4.6 out of 5 stars

Vertica offers a software-based analytics platform designed to help organizations of all sizes monetize data in real time and at massive scale.

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Vertica review by <span>Marcello P.</span>
Marcello P.
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What do you like best?

Before I met HP Vertica, I've worked with a few other column-store databases.

Few were easy to install and use, but were not reliable; others had good performance and stability but were definitely not use to deploy.

After 2-3 hours of deciding to try Vertica, I was running queries on it.

So what I like the best:

- Easy to deploy;

- Easy to Configure;

- Plenty of native tools to help the maintenance;

- Plenty of *clear* documentation to help you though;

- Fast - hands-down, the fastest for hardware I've worked with;

- Small - incredible compression rates to the data;

- Reliable/Stable fault-tolerant and - in my case - no data loss issues;

- No need for planning, analyzing or maintaining table indexes;

- Familiar way of loading data [PostgreSQL-like / COPY]

What do you dislike?

Even though most of the problems I've had were already answered in public forums from the Vertica Communiy - I use the community version -, I feel like there's not a big crowd trying to help or at least trying to make their voice heard online.

Recommendations to others considering the product

Read all the documents you can about Hardware Requirements. Even though it looks big, it's a very complete document, every DBA should beware of.

Because Vertica looks from outside a lot like PostgreSQL, users tend to forget some of its specificities when writing SQL queries or doing some other maintenance tasks. So beware of subqueries, data types, row-inserts - prefer bulk inserts - table/column drops, etc.

What business problems are you solving with the product? What benefits have you realized?

Using the Vertica Community Version, we were able to increase - sometimes by a factor of 10 - the speed of Data Warehouses queries.

As we didn't have much time to try tens of different databases, Vertica showed handy when it comes to Install/Loading data.

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Vertica review by <span>Cameron W.</span>
Cameron W.
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"Vertica in a League of its Own"

What do you like best?

Vertica query optimizer relies on up-to-date statistics for tables, schema, and the database. The statistics allow the optimizer to determine the most efficient plan to execute a query

What do you dislike?

I am not able to change Profile data, Its saved in the pre made tables provided by creator.

Recommendations to others considering the product

Quires can be made in any fashion as you need

SQL Functions, Each function is annotated with behavior type as immutable, stable or volatile.

What business problems are you solving with the product? What benefits have you realized?

We are able to store more data, And customize that data into what we need.

What Relational Databases solution do you use?

Thanks for letting us know!
Vertica review by <span>Traian A.</span>
Traian A.
Validated Reviewer
Verified Current User
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"Fast and powerful analytics platform"

What do you like best?

A lightweight performance-focused implementation and various features:

− 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;

− Querying will only read specific columns' data, pushing predicates to the storage layer is very important, analytical queries on row store databases will never be able to match that. Columns with RLE are similar to having an infinite number of partitions and also sub-partitioning levels, in some cases if multiple predicates are used with proper sorts it can be incredibly fast.

ANSI SQL compliant, SQL-92 and most of the SQL-99 standard; easy to extend with user-defined functions written in C++, Java, R and to turn it into a powerful data processing engine that is able to easily parallelize, distribute, and partition datasets for processing (moving processing between Hadoop Pig/Hive and Vertica is very simple).

Developer friendly: from verbose explain plans and query profiles to the ability to track execution engine metrics by query paths/operators (e.g. CPU cycles used, rows processed, bytes sent over network, etc.)

Easy to setup and manage fairly large clusters. In our experience a dba should be able to handle many large clusters.

Very stable, easy to scale, reliable, highly available (most of issues we had were hardware issues; never had down time or lost any data).

Constant addition of features, improvements (e.g. support for large data types, GIS package, flex tables, etc.).

What do you dislike?

Price may be high; small startups trying to keep costs down may choose open source (e.g. HBase, etc.)

There were some stability issues at first when certain errors were bringing down nodes, etc. but have been solved for a while

Supporting large workloads (many concurrent queries) is still not a strength of Vertica.

Loading very large data sets may use some improvements (e.g. in some cases you may have more capacity to parse and segment the data on the client side and stream the data to a specific node thereby directly reducing load and data redistribution between nodes.

Depending on the data model used, in some cases you might have trouble optimizing the queries (large joins with large group-by's on columns across multiple relations);

Recommendations to others considering the product

Dr. Michael Stonebraker was the co-founder and architect (Vertica is based on the C-Store project). If you haven't heard of him it suffices to know that he received the 2015 Turing Award for his contributions to database systems.

