The Spice v0.11-alpha release significantly improves the Databricks data connector with Databricks Connect (Spark Connect) support, adds the DuckDB data connector, and adds the AWS Secrets Manager secret store. In addition, enhanced control over accelerated dataset refreshes, improved SSL security for MySQL and PostgreSQL connections, and overall stability improvements have been added.

Highlights in v0.11-alpha​

DuckDB data connector: Use DuckDB databases or connections as a data source.

AWS Secrets Manager Secret Store: Use AWS Secrets Managers as a secret store.

Custom Refresh SQL: Specify a custom SQL query for dataset refresh using refresh_sql.

Dataset Refresh API: Trigger a dataset refresh using the new CLI command spice refresh or via API.

Expanded SSL support for Postgres: SSL mode now supports disable, require, prefer, verify-ca, verify-full options with the default mode changed to require. Added pg_sslrootcert parameter for setting a custom root certificate and the pg_insecure parameter is no longer supported.

Databricks Connect: Choose between using Spark Connect or Delta Lake when using the Databricks data connector for improved performance.

Improved SSL support for Postgres: ssl mode now supports disable, require, prefer, verify-ca, verify-full options with default mode changed to require. Added pg_sslrootcert parameter to allow setting custom root cert for postgres connector, pg_insecure parameter is no longer supported as redundant.

Internal architecture refactor: The internal architecture of spiced was refactored to simplify the creation data components and to improve alignment with DataFusion concepts.

Announcing the release of Spice v0.10.2-alpha (Apr 9, 2024)! 🔥

The v0.10.2-alpha release adds the MySQL data connector and makes external data connections more robust on initialization.

Highlights in v0.10.2-alpha​

• MySQL data connector: Connect to any MySQL server, including SSL support.

• Data connections verified at initialization: Verify endpoints and authorization for external data connections (e.g. databricks, spice.ai) at initialization.

Announcing the release of Spice v0.10.1-alpha! 🔥

The v0.10.1-alpha release focuses on stability, bug fixes, and usability by improving error messages when using SQLite data accelerators, improving the PostgreSQL support, and adding a basic Helm chart.

Highlights in v0.10.1-alpha​

Improved PostgreSQL support for Data Connectors TLS is now supported with PostgreSQL Data Connectors and there is improved VARCHAR and BPCHAR conversions through Spice.

Improved Error messages Simplified error messages from Spice when propagating errors from Data Connectors and Accelerator Engines.

Spice Pods Command The spice pods command can give you quick statistics about models, dependencies, and datasets that are loaded by the Spice runtime.

TL;DR: We've rebuilt Spice.ai OSS from the ground up in Rust, as a unified SQL query interface and portable runtime to locally materialize, accelerate, and query datasets sourced from any database, data warehouse or data lake. Learn more at github.com/spiceai/spiceai.

In September, 2021, we introduced Spice.ai OSS as a runtime for building AI-driven applications using time-series data.

We quickly ran into a big problems in making these applications work... data, the fuel for intelligent software, was painfully difficult to access, operationalize, and use, not only in machine learning, but also in web frontends, backend applications, dashboards, data pipelines, and notebooks. And we had to make hard tradeoffs between cost and query performance.

We felt this pain every day building 100TB+ scale data and AI systems for the Spice.ai Cloud Platform. So we took our learnings and infused them back into Spice.ai OSS with the capabilities we wished we had.

We rebuilt Spice.ai OSS from the ground up in Rust, as a unified SQL query interface and portable runtime to locally materialize, accelerate, and query data tables sourced from any database, data warehouse or data lake.

Spice is a fast, lightweight (< 150Mb), single-binary, designed to be deployed alongside your application, dashboard, and within your data or machine learning pipelines. Spice federates SQL query across databases (MySQL, PostgreSQL, etc.), data warehouses (Snowflake, BigQuery, etc.) and data lakes (S3, MinIO, Databricks, etc.) so you can easily use and combine data wherever it lives. Datasets, declaratively defined, can be materialized and accelerated using your engine of choice, including DuckDB, SQLite, PostgreSQL, and in-memory Apache Arrow records, for ultra-fast, low-latency query. Accelerated engines run in your infrastructure giving you flexibility and control over price and performance.

Announcing the release of Spice v0.10-alpha! 🧙‍♂️

The Spice.ai v0.10-alpha release focused on additions and updates to improve stability, usability, and the overall Spice developer experience.

Highlights in v0.10-alpha​

Public Bucket Support for S3 Data Connector: The S3 Data Connector now supports public buckets in addition to buckets requiring an access id and key.

JDBC-Client Connectivity: Improved connectivity for JDBC clients, like Tableau.

User Experience Improvements:

• Friendlier error messages across the board to make debugging and development better.
• Added a spice login postgres command, streamlining the process for connecting to PostgreSQL databases.
• Added PostgreSQL connection verification and connection string support, enhancing usability for PostgreSQL users.

Grafana Dashboard: Improving the ability to monitor Spice deployments, a standard Grafana dashboard is now available.