TrustRadius Insights for SingleStore are summaries of user sentiment data from TrustRadius reviews and, when necessary, third party data sources.
Pros
Real-Time Data Processing Capabilities: Users have consistently praised SingleStore for its efficient real-time data processing capabilities, noting its effectiveness in online transaction processing and big-data batch handling. The seamless integration with external services like Kafka and S3 has also been highlighted as a significant advantage.
Super Fast Data Queries: Reviewers have emphasized the exceptional speed of data queries on SingleStore, enabling them to quickly and efficiently retrieve information for their needs. This feature is seen as a key benefit that enhances overall productivity and decision-making processes.
Scalability and Performance Improvements: Users appreciate SingleStore's scalability for both writes and reads, along with notable performance enhancements. These include faster request processing rates, improved algorithm processing times, and the ability to handle growing workloads without compromising efficiency or reliability.
Ensuring that the system processes undertaken within a business environment ensures that it coincides with the area in which the business works in, allowing the need for flexibility while at the same time increase productivity both internal and external.
Pros
Informing their clients about products
Webinars and articles that address new technology
Allowing the need for clients to choose what platforms works for them
Cons
A bit more clarity around how to address software package costings
How to ensure A.I is a real effective solution when choosing the platform
Likelihood to Recommend
A environment where there is a need to automate processes such as admin work while at the same time balancing how the platform would become a effective solution to address real world scenarios.
VU
Verified User
Analyst in Information Technology (1-10 employees)
We use for real time analytical and data store and serving platform for our ML workloads. It enables us to run low-latency analytics and model driven use cases at scale which is quite difficult for OLAP and OLTP databases alone.
Pros
Serving ML features with low latency
Real time analytics on continuous incoming data
Fast ingestion without heavy ETL infrastructure
Cons
Diagnosing slow queries, skewed partitions can be difficult
Evolving schemas for real time data requires careful coordination
It lacks advanced features like fuzzy logic matches to dedup string formatted data. It should be quite intelligent enough to do that.
Likelihood to Recommend
Low latency APIs can use SingleStore quite easily and faster.
ML Feature store and Online feature serving use cases can easily be using it.
Real time analytics can use it.
Complex and heavier ETL pipelines can be less relevant for SingleStore
I use SingleStore for the data warehouse of a fintech, which processes payments through TCs worldwide. The integration was with Azure DataFactory, without complications, it helps us in an excellent way since we are very fast in obtaining the data for our dashboards and additionally the compression of the information.
Utilizo SingleStore para el datawarehouse de una fintech, la cual procesa pagos mediante TCs a nivel mundial, la integracion fue con Azure DataFactory, sin complicaciones, nos ayuda de manera excelente dado que tenemos mucha rapidez en obtener los datos para nuestros dashboards y adicional la compresion de la informacion
Pros
Fast Data Recovery
Data compression by 80%
Having the information in sheets, which helps to process the information quickly
Simplicity in TSQL
Recuperacion de Datos de manera rapida
Compresion de datos en un 80%
Tener la informacion en hojas, lo que ayuda a procesar la info rapidamente
Simplicidad en TSQL
Cons
Azure pipelines do not have many parameterization features compared to others, for example AWS.
Error handling, for example when it fails due to memory, only indicates that but not exactly in which process it fell.
a more detailed profiler
Direct purchase through partners, but buying directly from the brand, I think, would be better without intermediaries.
los pipelines para Azure no tienen muchas caracteristicas de parametrizacion en comparacion con otros, ejemplo AWS
El manejo de errores, por ejemplo cuando falla por memoria solo indica eso pero no exactamente en que proceso cayo
un profiler mas detallado
la compra directa a travez de partners, sino comprar directo a la marca creo que seria de mejor manera sin intermediarios
Likelihood to Recommend
I think it is very useful for managing information for business intelligence processing, given that the information that is brought is done quickly, therefore when loaded into the dash it is processed correctly. In addition, for large companies the cost would be significant. The cloud service is good.
