SingleStore

Formerly MemSQL

I would recommend SingleStore for use cases where a relational database is needed for handling both online transaction processing (OLTP), online data streaming, and batch big-data processing. Since it supports both in-memory and disc-based tables, it is suitable for handling large tables as well as smaller, fast ones.
SingleStore is MySQL compatible in terms of connection so you could use it with any connector that supports MySQL, and is mostly TSQL compatible so if you come from Transact-SQL background the switch will be simple.
While supporting JSON and document datatypes, I would not use SingleStore as a main document database, as its strengths lay in the relational realm.

SingleStore HTAP engine is well suited for real-time analytics, fast ingestion, scaling OLTP system like MySQL.
When you need to run reports or perform aggregates on billions of rows and you get result in seconds, you cannot get this experience with other OLTP engines.
I wish DBtLab was a little more developer and supported for SingleStore. This would allow to perform better data transformation.
You can use stored procedures, but DBTLabs has become a standard for dimensional modeling in data warehousing projects. This is probably why SingleStore has trouble piercing in the data warehouse world. It is definately capable to compete with Snowflake when it comes to scalability, query performance, data compression, but Snowflake has ravaged the data warehouse market in few years and large corporations have already invested lots of money in migrating into Snowflake.
The SingleStore community needs to grow. Everyone who uses SingleStore loves it.

When analytics are needed on millions of rows of data SingleStore is a great solution.

SingleStore is well suited to handling tables with huge numbers of rows and being able to query these. It does its own thing with auto-incremented primary keys, so it may be difficult to migrate legacy MySQL applications with large numbers of tables.

Business analytics is one of the areas where SingleStore really helped. As we implemented BI platforms, the previous DB would bog down so bad that it negatively affected our online products. Moving to SingleStore made a huge difference. General performance is amazing. We gained speed in areas we were not even expecting to the point we had to slow down or put rate limits in place to avoid duplications - something completely unexpected. The hosted service is great. We no longer have to worry about maintenance, db infrastructure or system updates which saves a significant amount of time for the IT team.

SingleStore is a phenomenal solution and replacement to MySQL/MariaDB. Being able to instantly start using it without any prior experience is fantastic, as other solutions required learning new syntax which slows development.

In addition, SingleStore offers many opt-in features such as Rowstore rather than being obliged to use it. I believe it’s better to use features as needed, rather than being forced to dive in and deal with learning things.

One of the biggest factors to what drew me to SingleStore was the self-managed option, being a startup at the time we didn’t want to pay for a solution. We still use the self-managed option 2 years later, and couldn’t imagine using anything else.

Big Data ingestion and real time analytics capability/performance is crucial to the success of our water monitoring IoT Platform - FLOWD

SingleStore is excellent at computing heavy workflows. The column-store table design combined with efficient sharding strategies make for a beast of a database that lets you compute large queries at ease. Singlestore also offers a variety of developer tools such as SQrL (their AI assistant) and their Query Profiler, which are massively helpful for figuring out what bottlenecks exist in a given query.

Reliable, scales and you can do full text search across the data. Customer support ticketing system could be improved but it's barely a con

SingleStore is best suited for high concurrency and low latency query executions.

When intense analytical queries are needed to be done on billions of rows of data then SingleStore is great and outperforms other databases. If an application just requires transactional operations then its probably overkill.

SingleStore is great for large amounts of data, and handling highly performant queries at scale, across many tables. We've built intense queries, that finish extremely quick, helping us provide realtime data.

If a company is not needing this type of performance, or do not need complex analytical queries, I'd recommend looking else where for a simpler and cheaper database service.

Well-Suited
1. Real-Time Analytics -Ideal for platforms needing instant data analysis, like financial trading systems.
2. Operational Intelligence-Great for sectors such as e-commerce or logistics that require real-time operational decision-making.
3. Scalable SaaS Applications - Suitable for fast-growing SaaS applications due to its excellent scalability.

Less Appropriate
1. Budget-Constrained Projects - Not the best fit for projects with limited funding due to its cost structure.
2. Complex Data Warehousing- Other solutions might be better for deep analytical queries over massive, historical datasets.
3. Unstructured Data Storage-Less efficient for storing and managing large volumes of unstructured data like images or videos.

SingleStore is suitable when using transactional and analytical databases in one. SingleStore is highly scalable for new applications where they start small while planning for a large future growth.

SingleStore is perfectly suited for ingestion of big-data at faster speed

usage as document store is still very limited . Using SingleStore as a relational database is a breeze but using it as a document store to needs a lot of improvement.

Suited well for application databases. Not best fit for key-value storages.

SingleStore shines where you want to execute complex analytical queries over your datasets in real-time, and still want to handle transactions. It prevents you from having a transaction database and a separate async analytical database. However, running SingleStore is expensive, it requires a powerful cluster with a lot of RAM. If you don't need to process real-time analytics, you won't benefit much from SingleStore.

Our workload is 100% analytical. We also have to ingest a lot of data each month. SingleStore is a perfect match for our needs because it has fast pipelines for data ingestion and great performance, even in large and complex queries. We need fast response times for our user interface and great performance in our ETL processes, which are rather complicated. SingleStore handles all of this very well.

If you have a massive database, then Singlestore is perfect for you! You can save lots of time on many projects, get everything faster :)
However, if you database is relatively small (under 1GB) then the value that SingleStore provides may not be there for you. Yes, even 1GB can take 15 minutes to upload sometimes but that is not awful.

Any application which needs 1. Low Latency query responses and 2. Access to real time data would benefit from implementing SingleStore. Use cases which are not time sensitive and can run in adhoc batch mode may be well suited to work off data warehouses. Also, SingleStore helps reduce operational overhead in terms of engineers managing batch and real time flows separately

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 to transfer 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 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.

It has transformed into my go to database software. The only scenario in which I wouldn't use or recommend it, is if their base managed cluster is too expensive and I don't want to managed it myself

singlestoreDB can be used for both staging data and creating marts on which ML algorithms can be run.
The most interesting part is their pipeline function which allows really fast and consistent ingestion of data. And setting up these pipelines is really easy with configurations for sources system creds.

1. The one place where it is required the most is, during ETL processing where the resource utilisation is optimized and faster data load is achieved.

2. The places where it is not suited are, when the data load is very low and there is not much difference achieved while execution of data loads.