Good analytical database
Vertica serves a database niche that is highly ingested with fast query analytics (MPP). It competes with platforms such as Teradata, …
Vertica
Vertica
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What is Vertica?
The Vertica Analytics Platform supplies enterprise data warehouses with big data analytics capabilities and modernization. Vertica is owned and supported by Micro Focus.
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Alternatives Pricing
What is Amazon Redshift?
Amazon Redshift is a hosted data warehouse solution, from Amazon Web Services.
What is Google BigQuery?
Google's BigQuery is part of the Google Cloud Platform, a database-as-a-service (DBaaS) supporting the querying and rapid analysis of enterprise data.
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Product Details
What is Vertica?
The Vertica Analytics Platform supplies enterprise data warehouses with big data analytics capabilities and modernization. Vertica is owned and supported by Micro Focus.
Vertica Technical Details
| Operating Systems | Unspecified |
|---|---|
| Mobile Application | No |
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Frequently Asked Questions
What is Vertica?
The Vertica Analytics Platform supplies enterprise data warehouses with big data analytics capabilities and modernization. Vertica is owned and supported by Micro Focus.
What is Vertica's best feature?
Reviewers rate Support Rating highest, with a score of 7.9.
Who uses Vertica?
The most common users of Vertica are from Enterprises (1,001+ employees) and the Computer Software industry.
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May 11, 2021
Good analytical database
Vertica serves a database niche that is highly ingested with fast query analytics (MPP). It competes with platforms such as Teradata, Greenplum, Exadata, and Netezza. It does not compete with pseudo column stores such as a SQL Server column store, as those types of "features" are immature and still built on an OLTP platform. Vertica is quick with a large amount of data ingestion.
- Column-oriented storage organization, which increases performance of queries.
- Compression, which reduces storage costs and I/O bandwidth. High compression is possible because columns of homogeneous datatypes are stored together and because updates to the main store are batched.
- Shared nothing architecture, which reduces system contention for shared resources and allows gradual degradation of performance in the face of hardware failure.
- Easy to use and maintain through automated data replication, server recovery, query optimization, and storage optimization.
- Support for standard programming interfaces ODBC, JDBC, ADO.NET, and OLEDB.
- Integration to Hadoop with the capability to perform analytics on ORC and Parquet files directly.
- Per TB licensing. Users have to worry about license usage at all times which becomes a challenge with you are working in an organization with huge amounts of data.
- The geospatial functionality could be designed better.
- Support for containerization and flexibility from the deployment standpoint.
May 07, 2021
Robust Vertica Experience
It's used by couple of departments. I work in the entertainment industry, so it is used to deal with the rendering data. It is also used for big data analysis.
- After the initial setup and performance tuning phase, Vertica database cluster pretty much runs on its own. We haven't had too much maintenance to do.
- When we had to scale up the cluster from 6 nodes to 12 nodes, it was an easy task.
- At one time, because of some issues with a server, we had to take a node out and could do it on the fly.
- One time, one of the nodes wasn't coming up because of some ambiguity with the local data. Vertica wasn't able to fix it by itself and we were trying to remove the node out of the database and we couldn't do it. It would be great if that could be addressed. Luckily when we rebooted the whole server, some of the dead transaction got flushed because of which vertica was able to recover and the node came up.
December 15, 2019
Vertica Review
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.
December 04, 2017
Analysis at Scale
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.
December 29, 2016
Fast with some limitations
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.
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.