Fast and powerful analytics platform
Overall Satisfaction with Vertica
- IO optimized - it's a columnar store, 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.
- Reduced data storage footprint through advanced encoding schemas (RLE, common-delta, etc.) as well as compression algorithms ability to operate directly on the encoded data.
- 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.
- Vertica increased our productivity in analyzing the data and validating simple proof of concepts with our data.
- Results of analytical queries produced from Vertica are used by all departments as well as part of some of our products.
Vertica is not the silver bullet but based on my experience in 9/10 cases in which you need an analytical database, Vertica is probably the answer.
Currently we're using Vertica more as a data processing engine in conjunction with a Hadoop cluster as some of the steps are way more efficient than doing them in Hadoop and easier to manage (e.g. iterative processing steps). We also had a pretty good experience using it with Storm and Hadoop.