Python Data Jobs for Seamless Data Migration Launched by Space and Time

The SQL-driven service will enable seamless data migration into and out of Space and Time and serve as a foundation for more complex businesses across the DeFi ecosystem.

AI-powered data house Space and Time announces its Python Data for Jobs, which facilitates smooth data migration for businesses and startups in Web 3. The latest development is designed to expedite the process of transferring data to and from Space and Time using AI SQL technology called Houston. The beta version is currently available on the Space and Time Studio.

By extending the capabilities of its Proof of SQL tool, launched in August, which allows smart contracts to retrieve and process data using SQL in a secure manner, Python Data for Jobs aims to cater to Web 3 businesses with long-running Python jobs. This innovative solution eliminates the need for developers to create Python scripts to extract, transform, and load data onto their platforms. The platform relies on Python, widely used by data engineers, to extract, transform, and load data without any coding. Additionally, the platform accurately connects long-running Python jobs to smart contracts, ensuring data integrity.

In essence, Python Data for Jobs allows users and developers to seamlessly transfer data to and from the Space and Time platform using Houston, the AI-powered SQL service. Users can input natural language prompts, which are then converted into SQL queries that return the desired results. The beta version of Houston can generate simple extract, transform, load (ETL) scripts from Web2 databases or Web3 decentralized storage platforms, prepare the data, and load it into Space and Time.

With Houston, a script is created that connects to PostgreSQL (or other platforms like Snowflake or IPFS), understands the database contents, transforms the data, creates tables in Space and Time, and imports rows from PostgreSQL into Space and Time. This eliminates the need for coding Python scripts. Instead, a simple natural language command is sufficient.

In addition, Python Data for Jobs enables the retrieval of processed data from Space and Time directly to a smart contract. Space and Time is developing a zero-knowledge (ZK) proof architecture to ensure data integrity. However, the current service utilizes optimistic security (similar to optimistic rollups) to ensure efficient and secure data migration across the platform.

When a user runs a Python Data Job in Space and Time, the inputs, outputs, and code are all hashed to a major chain. The script is only executed once, and if the outcome is not as expected, the user can request proof, and Space and Time can cryptographically prove what was executed. Rather than proving it in real-time with redundant computation and consensus, the platform runs it once and hashes all the metadata to create an auditable and tamper-proof trail, incentivizing node operators not to tamper with the execution.

Python Data for Jobs: Relevant Use Cases

Python Data for Jobs is a significant development in the decentralized finance (DeFi) world for businesses that need to collect large volumes of data over extended periods. By querying Houston and granting access to the source database, the AI SQL service can retrieve the information and generate a Python script that extracts the data and replicates it on Space and Time. Jobs requiring substantial amounts of data can be efficiently processed and prepared for aggregation.

In addition to seamless data migration, Python Data for Jobs is crucial for enabling complex off-chain computations, such as forecasting future token prices. This is especially important for Web 3 exchanges and lending platforms. The integration of Python Data for Jobs into DeFi protocols allows for the incorporation of sophisticated financial models into Dapps’ smart contracts, all with optimistic security. This opens up a plethora of new use cases for blockchain technology in the DeFi space.

Disclaimer: This article is provided for informational purposes only. It is not intended to be used as legal, tax, investment, financial, or other advice.

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