Pienso que es bien util para el manejo de informacion para procesamiento de business intelligence, dado que la informacion que se traer es de manera rapida, por ende al cargar en los dash esta se procesa de manera correcta, adicional para grandes empresas el costo seria significativo, el servicio de nube es bueno
<i>This review was originally written in Spanish and has been translated into English using a third-party translation tool. While we strive for accuracy, some nuances or meanings may not be perfectly captured.</i>
IOT data processing and log analysis. The power of unified management is evident out of the box. Quick deployment, comprehensive troubleshooting, and customizable periodic reports increase productivity with fewer resources. Streamline, optimize, and elevate the network management experience through a single pane of glass with organization hierarchy, access controls, alerts from security threats, and usage thresholds - solutions that simplify network fabric and prioritize results to improve efficiency.
Pros
Can scale horizontally on cloud instances.
Can analyze large volumes of time-series data in real time.
Can perform queries in milliseconds.
Can process large amounts of data in parallel.
Cons
Can be expensive for small startups.
Migrating from traditional databases e.g., MySQL, PostgreSQL is complex.
Switching to another database might require significant re-engineering.
Likelihood to Recommend
Good for Applications needing instant insights on large, streaming datasets. Applications processing continuous data streams with low latency. When a multi-cloud, high-availability database is required When NOT to Use Small-scale applications with limited budgets Projects that do not require real-time analytics or distributed scaling Teams without experience in distributed databases and HTAP architectures.
VU
Verified User
Employee in Information Technology (11-50 employees)
We use SingleStore for real time analytics (primarily for dynamic and transactional data). We have row store used for fast compute and streaming data and column store for more historic data fetch. Use case is to stage data from different domains within enterprise in real time streaming (kafka) and compute/apply algorithm on the dynamic data across enterprise for quick decisions.
Pros
Real time computations on large sets of data
Persisting streaming data
Data distributions and fast fetch
Cons
Semantic layer can be better, currently requires significant dev experience to fine tune queries
Query performance dashboard and self optimization methods instead of relying on keys
Bootstrap AI models to help provide recommendations as the user gets into UI (back to semantic)
Likelihood to Recommend
It is extremely good for scenarios where large sets of data is generated in a day and data is streamed. Especially if you would like to run queries, analytics on such data it would really scale and outperform Times DB or Oracle In memory options. But choosing this tech for right use case is key, should avoid using SingleStore like a ER DB and for that there are so many options in the market like Postgres or Oracle lite etc.
VU
Verified User
Employee in Information Technology (10,001+ employees)
We use SingleStore for a super fast client experience, running real time analytics on billions of events arriving every day from various publishers channels.
Pros
Performance - Milliseconds response of 80 tables Joined queries
Scalability - Ability to grow with no downtimes
Client success - Attentive to business needs, deep level support, patches and fixes
Efficiency - Built-in Kafka / S3 / MySQL integrations well adjusted to leverage SingleStore architecture and hardware
.
.
.
.
.
Cons
Add Iceberg tables / files Pipeline
CDC out in form of logfile / binlog / producer to Kafka
Efficiency with multi shard-key use case: Joined three tables when one of them holds both shard keys of the other two.
.
.
.
Likelihood to Recommend
SingleStore shines as a unified solution of high OLTP & OLAP workloads.
The technology suits big data systems with mutual identity (shard key/s), fast JSON processing, vector search for AI features and streaming.
The client success attentiveness and the consistent support of experts in any matter shows the company maturity and their vision for success.
Performing real-time risk calculations on complex financial instruments. Advanced analytics at scale helps with risk management and compliance with regulatory reporting requirements. Other usages include an anomaly detection system, order management platform, and tracking and optimization movement across multiple regions in real-time. The distributed architecture and sub second query responses helps manage huge systems with ease.
Pros
Distributed architecture.
Sub-second query responses.
Handling time series data with high write and query performance.
Cons
The UI can be made more user-friendly.
Kubernetes integration.
Compression and storage efficiency.
Likelihood to Recommend
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.