You will need to understand its physical layer and how your queries will access and process the data to come up with the right design (Database Designer can be a great help to get you started) and then you will be amazed how fast you can do data filtering, joins and group bys, etc. on billions of records with a handful of nodes in minutes. At the same time if your queries are suffering from bad segmentation, can't do block processing, push predicates to storage layer, etc. then you will not really get anything from what Vertica has to offer.

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.

With all the NoSQL, NewSQL buzz I’ve seen there is a misconception that SQL is old, RDBMS don't scale, etc. but the reality is many of these NoSQL products are adding more and more SQL-like features to stay competitive so be sure SQL is here to stay.

What business problems are you solving with the product? What benefits have you realized?

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.

The main reasons I usually choose Vertica are it's the performance that’s fairly easy to scale and extend.

Vertica review by <span>David L. P.</span>
David L. P.
Validated Reviewer
Verified Current User
Review Source

"Vertica as a Lightening Fast BI Data Source"

What do you like best?

Vertica is truly the fastest column-store database implementation on the market today.

What do you dislike?

There are still some glitches in the bulk import COPY process that requires inlining the source query details and the management tool ecosystem is still in the process of maturing.

Recommendations to others considering the product

Clearly understand your requirements prior to implementing a column-store database. Queries with complex WHERE clauses and large scale aggregation can complete with lightening speed, new data can be INSERTed with very fast caching, but rapid UPDATEs and DELETEs will need special design considerations (such as chunking, reordering, and/or tombstoning) in a high velocity solution.

What business problems are you solving with the product? What benefits have you realized?

Vertica's column-store solution essentially makes every column its own index -- while still facilitating row-level selection of data. This makes previously unfeasible requirements -- such as "filter on every column" -- possible even with interactive user interfaces.

Vertica review by Consultant in Hospital & Health Care
Consultant in Hospital & Health Care
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Verified Current User
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"Fantastic Analytics Platform"

What do you like best?

Very fast and relatively easy to implement. Very nice SQL implementation (albeit with some minor limitations) with great bulk load facilities. I successfully migrated multiple multi-terabyte customer databases from Netezza to Vertica with huge increases in performance, lower TCO, and reduced administrative overhead.

What do you dislike?

Refreshing lower environments with data from upper environments is easy if the clusters have the same number of nodes, otherwise you have to get more creative. There are out-of-the-box methods to facilitate data refreshes between differing cluster sizes, but it's more of a roll-your-own approach.

Backup and recovery faces the same challenge in that recovering to like cluster sizes is possible, but not so with a target cluster of a different size.

Vertica's SQL implementation is really good, however, there are a number of odd and/or limited implementations for certain things (e.g. NOT IN with NULL returned by subqueries, CTE support but not recursive CTE support, etc). This may be addressed in the latest versions, but these items were present in 7.0.x.

The operating system-level configuration is fairly straightforward, however Vertica is very sensitive to even the slightest misconfiguration. Highly recommend that implementers follow the vendor documentation to the letter when configuring host servers.

Also recommend a high performance, direct-attached storage device for performant backups.

Recommendations to others considering the product

Purchase dedicated, direct-attached storage devices for backups (1 per environment), follow vendor configuration instructions to the letter, send your DBA to training, and study up on the SQL limitations.

What business problems are you solving with the product? What benefits have you realized?

Legacy DW appliance replacement with lower TCO and improved performance with lots of room to scale.

Vertica review by <span>Joe G.</span>
Joe G.
Validated Reviewer
Review Source

"Vertica is an amazing data warehouse!"

What do you like best?

Columnar storage makes for fast data access. Lots of storage on fewer nodes. Use of projections is an intuitive way to manage tables and help optimize queries. Great customer service, very responsive! And comprehensive training allows you to be able to manage and administer the system with ease, using tech support as a last resort.

What do you dislike?

I am finding it difficult to find anything to dislike about this system.

Recommendations to others considering the product

Go through with a POC, it will open your eyes to the benefits Vertica will have on your system.

What business problems are you solving with the product? What benefits have you realized?

We use our data warehouse to run queries, reports and analytics on billions of rows of clickstream data. Our old data warehouse solution was extremely slow, and required 1 nodes to house 30TB of data. With Vertica we only require six nodes that require far less involvement from tech support to maintain. Queries ran faster right out of the box prior to any additional optimization. We are now moving toward being able to provide close to real time analytics for our clients!

Kate from G2 Crowd

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