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.
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SingleStore Reviews
23 Reviews
Enterprises (1,001+ employees)
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We use the SingleStore as our OTLP side to our Vertica as DWH. the purpose is to do things fast like PII or Enrich data . we load the most of the data straight from Kafka and it's working pretty well.
Pros
pipelines - load data from variety of sources
availability of the cluster and redundancy
high performance - queries run fast on SingleStore
shard tables
Cons
we need to know what is the road map of SingleStore
I think some feature are still not mature enough
AMD cpu not supported as Intel cpu
I think SingleStorehas a long way
Likelihood to Recommend
load data from Kafka or other sources such as S3 using pipelines are working very well and fast queries are running fast on the system also DML's
VU
Verified User
Administrator in Information Technology (1001-5000 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)
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.
SingleStore is being used for concurrent high performance reporting powering Tableau BOBJ PowerBI and conventional .net and java pages. It is also being used for real time reporting with data loaded from OLTP systems using GoldenGate, Kafka and Spark. SingleStore is being used in Sales, Finance, Supply Chain, Inventory Management, Marketing, Servicing and Logistics reporting.
Pros
Powering multiple dashboards on a single screen within short span of time
Real time reporting for IOT devices
Warehouse queries powering dashboards which suffers due to concurrency in Warehouse Database Systems
Cons
Auto failover to DR Site
Eventual Consistency
Point in time recovery
Robust Monitoring
Likelihood to Recommend
SingleStore is well suited for warehouse dashboards used at executive level. There is no latency due to concurrency and the performance is terrific compared to SingleNode traditional databases.
High speed data ingestion powering IOT and other workload which also needs high speed seeks.
SingleStore support as well as Marketing team is excellent. They are always with you to troubleshoot until we achieve a fix.
Where SingleStore lacks is in OLTP systems where there is no capability of PITR. It is more like Flashback. Monitoring is still not robust, they provide exporter that can be used in Prometheus for alerting, but no monitoring rules are directly provided by SingleStore. Grafana prebuilt dashboard is provided which is good.
VU
Verified User
Administrator in Information Technology (10,001+ employees)
We use a single store for an analytics use case in our organization. The previous database was not distributed, and scaling issues were occurring, so a single store with a distributed nature helped us solve this issue. We mainly use the columnar tables in a single store for analytics.
Pros
Sharding/Distributed Database.
Analytics Queries.
Good Observability.
Cons
Row tables.
Query Profiler.
Likelihood to Recommend
In my opinion, it is well suited for analytics use cases. However, it is less appropriate for transactional data as row tables are saved in memory, and single nodes are more costly than traditional databases.
The SingleStore is high-performance, scalable, and real-time analytics on large datasets, particularly for hybrid transactional and analytical processing (HTAP); we had a requirement to handle fast OLTP workloads (e.g., processing transactions, user interactions, etc.). It is a Fast column store database with a Pipeline concept. In our current use case, we do data ingestion directly into the database from external sources like Kafka, S3, and various other data streams with their "data pipeline model." It allows for high-throughput data ingestion without needing separate ETL (Extract, Transform, Load) processes, enabling SingleStore to handle transactional and analytical workloads with minimal latency.
Pros
Very fast columnar store database.
Data pipeline model.
Designed for horizontal scalability across distributed clusters.
Cons
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.
Likelihood to Recommend
Lack of enterprise capabilities in a patch release, large-sized backup and restore, CDC, and tech support.
We use it to achieve fast performance and low latency with our custom applications. It has helped to improve performance compared to other database technologies.
Pros
Fast performance.
Pre-aggregation.
Little latency.
Cons
Ease of use.
Not having to spend so much time modeling the data to get it ready.
Cost.
Likelihood to Recommend
Well-suited for pre-aggregations and performance improvements.
We felt that running queries on BigQuery for every query is really slow, especially from an user point of view. After seeing the drastically improved latency of SingleStore we decided to use to solve this issue. We currently use it to run low volume queries for a much faster response, significantly improving user experience.
Pros
Low latency
Better concurrency
Cons
Query cost is bit higher.
Likelihood to Recommend
SingleStore is best suited for high concurrency and low latency query executions.
We are using SingleStore as a DBMS. We wanted fast engine which reflects the data on portal immediately for better user experience. Also there were couple of use-cases where we wanted capability of transactional and real-time analysis. And SingleStore has fulfilled this requirement in my organization as it has capability of fast ingest and high-performance queries.
Pros
Gives result of complex queries on large scale dataset
Data ingestion is much faster
User friendly SingleStore dahboard
Cons
There are lot of improvements need to in SingleStore official document
Likelihood to Recommend
SingleStore is perfectly suited for ingestion of big-data at faster speed
