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- Space and Time Launches Python Data Jobs to Solve SQL Proof Challenges
- Python Data Jobs allows developers to input and output tamper-proof data to/from its platform
- Developers can seamlessly migrate smart contract data and build complex DeFi DApps
Trustless Web 3 Data and Analytics Platform Space and time revealed Python data for tasks, this Wednesday, marking a new milestone in how smart contracts migrate and extract data using proof of SQL. The platform aims to enable companies and startups to seamlessly migrate their data and create more complex financial products in the decentralized domain.
In August last year, Space and Time launched the first and only Zero Knowledge (ZK) system proof of SQL, a powerful tool that allows smart contracts to retrieve and process data with SQL in a cryptographically secure manner. Although the ZK proof of SQL has proven useful in the blockchain space, the technology does not cover all business use cases of Web 3, and particularly long-running Python tasks.
As a new development, Python Data for Jobs aims to enable users to leverage Python to extract data from their existing database, transform it, and load it across space and time in the simplest and most cost-effective way. as quickly as possible, without actually writing any code. Additionally, Python will connect to smart contracts and transfer data cryptographically, ensuring that the data is tamper-proof.
Crack the code on how Python Data for Jobs works
As explained, Python Data for Jobs allows users and developers to retrieve and process data transparently, faster than SQL proof. The latest product contributes to two main functions, namely importing data into the Space and Time platform and sending it from the platform to a smart contract.
Python Data Jobs accelerates the process of obtaining data in space and time from any off-chain source without ever writing code using its AI, Houston. With the launch of Space and Time's Houston, an open AI-powered SQL service, earlier this year, users can write a prompt in natural language and the SQL AI will convert it into an SQL query and return the results. For example, a user can type “show me the top 5 wallets on Sui with the most transactions ordered by balance” into the AI chatbot and Houston will return the results in a table. This reduces the tedious, expensive, and time-consuming process of coding in Python to retrieve data from external sources, allowing anyone to query data across space and time.
In addition to transferring data across space and time, Python Data for Jobs also allows users and developers to extract data from space and time, process it, and send it to a smart contract. This has been a difficult problem for most blockchains to solve, given that Python tasks typically run for long periods of time.
Imagine you are calculating the probability that the price of a cryptocurrency will remain above a given price for an extended period of time. You will need a Python script that will need to continuously capture market data, process it and run a simulation, which would take around 20 seconds. Since blockchains require consensus, a script running for a long time may see some nodes resolve the calculation faster or slower than others, which is not ideal.
Python Data for Jobs introduces a new architecture using the ZK proof for Python to make this process easier. Relying on optimistic security (similar to optimistic rollups), the platform hashes all inputs and outputs on a major chain, meaning the script will only need to run once and a result will be given . If the result is not as expected, the user can request a proof and Space and Time cryptographically proves what was executed. This reduces the time required to run the computation on multiple nodes by hashing all metadata, creating a tamper-proof audit trail to incentivize node operators not to tamper with the execution.
Real-World Use Cases for Python Data for Jobs
The new development brings several benefits to developers and users, including seamless database migrations and enables complex calculations for decentralized finance (DeFi) DApps. By simply querying Houston and giving it access to the source databases, Python Data for Jobs will generate a script for the query, retrieve the data from the source, determine the schema, and replicate it in space and time in a single inference LLM. Several platforms such as Trufflea real-time inflation data platform, and dClimatea platform containing huge volumes of weather data, uses the solution for seamless data migration and execution.
Finally, the platform will also expand the development of new DeFi platforms by allowing users to integrate sophisticated financial models, which goes beyond what proof of SQL allows. Decentralized exchanges and lending platforms are expected to be the biggest benefactors of Python Data for Jobs, the platform offering smart contracts with optimistic security. The platform makes it easy to collect, migrate and execute historical price movements in an inviolable manner.
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