SingleStore

Formerly MemSQL

We use it as our application database and have a tremendous performance impact since we are using SingleStore. We also like how it has developed and what future benefits it will offer.

We use SigneStore as the primary database for our Digital Marketing OS. It allows us to process transactions and answer complex analytical queries within the same database.

We use SingleStore to manage a large and complex analytical workload for multiple financial customers. SingleStore is the database behind our real-time user interface. SingleStore also hosts our dashboards that display a mixture of data computed dynamically at the time of display and pre-computed values generated when data is ingested. Our software allows users to track the performance of retirement plans, and to compare data across plans. Many of these operations are data-intensive and we've been able to roll out many new and often compute-hungry features since switching to SingleStore two years ago.

I was doing some data engineering for this one client, and I thought that migration would take up to two weeks. In the end, thanks to Single store I was able to get everything done in less than an hour! The learning curve was incredibly easy, and incredibly well documented!

We are an analytics SaaS product and currently use Singlestore as our primary database. Customer data from different sources - website, ad platforms, CRM are streamed into singlestore and our end users can directly query this data from our web application. Queries are run against the singlestore database which helps us in streamlining the ingestion and analytical use cases.

The core business problems being solved by Singlestore are - having a unified database for customer data, running low latency queries and serving responses within tight SLAs to our clients

We have a lot of queries for analyzing data about student engagement and courses chosen and bought; we need to get future trends by combining our data with the data coming from open sources and bought. Most problems are related to the processing of big volumes of data and presenting it using tableau and power bi dashboards.

We use singlestoreDB as our landing storage for our product. Our product provides insights in data and quality of data. For that we require fast ingestion feom sources like S3, GCS and Azure Blob. We extensively use singlestoreDB's PIPELINE function for ingesting data into SingleStoreDB. We ran our ML algorithms on the data stored on singlestoreDB. Which gave us really good results and helped optimize our algorithms by using some of its inbuilt features

The most important use case is to load huge files into a distributed database system within a short span of time without having memory and time complexities. Whenever there is a need of transferring data for ETL during the staging phase and data warehousing stage, the load should be transferred without a lot of resource usage. Especially in the cloud space, the lesser we use the resources, the more cost-effective it is.

SingleStore is perfect for running data analytics workloads due to how easy it is to get data in & out. The performance and ease of use of the product are almost unmatched, which saves us a tremendous amount of time over having to spin up custom RDS instances for data analytics.

Real-time Analytics and Reporting can be best used in cases as Single Store is very much efficient on faster and complex query processing. It is extremely fast on data ingestion from many sources (AWS, Azure,GCP, Files etc).

I am a DevOps Engineer and I work on Kubernetes, Cloud, I had a use case where I was working on creating a product that can calculate Kubernetes and cloud costs and represent it in that use case I was supposed to ingest data which was being populated in s3 to my database on which I can run queries, this using SingleStore could have been seem-less as it makes data ingestion from cloud simpler and since it helps running lot vol queries with smaller response time could have helped me lower the latency of my service.

the product currently is excellent, it deserves 10/10, just the fact of processing more than 1 million records in a few seconds, whatever in the import of the data or the requests, it is already a great advantage, that we the developers we never dreamed of that. I'm telling you about my experience, no vacuum, because I've had nightmares with large databases, and we have to play right and left to optimize response time It is really amazing. I've never in my life worked on such a fast database

I'd be using SingleStore as the POC Database so as to handle data ingestion and analysis of data with a big volume of E-Learning Data. We process the data for different tenants and provide recommendations based on the processing.

I'd be using SingleStore as the primary Database so as to handle real-time ingestion and analysis of data with big volume. The existing data model is working on processing batch data which is not much efficient and fast. I'll be ingesting the data from Kafka and because of the great compatibility of SingleStore with Kafka, I'll be using it to build reports on the top of the data.

SingleStore is super easy to implement and use. You can deploy a cluster in a matter of clicks. The UI is very intuitive, and everything is easy to configure even for a new user. The look and feel are very similar to any relational DB workbench. The source configuration is very easy. Other than the good HCI, the query engine is very fast and responsive, computes huge datasets, and delivers results in seconds. Compared to MySQL, it is fast, robust, and has a good IDE. I would definitely recommend it.

I would use SingleStore databases on e-commerce websites where loads of data are being generated, and where you need a fast data collection to analyze user behaviors and provide real time recommendation.

I am exploring SingleStore as a personal interest to learn new tech skills.

We are using SingleStore as our primary analytics database within our own platform (called "Pulse"). The Pulse platform is used primarily in Siemens internally, but is also available to enterprises outside of Siemens.
We offer end to end reporting & analytics services and use SingleStore to power our dashboards and reports.

We use MemSQL for a particular slice of our OLAP use cases: client-facing UI dashboards that have solid query latency and data freshness requirements (generally 4 9s under 2 seconds, although we try to get under 1, and data replication and recalculation in under a second), and on small to medium size data sets (10s of GB for fact data, GBs or 10s of GBs of dimension data). It is currently owned by a data platform team that supports client-facing use cases from a number of product teams.

SingleStore is used for 2 main use cases:

• In-house developed A/B testing analysis tool uses this to fetch raw data in real-time when end users analyse the tests using the UI.
• Machine learning models fetch the raw data and calculate needed features in real-time instead of preprocessing the data by the data engineers.

We are proud to say that SKAI is a data company and our main asset is our data. My department handles the Measurements data and other incoming data sources. SingleStore is used across the organization to do real-time calculations, supply fast loading grids and store massive amounts of data. In our department, users needed to configure pre-hand MetaData for the calculations that they are basing their decision on. Our nightly batches used to work extremely hard and waste valuable time and resources to make this data available the next day (while storing it both in raw form and aggregated) and any changes required a rerun of the batch. Today the clients can configure, change and get their aggregated data in almost real-time, while the data is being stored in SingleStore only in its raw form. It allowed us to add more calculations and more data and use fewer resources on our server-side and the added bonus is that our clients love it!

We use SingleStore's managed solution as a data warehouse for the analytical analysis of quality metrics. We ingest data from Kafka and build reports on top of the data to inform our customers about various scenarios. We use hosted business intelligence tools to analyze data and answer both specific as well as open-ended questions that our customers may have.

We worked with MySQL and needed a faster solution that it will be easy to integrate with.
Right now, MemSQL is our main DB and serve all of our customers.
We are transferring data with MemSQL pipelines.
In addition we are collaborating multi DBs into clusters. It is very comfortable instead of MySQL