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The Vertica Analytics Platform supplies enterprise data warehouses with big data analytics capabilities and modernization. Vertica is owned and supported by Micro Focus.Analysis at ScaleVertica 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.,10,For our internal business we have insights we could never have had without Vertica. We can actually see where our money is coming from and point our marketing and sales strategies in the correct direction thereby returning far more than we pay. For our customers we can offer services they had been begging for. Before implementing Vertica we had no insight on a client's marketing across all their activities because the data was just too large. Now, there is no question we can't answer.,Teradata Database, IBM Netezza Data Warehouse Appliances, Infobright, MySQL and Microsoft SQL Server,Pentaho, TIBCO Jaspersoft, MySQL, Microsoft SQL ServerVertica's Strengths and WeaknessVertica 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),7,We've been using vertica to derive a lot of valuable ad-hoc human insights Used to run periodic batch jobs that generate production results in the past, now moved to Hadoop for such use-cases We had a couple of big outages due to vertica unable to keep up with the load of queries and data (however were mitigated w/ leveraging hadoop).,Hadoop and Presto,Presto, Apache Hive, MemSQLFast and powerful analytics platformVertica 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.,9,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.,EMC Greenplum HD, Amazon Redshift and IBM Netezza Data Warehouse Appliances,Amazon Elastic MapReduce, Elasticsearch, Apache SparkFast with some limitationsWe 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.,Data size limitations. Beyond a certain threshold Vertica breaks down. Because of this, we are not able to put all our data in Vertica and have to resort to Scala/Hive on Hadoop. 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.,7,The ROI on fast, interactive querying is very high. Table size limitations force us to use other tools, slowing things down. Relatively expensive pricing forces us to regularly upgrade our Vertica subscription and database. Each upgrade leads to a downtime of a few days.,Presto,Hadoop, Apache Hive, Looker, GitHub
  3. Vertica Reviews
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Vertica
19 Ratings
Score 8.0 out of 101
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Vertica Reviews

Vertica
19 Ratings
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Score 8.0 out of 101
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Reviews (1-4 of 4)

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Eva Donaldson profile photo
December 04, 2017

Vertica Review: "Analysis at Scale"

Eva Donaldson
Score 10 out of 10
Vetted Review
Verified User
Review Source
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.
Vertica as a data warehouse to deliver analytics in-house and even to your client base on scale is not rivaled anywhere in the market. Frankly, in my experience it is not even close to equaled. Because it is such a powerful data warehouse, some people attempt to use it as a transactional database. It certainly is not one of those. Individual row inserts are slow and do not perform well. Deletes are a whole other story. RDBMS it is definitely not. OLAP it rocks.
Read Eva Donaldson's full review
Praveen Murugesan profile photo
November 04, 2016

User Review: "Vertica's Strengths and Weakness"

Praveen Murugesan
Score 7 out of 10
Vetted Review
Verified User
Review Source
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)
As someone just starting out with data analytics and warehousing vertica is a great tool for a small scale business. It has amazing performance and can scale upto TBs of data. It works well for any organization which has about 100 - 500 DAUs of the system. The system doesn't require a lot of ops overhead.

Scaling for PB data and 1000s of DAU is vertica's weak point. The system is just not designed for large scale usage and still has a long way to go to improve scalability. There are experiments to run Vertica query engine on top of HDFS which seem promising, however - if you have the the Hadoop ecosystem you are better off going the HDFS + Presto/Impala/SparkSQL route. But if you are in the Hadoop ecosystem, you probably are already investing a lot in ops.
Read Praveen Murugesan's full review
Traian Antonescu profile photo
December 08, 2015

Vertica Review: "Fast and powerful analytics platform"

Traian Antonescu
Score 9 out of 10
Vetted Review
Verified User
Review Source
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.

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.

At the same time, using Vertica as a traditional OLTP database, with many small transactions inserting/deleting/updating data is not going to take you very far so that’s an obvious case where Vertica is not recommended.
Read Traian Antonescu's full review
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December 29, 2016

Vertica Review: "Fast with some limitations"

Verified User
Score 7 out of 10
Vetted Review
Verified User
Review Source
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.
  • Data size limitations. Beyond a certain threshold Vertica breaks down. Because of this, we are not able to put all our data in Vertica and have to resort to Scala/Hive on Hadoop.
  • 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.
It is appropriate for interactive querying. It is not appropriate for complete storage of all your data for bigger companies.
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Vertica Scorecard Summary

Likelihood to Recommend (19)
8.0

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

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