SingleStore aims to enable organizations to scale from one to one million customers, handling SQL, JSON, full text and vector workloads in one unified platform.
$0.69
per hour
Vertica Analytics Database
Score 10.0 out of 10
N/A
The Vertica Analytics Platform supplies enterprise data warehouses with big data analytics capabilities and modernization. Vertica was acquired and supported by OpenText, then sold to Rocket Software in 2026.
Effective solution around data even when it comes to real time info. Knowing you can access the data from any country is effective solution to extract the info needed.
I can only compare it with Exasol, which I have used a similar base, which manages the Hadoop scheme and is very similar to SingleStore. SingleStore has many advantages: being in the cloud, with just a couple of clicks I can increase the capacity, the configuration is super …
first of all SingleStoreis a cluster with high availability and easy to use. you need to design you tables / procedures such in a way that your SingleStore perform well and with handle heavy load
Reduces database sprawl, ETL costs, infrastructure expenses, etc. Supports horizontal scaling, unlike PostgreSQL & Aurora, and real-time analytics and fast transactions (HTAP), unlike Snowflake & ClickHouse.Handles high-volume workloads with thousands of concurrent queries. No …
SingleStore (memsql) out performs based on our analysis with sample data sets within org. We could see limitations with other products which SingleStore can overcall like scaling with data while performing with similar SLA. It also has the advantage of row store and column …
SingleStore outperforms Snowflake in real-time analytics and transactional workloads but lags in large-scale batch processing. Compared to MongoDB Atlas, SingleStore excels in complex SQL queries and joins, while MongoDB handles unstructured, document-based data better. Its …
Greenplum is good in handling very large amount of data. Concurrency in Greenplum was a major problem. Features available in SingleStore like Pipelines and in memory features are not available in Greenplum.
Gemfire was not scaling well like SingleStore. Support of both …
We previously used Bigquery for our application, and a single store gave us very good performance over Bigquery. But the comparison is not apples to apples, as Bigquery is more of a data warehousing solution.
SingleStore is just a bigger engine with more capability. Its ability to handle larger data sets with ease is its biggest advantage. These other database solutions are great for smaller scale projects that don't include large data sets but Singlestore greatly out performs in …
It has more APIs and other access methods. It has a multi-version concurrency control (MVCC) Distributed RDBMS that combines an in-memory row-oriented and a disc-based column-oriented storage with patented universal storage to handle transactional and analytical workloads in …
Its easier to query and faster. Ingestion is for the most part easier to understand and monitor and directly integrated with other storage solution products we use such as AWS S3. Singlestore overall is a better database to serve up an application large amounts of data very …
SingleStore is built for fast data ingestion and fast queries against large tables (> billions of rows). This is possible because of the column store engine that SingleStore uses. SingleStore also support a memory engine. Pipelines is also another big advantage. Being able to …
We knew early on that MySQL (Amazon Aurora) would not be suitable for this workload as it cannot query our time series data as fast as SingleStore. We also use MongoDB Atlas for another application but we could not achieve the raw speed we saw from SingleStore. Our technical …
We were initially using AWS Aurora which worked well at the time but as we grew it just felt like a lumbering beast - even with tier upgrades. We started looking at caching & search options to help make searches faster - we were already using redis. We looked at and even …
Timescale was the biggest alternative option we looked at for SingleStore, however the requirement to learn a new syntax (due to not being SQL compatible) was our biggest pain point.
Supporting a new language would require alterations to the Laravel framework, as this only …
SingleStore provides an abstraction layer in managing a sharded database solution reducing complexity for the FLOWD team. Coupled with the SingleStore Managed Service, we are partnering with SingleStore to provide FLOWD services to various utilities & councils.
SingleStore is eons faster than other database providers, and it absolutely crushes calculations & aggregations. While other providers may have a few quality of life enhancements over SingleStore, the speed benefits of SS far outweigh the cons. At the end of the day, speed …
Vertica performs well when the query has good stats and is tuned well. Options for GUI clients are ugly and outdated. IO optimized: it's a columnar store with no indexing structures to maintain like traditional databases. The indexing is achieved by storing the data sorted on …
SAP HANA, Oracle, MySQL, and PostgreSQL are too heavyweight for achieving real-time latency requirements. Google BigQuery is limited to Cloud that makes hard to integrate with a large ingestion pipeline that may have both Cloud-based and on-prem components. Hadoop is much more …
MySQL and MS SQL Server are both fantastic RDBMS products. MS SQL Server goes a bit further since it has the builtin analytical functions. But it only scales so far. Once the data goes beyond capacity, getting results out just does not happen anymore. IBM Netezza and …
Presto would be a good solution that would be less expensive and would also allow direct querying of all our data on Hadoop while maintaining good speed.
