Enhancing the capabilities of TerrariumDB, a real-time, schema-less database engine, is our top priority. Currently, our analytics backend relies on a JSON-based API, which proves vital on a daily basis. However, we are excited to announce the integration of SQL support, paving the way for a future of expanded functionality

Last year, in the Terrarium Team we’ve decided to migrate our internal network communication from REST and WebSockets to gRPC. TerrariumDB is a distributed database system so choosing the right protocol can significantly improve its performance, similar to how optimizing code or algorithms can bring about similar benefits. Our

There are numerous cutting-edge technologies for real-time analytics that can be categorized into various types, each catering to distinct purposes and necessitating different technologies and approaches. Examples include Streaming Processing, Time Series Databases, Real-time Monitoring and Alerting, and more. In this article, we will explore how our customer classifies Synerise

In the TerrariumDB, we have three types of nodes: Agent, Gateway (we named it ScorpIO), and Controller: * A Controller is responsible for the configuration of Agent nodes and Gateway and handles transactions, * An Agent is responsible for managing data storage and executing crucial compute operations for optimal performance * ScorpIO is

I was a fresh-faced newcomer to the tech world when I joined Synerise 1.5 years ago, with no commercial experience and just a few small projects done by myself.

Pre-processing raw input data is a very important part of any machine learning pipeline, often crucial for end model performance. What is more, different fields of ML require different methods to represent relevant information from the input domain in machine-readable format – as numerical vectors. In image processing, we can use

Self-driving cars do not yet roam the streets. AI has yet to wait to independently generate a computer game or a larger piece of software, and chatbot assistants are only carefully deployed with human assistants as backup. We continuously wonder how much longer it will take until AI reaches human

In data science, we sometimes need to calculate hash functions of unsigned 32-bit integer tuples. This need can arise in simple use cases such as efficient pairs counting, but also in approximate data structures which require fast hashing. Such data structures provide a tradeoff between accuracy and speed/memory storage:

Having achieved remarkable successes in natural language and image processing, Transformers have finally found their way into the area of recommendation. Recently, researchers from NVIDIA and Facebook AI joined forces to introduce Transformer-based recommendation models described in their RecSys2021 publication Transformers4Rec: Bridging the Gap between NLP and Sequential / Session-Based Recommendation,