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Formerly Actian Matrix


What is ParAccel?

ParAccel was a data warehouse appliance (DWA) option, offered by Actian since the April 2013 acquisition of ParAccel as Actian Matrix, that has since been discontinued.

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What is ParAccel?

ParAccel was a data warehouse appliance (DWA) option, offered by Actian since the April 2013 acquisition of ParAccel as Actian Matrix, that has since been discontinued.

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What is Amazon Redshift?

Amazon Redshift is a hosted data warehouse solution, from Amazon Web Services.

What is Db2?

DB2 is a family of relational database software solutions offered by IBM. It includes standard Db2 and Db2 Warehouse editions, either deployable on-cloud, or on-premise.

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Product Details

What is ParAccel?

ParAccel was a data warehouse appliance (DWA) option, offered by Actian since the April 2013 acquisition of ParAccel as Actian Matrix, that has since been discontinued.

ParAccel Technical Details

Operating SystemsUnspecified
Mobile ApplicationNo
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Reviews and Ratings


Attribute Ratings


(1-3 of 3)
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Dipankar Pradhan | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Actian Matrix is being used as analytical tool to answer analytical questions business might have. Business inteligence group uses this database. It is not used across organization. Business has to run aggregate query on top of billion of rows - they use Matrix. BI team has to create internal reports (KPI), and Matrix is being used to do all the processing, [as well as] joins between multiple tables to generate aggregated report. Also, this is the source of a data virtualization layer.
  • 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.
  • Dashboard for Admin instead of a linux console.
  • Admin with ability to kill inactive sessions when sessions reaches to more than allowed sessions. We ended up restarting the database.
  • Ability to easily scale up database by adding extra node.
  • Better support from Actian.
[Actian Matrix is well suited for:]
1. Power user who wants to derive aggregate metrics by joining huge dataset.
2. Batch load where you have to load billion of records very fast (copy command).
3. Scenario where you want to do ELT because your ETL tool cannot handle huge volume of data.
  • 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.
Redshift, Greenplum
Score 7 out of 10
Vetted Review
Verified User
Actian Matrix is leveraged by multiple teams and organizations to solve the issues related to data mining and big data crunching. It is a tool used to aggregate data from multiple internal and external sources and perform detailed analysis in a shared repository. It is often used in conjunction with ETL programs to transform and load the data, and then sourced from this repository by multiple analytics platforms, both in house and off the shelf software packages.
  • Handles Various data types from multiple sources.
  • Indexing speed - using the Actian platform as a back end database greatly improved processing time.
  • Its a new product, there are plenty of bugs to work out with regards to converting special reserve characters that might crop up in data.
  • The Matrix 2 Matrix database port tool needs some ironing out.
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.
  • I cannot comment on the specific numbers, all I can say is that switching to the backend greatly improved our processing speed. The more we process through our systems, the more clients we can serve. Which should bump ROI.
  • oracle
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.
For the specific applications that now run on Actian Matrix, we replaced back end source databases that ran on Oracle and DB2 Databases. They were replaced because Actian Matrix is a big data analytics database, a columnar database, as opposed to the more traditional relational database. This kind of platform can better process and index large quantities of data, which is what we were looking for. Process - and processing - improvement.
  • Price
  • Product Usability
  • Analyst Reports
Unfortunately, I was not involved in the selection process. I came in on the pilot programs that leveraged the tool ,and its success lead to our company adopting it across a few other application platforms. My team helped both my company, and the vendor (Actian) iron out various conversion issues the tool had - but I was not involved in the decision to go with the platform.
I would include more of the middleware support groups in the process of selecting tools.
Score 6 out of 10
Vetted Review
Verified User
I used it in MicroStrategy Cloud. It is used as one of the big data solutions provider for all the performance tier customers. Apart from MicroStrategy Cloud, it was also used with MicroStrategy wisdom and MicroStrategy Social softwares. It definitely solved the slower queries that were pestering in SQL Server and Oracle. It is the best in loading the data into the database.
  • Data loading is excellent
  • ParAccel support is the best I have seen so far.
  • 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
It is one of the best fit for data analytics, but if the database is smaller than 500GB then it is less appropriate.
  • Faster reports
  • Faster ETL
  • Bugs were annoying and [it was] tough to get the resolution sooner
My comparison with Vertica: ParAccel stays at top because we do not know the projections ahead of time (which is a major performance tuning technique in Vertica)

My comparison with Netezza/Teradata: Definitely, ParAccel is economical, but if you can afford it, go for other providers as they are stable.
PostgreSQL, Amazon Elastic Compute Cloud (EC2), MongoDB, Docker, Nagios
  • Speed
  • Columnar structure
  • Compression
  • Columnar storage
  • Compression to 10:1 ratio
  • Backup and restore
  • Workload management
  • Price
  • Product Features
  • Product Usability
  • Prior Experience with the Product
I would have reviewed workload management more, especially managing the number of concurrent heavy and lighter queries.
  • Implemented in-house
  • Professional services company
  • Getting OS configuration right
  • Less documentation made the setup tough
Leader failover setup is the toughest and lack of proper documentation is making things tough.
  • Faster initial response
  • Trained professionals
  • Very helpful in resolving issues
Yes, a hot fix was deployed very fast just to address my bug. Very impressive!!!
For the leader failover setup, a particular version of Corosync was giving an issue. Support directly jumped in and solved it for me.
  • Table creation
  • Understanding columnar nature of storage
  • Backup
  • Incremental restore
  • Data Compression
  • Workload management
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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