I have varied experience in this category of products and I have used a few different ones. We have a legacy Oracle data warehouse, which is just not performing quick enough - hence the move to Vertica. Since moving to Vertica all the manual maintenance and speed issues of the Oracle data warehouse are left behind.

We did look at ParAccel, Vertica  and Kognitio when deciding we needed a fast database that could handle large volumes of data. Vertica worked on-premises, in the cloud, and I did extremely in-depth, rigorous testing on it, concurrency, size, volume, and it outperformed all the other databases. The cost was reasonable as well. Some of our larger reports are running on 3.200 billion row tables and they are running in seconds.

Snowflake is a platform that we use in our company. We have been using this solution for the last six months.

As a user of the platform, I don't engage in Snowflake development directly.

Instead, I use Snowflake as a platform where tables are already set up for me to work with. 

I do not handle any aspects of Snowflake management, such as table creation or sharing configurations, as these are typically set up with minimal clicks. 

My work revolves solely around Vertica, and I am responsible for handling everything related to Vertica.

We usually work with other vendors like Netezza and Oracle, some open-source databases, big data systems, and cloud-native tools like Azure, GCP, or BigQuery. We decided to go with Vertica because we had everything on-premises, and we preferred to have a database on-premise.

• I have used traditional, row-oriented relational databases like SQL Server, Oracle and PostgreSQL for data warehousing. They are optimized for handling transactions, not data warehousing. Vertica is optimized for data warehousing and that was very clearly demonstrated in its ability to scan large amounts of data at high speed. It is also very fast at loading data.
• Vertica uses a distributed, shared-nothing architecture which allows for nodes to be added (or removed) according to need. This is a very scalable architecture which is very difficult to achieve with traditional row-oriented databases.
• Compared to Hadoop, Hive, and Spark, Vertica is much more adept at handling concurrent users.

We previously used Oracle and we decided to go for big data. 

No, not at this company.

At my last company, we initially used Aster Data (now owned by Teradata). Once our database grew too large, it was unable to handle the number of transactions we were completing per day. Many queries on our largest table were taking from 20 minutes to over an hour to complete. Right out of the box, our longest queries went down to under a minute, most completing in a matter of seconds.

I have previously used Teradata long time ago. The price of Teradata was very high and hardware is vendor specific. Vertica can operate on comodity hardware and therefore easily scalable.

We previously used Exadata, which is typically very expensive by comparison. This is because Oracle throw top end hardware at the problem as opposed to
HP Vertica’s commodity hardware and smart software approach.

I used Hadoop as the first approach. However, Vertica provided the best of both worlds (huge amounts of data and speed of access for analytics).

I have not used another similar solution to Vertica.

We used Oracle for our DWH. When selecting a new database, we evaluated -- based both on written documentation and hands-on experimenting -- quite a lot of databases, such as Exadata, Teradata, and IBM Netezza. We selected HP Vertica as it runs on bulk hardware since it has “open interfaces”. It performed really well during hands-on experimenting and its “theories in practice” is good. Performance is excellent, development is easy (however, you need to re-think some things that you may have gotten used to when using other SQL databases), and its license model is simple.

We've used Greenplum, Teradata, and then Vertica. We used the big data open source solutions as well that are getting better. So those are the four that I can think of off the top of my head. Greenplum and Teradata are just getting too expensive. 

Particularly compared against its open source set, I think that's really the one key piece where Vertica might have a little bit more ladder room. It was always the leader in terms of pricing against Greenplum and Teradata, so that's why Vertica turned up again for us, but now that the open source solutions are trying to compete a little bit better in terms of stability, that's where we sometimes consider change.

I have previously tried SQL PDW, Mongo, Cassandra for alternatives. Even though all of those products are in different landscapes, the Vertica column store ended up being the best thing that was needed.

We switched off of Infobright because it was not performant at all at the scale we needed. The number of limitations on Infobright are too many to list in a small review like this.

We had SQL Server, switched for money reasons and space. But we're not sure yet, SQL Server is way more stable and predictable.

We currently use IDOL as well as Vertica.

We do have some pipelines accessing raw data directly and process it as a batch Spark job. Why? I guess it's because the type of operations we do can be done easily in code vs. SQL.

Postgresql, MySQL, SQL Server. Switched because of scalability and reliability, analytics functionality. V being a better engineered product.

We use MongoDB for some of our other internal production apps. It's a lot more involved and more complex than we like to go for a, just standard data warehouse, but we might look at Hadoop or similar for that.

I haven't used anything similar.

I previously used Postgres; switched as performance suffered due to data growth.

Choosing Vertica was the first time we used a data warehouse solution for handling the large amounts of data we were starting to gather. Since then, we have switched from an internally hosted Vertica to Spark managed externally.

In my current company I was not responsible for the switch. As far as I know, they switched from PostgreSQL, especially because of performance of analytical queries processing large data.

We did not previously use a different solution.

We used to use MS SQL Server. It is good for data transactions, but it is not good for big data analytics.

No

Not really in the same area (MPP databases). However, we ran benchmarks back then against a bunch of competitors and Vertica was one of the fastest, while
being relatively cheap and able to accommodate our hardware.

Previously, we were basically using an old school setup based on a relational database. I’m not sure which database management system it was.

The performance of the previous solution was no longer adequate to support the growth we were seeing in our business. Response times were up to 10-15 seconds on different queries. We needed to get that down to under a second.

Now we’ve moved to a real big data analytics solution.

I have used Greenplum and Postgres extensively. The latter is an excellent general-purpose database and is entirely suitable for most data needs, however Vertica works really well in cases where you are storing and querying a lot of data that can be compressed and stored in columnar format, and you need your data auto-balanced across many nodes.

It complements our SQL Server solution.

Greenplum. It was less stable.

Vertica is very robust and recovers predictably from unexpected infrastructure failures.

SQL Server did not scale.

MonetDB -- unstable, frequent crashes.

Yes, regular relational databases. We switched for scalability reasons.

We used Oracle but it did not scale well enough

NA

I've been a 'full stack' data expert for years, started on Oracle and SQL Server, moved to Hadoop, Mongo, etc, but Vertica was the right fit for large enterprises with high performance demands and ease of scalability.

The last company I worked for shifted from Oracle to Vertica. For our BI queries it's a huge win. Vertica is much better than any other raw store as it is built exactly for complex analytic queries with a huge amount of data.

See "Improvements to organization," above.

We use DB2, Oracle , MySQL, MSSQL. We switched to Vertica to explore it for  future projects.

SQL Server and Oracle.