Vertica is great for small low complex queries and has great query performance over the other technologies that I have worked with. Vertica fails to Hive wrt scalability and resource isolation, where Hive exploits hadoop's resource isolation. Presto is almost comparable to …
Vertica is much easier to manage; is just software (i.e. vs. Netezza), easier to scale and extend, with a very powerful query execution engine and storage layer. While other solutions (e.g. Greenplum) are just postgres clones that were extended to run at scale but still keep …
Well-Suited Scenarios: Real-Time Analytics: Financial trading platforms requiring instant insights. Operational Dashboards: Retail businesses monitoring live sales. IoT Data Processing: Smart device monitoring with high data ingestion. Fraud Detection: Banks detect suspicious transactions instantly. Less Appropriate Scenarios: Archival Storage: Cold data storage with infrequent access. Low-Volume Workloads: Small-scale apps with minimal data processing needs. Complex ETL Pipelines: Heavy data transformations without real-time demands.
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.
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.
It does not release a patch to have back porting; it just releases a new version and stops support; it's difficult to keep up to that pace.
Support engineers lack expertise, but they seem to be improving organically.
Lacks enterprise CDC capability: Change data capture (CDC) is a process that tracks and records changes made to data in a database and then delivers those changes to other systems in real time.
For enterprise-level backup & restore capability, we had to implement our model via Velero snapshot backup.
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.
[Until it is] supported on AWS ECS containers, I will reserve a higher rating for SingleStore. Right now it works well on EC2 and serves our current purpose, [but] would look forward to seeing SingleStore respond to our urge of feature in a shorter time period with high quality and security.
Solutions are based around a business needs and even when implementing such solution, real time insights are also followed through showing the updates the business are implementing while informing the end users as what is new with technology.
When it comes to ingestion speed, SingleStore is probably at the top. Being able to create pipelines using SQL to ingest data from S3, Kafka, and other sources, is a great advantages. This means you can dynamically ingest data by customizing your SQL queries. SingleStore pipelines are pretty sophisticated, yet very simple. Few lines of codes and you are ingesting data, while still able to perform analytical queries on your billions of row tables.
The support deep dives into our most complexed queries and bizarre issues that sometimes only we get comparing to other clients. Our special workload (thousands of Kafka pipelines + high concurrency of queries). The response match to the priority of the request, P1 gets immediate return call. Missing features are treated, they become a client request and being added to the roadmap after internal consideration on all client needs and priority. Bugs are patched quite fast, depends on the impact and feasible temporary workarounds. There is no issue that we haven't got a proper answer, resolution or reasoning
HP/Micro Focus Vertica support is in par with other bigger vendors. In addition to this, there is enough best practices documentation available for some of the most common ways you will use Vertica that makes it easy to get Vertica up and running.
We allowed 2-3 months for a thorough evaluation. We saw pretty quickly that we were likely to pick SingleStore, so we ported some of our stored procedures to SingleStore in order to take a deeper look. Two SingleStore people worked closely with us to ensure that we did not have any blocking problems. It all went remarkably smoothly.
Reduces database sprawl, ETL costs, infrastructure expenses, etc. Supports horizontal scaling, unlike PostgreSQL & Aurora, and real-time analytics and fast transactions (HTAP), unlike Snowflake & ClickHouse.Handles high-volume workloads with thousands of concurrent queries. No need for ETL processes, unlike BigQuery & Snowflake. Works with JSON, relational, and key-value data, unlike ClickHouse.
MySQL and MS SQL Server are both fantastic RDBMS products. MS SQL Server goes a bit further since it has the builtin analytical functions. But it only scales so far. Once the data goes beyond capacity, getting results out just does not happen anymore. IBM Netezza and Teradata were both appliances that required different expertise than we had in house. Vertica was able to do the same, and in some cases better, on commodity hardware (frankly in our case old servers that were slated for recycling!) and at a small scale. In other words, Vertica we could grow slowly over time. Infobright is a great log processing database but for the functions we were looking to serve it just didn't have some of the features Vertica had that we felt were show stoppers.
Lower operational complexity - Installation and maintenance is pretty easy
Object scale when used can compete with Traditional Warehouse Systems like Teradata, Netezza, Greenplum
Adds lot of value to the business like couple of operations which never worked in traditional DBMS including HANA, Oracle In Memory, SQL Server In Memory just flew in SingleStore