Über Vertica
Spaltenorientierte RDBMS-Architektur, die eine sehr schnelle Ad-hoc-Abfrageleistung für Data Marts und Data Warehouses bietet
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Nutzerbewertungen filtern (2)
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Nutzerbewertungen filtern (2)
Ruban
In Betracht gezogene Alternativen:
Best Analytical Database I have ever used
Kommentare: I'm using vertical as a analytical dB for twitter sentiment analysis. I have used Flume to get twitter data and use R to move the data from Hadoop to vertica DB and do analytical function and project the data in tableau.
Vorteile:
Analytical features that is available with this DB. Ease of use with Text User Interface and UI. Deployment is very fast and easy to do. Integration support with almost all top ingress and egress platform like. Apache Kafka, Flume, Hadoop etc. Very efficient and easy to deploy multinode cluster.
Nachteile:
Nothing I can think of, I was having node health issue alert even when the node was online. I need to upgrade to latest version and hope that issue has been addressed.
Verifizierter Rezensent
light weight platform that works very well
Kommentare: We were looking for an analytics platform that can work concurrently with hadoop clusters that was focused on performance and could also be inter-changeably used as a data processing engine.
Vorteile:
It is light weight and nimble but at the same time is good on performance - the fact that it is column store without the real need for indexing makes it easier to get started and then the storage requirement is also much smaller thanks to coding schemas and algorithms.
Nachteile:
for me, the biggest issue was the fact that it really affects performance when we try to run many large concurrent queries - I wish there was a solution to this. In addition, initial cost of investment to get started on Vertica may discourage SMB's.
