SingleStore

Formerly MemSQL

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 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.

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.

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

SingleStore as the backbone for our Data Analytics platform

With SingleStore, we are able to replace multiple other databases and started migrating from on-premise to SAAS. As SingleStore provides both scenarios, the migration to the cloud will be a smart and simple one. We now use SingleStore for our data and analytics environment. As we now can use the pipelines towards our data lakes and send data via procedures into our tables, we have achieved the next step in our data management by adding additional metadata during the ingestion. Using text search and the geospatial functions adds more functionality than we had before in our previous databases. The performance and scalability are beyond our expectations.

• The scenario to migrate on-premise and scale up to the cloud as a SAAS.
  
• Using HTAP means we can now use our database for transactions and analytics.
• The main language is similar to MYSQL, so our organization has already the knowledge to master the system.
• The SingleStore academy is a good program to support our organization to master the environment.

We build Data Warehousing solutions for our customers and provide consulting and resell license. [SingleStore (formerly MemSQL)] is a powerful technology which allows to solve most of the toughest data challenges within an organization. Its speed and flexibility makes it a key player in an organization's data innovation.

Right now we’re using [SingleStore (formerly MemSQL)] in the whole core platform, we were looking for a database that could handle a huge amount of transactions at the same time without loosing any and have the reliability of writing and storing the records fast so other services can use the data for finalizing the transactions. Also we were aiming to have a product which could be used as a storage solution for Data Scientists and our back office platform.

We use it to hold and query real estate listings in four states. This is the engine behind our website's and mobile app's main feature - property search. The queries have geospatial criteria. Because of the volume of records and complexity of queries, MemSQL was an obvious choise for it's performance. Now, as SingleStore offers collumnar storage, we can use it for other things like analytics and reporting.

We use SingleStore DB (formerly MemSQL) to store a real-time feed of all kinds of open source data. The data get full-text indexed, stored in start schema, and instantly ready for analysis. Multiple departments have access to multiple dashboards backed up with SingleStore DB (formerly MemSQL).

SingleStore DB (formerly MemSQL) is currently being used as the MPP DB for our data lake. We use it to ingest different information from the operational platform in order to enable it for app development and analytics. Using SingleStore DB (formerly MemSQL) allowed us to process a high volume of data since it has scalability by design. Memory management in SingleStore DB (formerly MemSQL) is very efficient and even on an infra level the cluster management is straightforward.