IBM Cloud Pak for Data vs. Vertica Analytics Database

IBM Cloud Pak for Data with Netezza is well suited for clients who require fast, economical analytics processing. It is not designed to be used as a transactional processing environment. For example, a large customer is using it during the point of sale process. That makes little sense in that business case. However, to take analysis to market faster, it excels well in that space.

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Vertica as a data warehouse to deliver analytics in-house and even to your client base on scale is not rivaled anywhere in the market. Frankly, in my experience it is not even close to equaled. Because it is such a powerful data warehouse, some people attempt to use it as a transactional database. It certainly is not one of those. Individual row inserts are slow and do not perform well. Deletes are a whole other story. RDBMS it is definitely not. OLAP it rocks.

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• Increases our impact by combining BI skills with advanced analytics and machine learning in an easy to use visual interface.
• Visualization and reporting.
• Rapidly provides business -ready data to all users equally.
• Manage data spread across distributed stores and clouds.

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• Extremely fast query performance - Vertica is one of the fastest query engines out there.
• Scales to TBs - Scales reasonably well up to 10-20 nodes and 10 - 100s of TB of data.
• Easy to Use - Fairly easy to user, we made quite some headway with just 1 person running it for a while.

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• Cannot save changes to some secrets in the internal vault
• Sign-in issues on environments where IAM is enabled
• The Enforce quotas option is disabled

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• Could use some work on better integrating with cloud providers and open source technologies. For AWS you will find an AMI in the marketplace and recently a connector for loading data from S3 directly was created. With last release, integration with Kafka was added that can help.
• Managing large workloads (concurrent queries) is a bit challenging.
• Having a way to provide an estimate on the duration for currently executing queries / etc. can be helpful. Vertica provides some counters for the query execution engine that are helpful but some may find confusing.
• Unloading data over JDBC is very slow. We've had to come up with alternatives based on vsql, etc. Not a very clean, official on how to unload data.

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I haven't had any recent opportunity to reach out to Vertica support. From what I remember, I believe whenever I reached out to them the experience was smooth.

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Independently Invited by TrustRadius

A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info

IBM Cloud Pak for Data takes the IBM Cognos solution and provides this on an enterprise cloud platform that can be extended to support better data integration and data science capabilities.

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Independently Invited by TrustRadius

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Vertica performs well when the query has good stats and is tuned well. Options for GUI clients are ugly and outdated. IO optimized: it's a columnar store with no indexing structures to maintain like traditional databases. The indexing is achieved by storing the data sorted on disk, which itself is run transparently as a background process.

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Independently Invited by TrustRadius

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• We have the ability to access all our data much quicker through the unified search option.
• 30% increase in productivity through the introduction of AI.
• Improved data security and governance.

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• Positive impact on ROI by being able to get customer insights in real-time.
• Positive ROI through reduced time to set-up and maintain Vertica instances.

Incentivized

Independently Invited by TrustRadius

A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info