ParAccel vs. Spoom vs. Vertica Analytics Database

Actian matrix is not good for small data sets. If you have a limited data pool, or do not plan on having multiple users/clients accessing a data source, stick with a more traditional relational database model - Access for the truly small user base, or a DB2 or Oracle back end if your going to have multiple users, and moderate sized data. Actian is for LARGE data sets (Big Data, in the industry parlance). Millions of rows of data from multiple sources with various down stream systems accessing the database. It is for data analytics of large data groups and intense data mining.

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It is well suited when you have several sources of data.

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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• Super fast. Aggregate query such as SUM(), Count() returns result within seconds from a table with more than billion records.
• Excellent data compression.
• Easy maintenance. We managed this database without having a full time DBA.
• Support ANSI SQL and ODBC/JDBC. It's easy to connect to this database from other systems.

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• Easy to use
• Focus on analyzing and not on finding and loading data
• "One version of the truth"

• 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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• Some of the bugs were annoying and QA definitely needs improvement
• Connectivity to Informatica and ETL providers
• Workload management could be better like when you compare with Teradata

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• I would like to have more modules. Eg. : supply chain tool.

• 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 wish to give higher rating for the speed and efficiency in handling the queries, but only 6 because of consistent bugs we encounter

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• Faster initial response
• Trained professionals
• Very helpful in resolving issues

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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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Leader failover setup is the toughest and lack of proper documentation is making things tough.

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Actian Matrix is our first big data analytics storage platform, and as I was not involved in the POC process to compare it to other products out on the market, unfortunately I cannot say if it is better than other Big Data storage options. I can say that it out performs products such as Oracle or UDB in regards to the volume of data it can easily index and handle.

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Cognos is very expensive and needs heavy IT-team to implement it.

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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• ROI is great, less spending on full time DBA and that money could be use to add additional node.
• Negative - Not many developers are well aware of this tool, it takes some time to learn.

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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.

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