Free Books
The SingleStore Cookbook: Recipes for Multi-Model, Machine Learning and AI Data Engineering
A hands-on cookbook covering SingleStore’s multi-model capabilities, from time series and geospatial data through vector search, machine learning pipelines and AI-powered applications. The recipes draw on first-hand experience building applications with the platform and are organized into four parts:
- Multi-Model
- Streaming and Big Data Pipelines
- Machine Learning
- AI and Agentic Frameworks
Seven Vector Databases in Seven Days
A practical guide that takes one vector database per day and pairs each with a use case chosen to showcase that database’s strengths. Databases covered:
- PostgreSQL and pgvector - Semantic job search
- MongoDB Atlas - Recipe finder
- Pinecone - E-commerce search
- Weaviate - Research paper discovery
- Neo4j - Fraud detection
- Snowflake - Customer support analytics
- Databricks - RAG over internal documents
Each chapter is self-contained, comes with a Jupyter notebook and gives an assessment of when you’d look elsewhere.
Generative AI: A Manager’s Guide
A practical guide for managers, directors and executives who need to make decisions about AI in their organizations, not the engineers building it, but the people responsible for making it work well. The book uses a single central metaphor, the Digital Intern, to frame what AI is genuinely good at, where it falls short and what managing it actually requires. It covers governance, risk, board-level accountability, business case building and the organizational change of moving from pilot to embedded capability.
Seven Ways to Do Vector Search in Python
A practitioner’s guide that benchmarks seven Python libraries against the same dataset, measuring recall and latency consistently so you can compare like-for-like. Libraries covered:
- FAISS
- Voyager
- Scikit-learn NearestNeighbors
- PyNNDescent
- USearch
- Chroma
- LanceDB
Each chapter covers one library, explains what it’s genuinely good at and when you’d reach for something else.
Real-Time Vehicle Tracking with Neo4j, Databricks Lakebase and OpenStreetMap
A fleet operations demo that puts ten simulated vehicles onto real road networks loaded from OpenStreetMap. The architecture:
- Neo4j Aura holds the road network graph
- Databricks Lakebase stores live vehicle positions
- Databricks Lakehouse handles historical analytics
Two Streamlit dashboards display live positions and trend data. The primary demo uses the London Borough of Merton, with additional configurations for San Francisco and Singapore.
Real-Time Supply Chain Routing with Neo4j, Snowflake Postgres and Confluent Kafka
In progress.