooks with our business analysts so that they can use the queries and generate value out of the data
• Try out PySpark and Spark SQL queries on raw data before using them in our Spark jobs
• Modern day ETL operations made easy using Databricks. Provide access mechanism for different set of customers

Incentivized

• IIS best for ETL ,not ELT , and many and diffrent source systems.
• It also can process big data , unstuctured data
• It is not only DWH , you can use infosphere for analys and see the bigger architecture of your OLTP systems

Incentivized

• Oracle Database : Deliver industry-leading security, high availability and scalability with Oracle Database, which has been significantly enhanced to take advantage of the Oracle Exadata Storage Servers.
• Exadata Smart Scan : Improve query performance by offloading intensive query processing and data mining scoring to scalable intelligent storage servers.
• Smart Flash Cache : Transparently cache 'hot' read and write data to fast solid-state storage, improving query response times and throughput. Exadata systems use the latest PCI flash technology rather than flash disks. PCI flash delivers ultra-high performance by placing flash directly on the high speed PCI bus rather than behind slow disk controllers.
• Hybrid Columnar Compression : Reduce the size of data warehousing tables by 10x, and archive tables by 50x, to improve performance and lower storage costs for primary, standby, and backup databases. Query high, query low, archive high and archive low.
• Infiniband Network : Connect multiple Oracle Exadata Database Machines using the InfiniBand fabric to form a larger single system image configuration. Each InfiniBand link provides 40 Gigabits of bandwidth–many times higher than traditional storage or server networks.
• Petabyte Scalability : Easily scale data warehouse to support enterprise data growth.

Incentivized

• Sometimes, when multiple jobs depend on each other in different environments, it is not always easy to see the full workflow in one place.
• It is sometimes difficult to determine which job or cluster contributes more to the overall cost.
• For beginners, cluster configuration may be a little difficult. So more recommendation in the platform can help.

Incentivized

• I would be nice to have a new web development environment for DataStage.
• Connectivity Packs such as Pack for SAP Application are a little pricey.
• It is confusing for new developers the possibility of developing jobs using different execution engines such as Parallel or Server.

Incentivized

• The process of patching and upgrade of Exadata server components could be improved with a goal to minimize the overall effort, make it fully automated and transparent.
• Improved guidelines and possibly more sophisticated tools for sizing of new Exadata servers for migration from old legacy hardware.

Incentivized

• Scale of implementation
• IBM techsupport

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

I am comparing Exadata with the Oracle RAC database experience. In addition to Oracle RAC features, Exadata provides automatic performance optimization through Smart Scan and storage indexes. Deep integration with the Oracle ecosystem and tight coupling with Oracle Enterprise Manager
for monitoring and management. Some downsides of Exadata are: a steep learning curve, concepts like cell offloading, IORM, and flash cache behavior aren’t intuitive initially. Operating
Exadata requires specialized DBA skills.

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.

The most important differentiating factor for Databricks Lakehouse Platform from these other platforms is support for ACID transactions and the time travel feature. Also, native integration with managed MLflow is a plus. EMR, Cloudera, and Hortonworks are not as optimized when it comes to Spark Job Execution. Other platforms need to be self-managed, which is another huge hassle.

Incentivized

DataStage is more robust and stable than ODI The ability to perform complex transformations or implement business rules is much more developed in DS

Oracle Exadata Database Machine had the best performance overall hands down. It clearly beat the competition and we were seeing 1000X improvement on SAP HANA. Oracle Exadata Database Machine beat that without us refactoring our code. To achieve that in HANA, we had to refactor the code somewhat. Now this was for our limited POC of 5 use cases. Given the large number of stored procedures we had in Sybase, we need to capture more production metrics but we are seeing incredible performance.

Incentivized

• The ability to spin up a BIG Data platform with little infrastructure overhead allows us to focus on business value not admin
• DB has the ability to terminate/time out instances which helps manage cost.
• The ability to quickly access typical hard to build data scenarios easily is a strength.

Incentivized

• Productivity of the development of integration processes.
• Better documentation and governance.
• Reduce training costs of various technologies.

• Single support from a single vendor with both machine and database from Oracle, which is costing us less.
• With Exadata, we need less technical manpower and less technical support. A business transaction with the integrated and centralized database helps us focus on other business needs.
• We don't need to buy additional licenses and Hardware for the next 3 to 5 years.

Incentivized