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tusharchou / local-data-platform

python library for iceberg lake house on your local
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Create a plan for implementation of near data api #12

Open tusharchou opened 2 days ago

tusharchou commented 2 days ago

To do eod

mrutunjay-kinagi commented 2 days ago

Please assign this issue to me .

mrutunjay-kinagi commented 1 day ago

Problem Statement:

"Design a scalable data storage and querying solution using Apache Iceberg file format and Near Protocol Catalog to efficiently store and analyze Near Protocol traders' dataset."

Potential Solutions:

Data Storage:

  1. Apache Iceberg: Leverage Iceberg's features like partitioning, metadata management, and incremental data processing.
  2. Near Protocol Catalog: Utilize the catalog's data discovery, schema management, and query optimization capabilities.

Query Engine:

  1. Apache Spark: Integrate with Iceberg and Near Protocol Catalog for efficient data processing and querying.
  2. Presto or Trino: Leverage these query engines for fast, distributed SQL queries.

Benefits:

  1. Scalable data storage and querying
  2. Improved data management and discovery
  3. Optimized query performance
  4. Enhanced data collaboration and sharing
tusharchou commented 4 hours ago

Plan to Implement a Blockchain Data Platform for Traders

Phase 1: Planning and Requirements Gathering

  1. Define Objectives

    • Provide real-time and historical data to blockchain traders for decision-making.
    • Offer data analytics, insights, and reporting.
    • Support different blockchain protocols and crypto-assets.
    • Ensure platform scalability, security, and reliability.

  2. Stakeholder Identification

    • Traders and institutional investors.
    • Developers (internal team or third-party).
    • Data providers (on-chain data sources).
    • Regulators and auditors (for compliance).

  3. Data Sources

    • Identify blockchain protocols (e.g., Bitcoin, Ethereum, Solana) to support.
    • Integrate on-chain data: transaction details, wallet balances, smart contract events.
    • Off-chain data: Market data (price, volume), social sentiment, news feeds, etc.
    • Use APIs and web scraping for external data sources (e.g., exchanges).

  4. Key Functionalities

    • Real-time data streaming: transactions, market orders, price movements.
    • Historical data for backtesting.
    • Customizable alerts (e.g., price thresholds, whale movements).
    • Analytics tools: charts, technical indicators, and automated reports.
    • Portfolio tracking for traders.

  5. Compliance and Security

    • Comply with relevant financial and data privacy regulations.
    • Implement encryption, secure APIs, and access controls.
    • Ensure data immutability and traceability through blockchain integrity.

Phase 2: Architecture and Design

  1. Data Architecture

    • Data Ingestion Layer:

      • Integrate with various blockchain nodes for on-chain data (via APIs or nodes).
      • Use Web3, JSON-RPC, or similar protocols to fetch data from smart contracts.
      • APIs for real-time off-chain data.
      • Data streaming technologies (Kafka, Pub/Sub) for real-time event processing.

    • Data Storage:

      • Use a mix of relational and NoSQL databases (e.g., PostgreSQL, MongoDB) for different types of data (transaction records vs. time-series market data).
      • Data warehousing (e.g., AWS Redshift, Snowflake) for storing historical data.
      • Ensure data availability using cloud services or decentralized storage (e.g., IPFS).

    • Analytics Layer:

      • Implement data pipelines to clean, transform, and aggregate data using tools like Apache Spark, Flink, or AWS Lambda.
      • Machine learning models to predict market trends and identify patterns.

    • Front-End/UI Design:

      • Create dashboards for traders using frameworks like React.js or Angular.
      • Visualize data through charts (using libraries like D3.js or Chart.js).
      • Offer mobile app support for trading on-the-go.

  2. System Architecture

    • Microservices: Design independent services for data ingestion, transformation, storage, and analytics.
    • APIs: Expose data via secure APIs for traders or external apps.
    • Cloud Infrastructure: Host the platform on cloud services (AWS, GCP, Azure) for scalability.
    • Blockchain Nodes: Maintain and manage nodes for selected blockchain networks or work with third-party node providers like Infura or Alchemy.

Phase 3: Development

  1. Set up the Blockchain Infrastructure

    • Integrate and run blockchain nodes or use APIs for data extraction.
    • Ensure consistent synchronization with blockchains for real-time data.

  2. Develop Data Pipelines

    • Build data ingestion systems to collect, process, and store on-chain and off-chain data.
    • Handle high-throughput data with real-time processing frameworks (e.g., Apache Kafka, AWS Kinesis).

  3. Back-End Development

    • Build RESTful APIs to provide data access and reports.
    • Implement secure authentication (OAuth, JWT) and authorization.
    • Use caching systems (e.g., Redis) to optimize performance.

  4. Front-End Development

    • Design and develop user-friendly interfaces with real-time dashboards, charts, and market insights.
    • Mobile-responsive design for trading on multiple devices.

  5. Testing

    • Ensure robust unit testing, integration testing, and performance testing.
    • Validate blockchain integrations, API reliability, and data accuracy.

Phase 4: Deployment and Monitoring

  1. Deployment

    • Use containerization (Docker, Kubernetes) for scalable and fault-tolerant deployment.
    • Continuous Integration/Continuous Deployment (CI/CD) pipelines for automatic updates.
    • Launch in a cloud environment (AWS, Azure, GCP) for flexibility.

  2. Monitoring & Analytics

    • Set up monitoring tools (Prometheus, Grafana, CloudWatch) to monitor platform health, performance, and security.
    • Ensure real-time alerts for critical issues (e.g., data lags, API failures).

  3. User Feedback & Continuous Improvement

    • Gather feedback from traders and key users to improve features and user experience.
    • Roll out regular updates and performance optimizations.

Phase 5: Scaling & Enhancements

  1. Performance Tuning

    • Optimize APIs and data pipelines for faster query performance and real-time responsiveness.
    • Handle scaling issues by leveraging cloud auto-scaling and load balancing.

  2. Additional Features

    • Incorporate machine learning models for predictive analytics (price trends, market movements).
    • Enable social sentiment analysis for crypto discussions across platforms (Reddit, Twitter).
    • Add trading bots or algorithmic trading features for automated strategies.

  3. Expand Protocol Support

    • Integrate additional blockchains based on demand (e.g., Polkadot, Avalanche).

  4. Security Audits

    • Regularly conduct security audits, especially for blockchain integration, to ensure data integrity and user trust.

Timeline

Technology Stack

By following this plan, the data platform will provide traders with comprehensive, reliable, and real-time insights to make informed decisions in the volatile blockchain space.

tusharchou commented 4 hours ago

@mrutunjay-kinagi thoughts?