Data Science & Machine Learning

Getting started with data science and ML workflow

Built with technologies like Apache Arrow, Spice is designed from the ground-up for data-driven apps, data science and machine learning.

Get started with the following guide.

1. Find the data you need

Spice includes a growing set of web3 data, including blockchain data, cryptocurrency and token prices, ENS domains, and more.

The dataset reference in the SQL Playground.

Explore the full list of datasets for an overview and see SQL Query Tables for schemas and details.

2. Query datasets using SQL

Querying datasets is as easy as querying data from any SQL database. Try SQL from your browser at Spice.ai.

Querying datasets with SQL in the Playground.

Reference SQL best practices for tips and the best query performance.

3. Refer to the SQL reference

Spice uses an Apache Calcite based query engine, which supports ANSI SQL with additional SQL dialect.

The SQL Reference at docs.spice.ai

Refer to the SQL reference for dialect specific data types, functions, and commands. SQL keywords are also indexed in search for quick lookup.

4. Use the Python SDK

Import Spice data in notebooks like Kaggle, Jupyter Notebooks, Google Colab, Anaconda Notebook, etc. with 3 lines of Python code.

Querying Spice from Python in 3 lines of code.

5. Use familiar data science tools and libraries

Easily use Python libraries like numpy, pandas, pyplot, sklearn, xgboost, and more to perform exploratory data analysis (EDA), create articulate visualizations, and build dynamic machine learning models.

See sample Kaggle Notebooks on the next page --> \