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Filter 22 vetted Vertica reviews and ratings
Reviews (1-5 of 5)
Vertica forms the analytics database that takes in realtime streaming data (from Apache Kafka) and is used to provide customer insights in near real-time. It is used for the consumer-facing web portal and mobile applications.
- It is able to intake real-time streaming data without much pre-processing and latency.
- Easy to integrate with real-time streaming ingestion engine.
- Vertica does not perform well when you have a lot of schemata.
- The management console including GUI is lacking features and can be improved with features that are typical of a database.
Vertica is our data warehouse. We use it for analysis of our internal business as well as the marketing results of our clients. Due to the size of our data, without Vertica analysis at this level would not be possible.
- Analytical querying due to built in analytical functions that actually perform across TB of data.
- Ingestion of data. We can send billions of rows to Vertica easily via the WOS system and it is ready for use immediately.
- Efficient storage of data. What raw is TB of data, once ingested into Vertica only takes up GB of disk space.
- Management! The management console is intuitive and useful making keeping an eye on your cluster easier than any other product like this I have used.
- Deletion is tough in Vertica. Because one of our larger fact tables is rapidly changing we have a need to run purges on a regular basis. Those purges can take a day and delays the other processes while that is happening. It would be nice if when I hit delete, it really deleted.
- Permissions on table manipulation is a bit lacking. In order to edit a table structure you have to be the owner, ie the creator, of the table. It means setting up true administrators who can maintain each other's work is tough.
November 04, 2016
Vertica's Strengths and Weakness
Vertica is used by uber for data analytics use cases. We have a vertica based data mart (subset of business data) for analytics insight and data science across the entire organization. We use it as a complementary solution to Hadoop. We initially started our with Vertica which worked for our needs, but over the last couple of years have started leveraging Hadoop in addition to vertica to help our data efforts with high scale.
- 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.
- PetaByte Scale data - Vertica Just cannot deal with this, it starts to crumble beyond 100s of TB of data.
- Concurrent Usage - Vertica starts to have significant backpressure as your concurrent users grow quickly. We had trouble scaling post 20-30 users and had to invent our our queuing strategies.
- Vertical stack - storage + compute tier in one stack, this doesn't help the cause of scaling. Other systems leverage the advantage of storage and compute being different tiers (eg: HDFS + Presto)
December 08, 2015
Fast and powerful analytics platform
Vertica is our main data warehouse. Is used as a source for most of our analytic reports as well as for all data analysis activities. We also use it in a non-traditional fashion, more like a data processing engine for solving problems at scale (matching, statistics, correlate sources, etc.). It runs in AWS with data loaded/unloaded from/to S3.
- 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.
We use Vertica as an analytics database for reporting, ad-hoc queries, regular reporting and more in-depth analyses. It is primarily used for querying by the analytics org and indirectly, through Looker, by the entire company. Vertica is most helpful because of its speed. Ad-hoc queries and analyses can be completed relatively quickly.
- Speed. Even with tables with 20 Billion+ rows, Vertica performs reasonably well.
- Analytical functions. Some of the advanced functions in Vertica enable/facilitate interesting and complex analyses.
- Reliability. We never run into reliability issues with Vertica.
- Pricing: Vertica can get pretty expensive with large data sizes.
- Speed: Queries could always be faster!
- Limited options for querying clients: We primarily use Vertica from our terminals. Options for GUI clients are ugly and outdated. Using the terminal for querying is sometimes annoying, with problems like showing query runtime only in milliseconds and not being able to change it, columns being hard to read when there are more columns than the display space etc.
Vertica Scorecard Summary
About Vertica
The Vertica Analytics Platform supplies enterprise data warehouses with big data analytics capabilities and modernization. Vertica is owned and supported by Micro Focus.
Categories: Data Warehouse
Vertica Technical Details
| Operating Systems: | Unspecified |
|---|---|
| Mobile Application: | No |