CrateDB is a perfect fit in scenarios where you need a distributed SQL database that excels in handling large amounts of data. Let's look at some specific use cases and scenarios where CrateDB shines.
First the Internet of Things. Here we need to store and analyse massive volumes of sensor data in near real time. Applications for this can include smart cities, industrial IoT or telematics.
Next: time series. Here we want to handle high velocity data streams and perform time based queries efficiently over large data sets, again in real time or near real time. This is ideal for applications such as monitoring systems, financial market data analysis, and environmental data tracking. When working with logs, CrateDB is a very good choice for ingesting, storing, and analysing log data from various sources such as web servers, applications, and network devices. CrateDB can provide insights into operational performance and security.
When working with geospatial data, Crate's support for geospatial queries makes it suitable for location based services and applications that require the processing and visualisation of geospatial data.
CrateDB's horizontal scalability and ability to handle large data sets make it suitable for big data applications and use as an application backend. Also, don't forget that through CrateDB's support of the PostgreSQL wire protocol, you can have access to a large range of drivers in popular programming languages.
Let's now turn to AI and ML: CrateDB has native support for vectors and a performance similarity search that many AI applications require.
When building digital twins you will have generated vast amounts of datas from sensors and other IoT devices. CrateDB's architecture is optimised for ingesting and processing large scale data in real time and this is essential for keeping your digital twin applications updated. If you require a database that could provide real time insight and support for analytical queries on large data sets then CrateDB's, distributed SQL engine, flexible schemas and column of storage format make it an ideal choice.
There are many benefits to consolidating all of your data into one database. CrateDB is ideal for this with its support for different data formats and types, both structured and semi structured objects, BLOB and vectors. Querying is done through a standard SQL interface, which makes your database consolidation applications much easier to develop and maintain. All of these projects apply equally across today's modern data intensive industries.
As shown on the slide. As a company, CrateDB has achieved a score of 4.4 out of five on the G2 Reviews site. Our customers also rate our support 4.9 out of five when surveyed. Adoption is growing and we see many interesting use cases across a range of industries.
When should you use CrateDB? It works well for use cases where you have relationships between different data sets and those are of different types. For example, you might have a mix of structured, semi structured, unstructured or time series data. You may also be working with real time analytics or monitoring application analytics.
Don't forget that CrateDB also supports full-text search based on Apache Lucene. It's a great choice for event tracking, geospatial data analysis and all hybrid applications.
When deploying CrateDB, you can deploy in the cloud or on premises in your own data centre. When choosing CrateDB, there's a couple of things to bear in mind. It's eventually consistent and strongly consistent on primary key lookups. It favours normalised data models. When designing for CrateDB, you'll need to keep these things in mind.