This platform is designed for real-time streaming and processing of unstructured data. It incorporates cutting-edge tools and services to manage data ingestion, processing, storage, orchestration, security, CI/CD, monitoring, and logging. The platform is built with scalability, reliability, and security in mind.
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Data Ingestion:
- Apache Kafka: Used for real-time data streaming and ingestion from various unstructured data sources.
- Apache Flume or Logstash: Can be used for collecting, aggregating, and transporting data into Kafka for processing.
- Filebeat: Lightweight shipper for forwarding and centralizing log data.
- Kinesis (Optional): For real-time data ingestion from AWS services.
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Data Processing:
- Apache Spark: Performs real-time data analytics and transformation with Spark Streaming.
- Apache Flink: Stream processing for low-latency, high-throughput event-driven applications.
- Apache Beam: Unified stream and batch processing that can run on Spark or Flink runners.
- TensorFlow / PyTorch: For processing and analyzing unstructured data with machine learning models.
- PrestoDB: Distributed SQL query engine for querying large datasets.
- Apache NiFi: For automating the flow of data between systems.
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Data Storage:
- NoSQL Databases: (e.g., MongoDB, Cassandra, Couchbase) for scalable storage of unstructured data.
- Hadoop HDFS: Distributed file system for large-scale data storage.
- Amazon S3: Cloud object storage for scalable data lake solutions.
- Data Lakes: Can integrate with various tools like Delta Lake, Iceberg, or Apache Hudi for handling structured and unstructured data.
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CI/CD Pipeline:
- Jenkins: Automated build and deployment pipelines for continuous integration and delivery.
- GitLab CI / GitHub Actions: Alternatives for managing CI/CD pipelines.
- Docker: Containerization of microservices and platform components to ensure portability and scalability.
- Kubernetes: Orchestration platform for managing containerized workloads and scaling them.
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Orchestration & Workflow Automation:
- Apache Airflow: For scheduling, managing, and automating workflows (ETL pipelines, batch processing, etc.).
- Dagster: Data orchestrator that can manage the lifecycle of data processing tasks.
- Argo Workflows: Kubernetes-native workflow management for automating tasks across a Kubernetes cluster.
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Monitoring & Logging:
- Prometheus: Real-time monitoring and alerting for the platform components.
- Grafana: Visualization of system metrics, logs, and real-time performance data.
- ELK Stack (Elasticsearch, Logstash, Kibana): Centralized logging and log analysis.
- Datadog: Cloud monitoring platform for tracking the performance and health of infrastructure.
- New Relic: Application performance management and monitoring.
- Sentry: For error tracking and monitoring in real time.
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Security:
- OAuth 2.0 or JWT: Secure authentication and authorization of users and services.
- Kerberos: Authentication system for securing communication between services.
- Vault by HashiCorp: For managing secrets and sensitive information securely.
- Encryption: TLS encryption for data in transit and AES encryption for data at rest.
- Role-Based Access Control (RBAC): Managing user permissions based on their roles.
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Data Quality & Validation:
- Great Expectations: For validating, documenting, and profiling data.
- Deequ: A library for defining "data quality" constraints in Spark.
- DataHub: For data governance, metadata management, and lineage tracking.
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Machine Learning & AI:
- MLflow: For managing the machine learning lifecycle (experiment tracking, model management).
- TensorFlow Serving: For serving machine learning models in production environments.
- Kubeflow: Kubernetes-native solution for deploying, monitoring, and managing ML models at scale.
- PyTorch: For building deep learning models on unstructured data like images and text.
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Data Integration:
- Talend: Data integration and ETL platform.
- Fivetran: Managed ETL service to integrate data sources into data lakes or warehouses.
- Apache Camel: Framework for integrating data from various systems and formats.
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Web & API Layer:
- FastAPI: Web framework for building APIs for the platform with fast performance and easy integration.
- Flask: Lightweight web framework for handling HTTP requests and serving data.
- GraphQL: API query language for flexible, real-time data fetching.
- RESTful APIs: Standard API endpoints for interacting with the platform services.
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Containerization & Orchestration:
- Docker Compose: To define and run multi-container Docker applications.
- Kubernetes: For deploying, scaling, and managing containerized applications in a clustered environment.
- Helm: For Kubernetes package management to deploy applications with ease.
- Real-Time Data Processing: Process and analyze unstructured data in real time using stream processing technologies like Kafka, Spark, and Flink.
- Scalable Architecture: Horizontal scalability of Kafka, Spark, and other components for handling massive data volumes.
- Automated CI/CD Pipeline: Jenkins, Docker, and Kubernetes ensure a seamless build, test, and deployment process.
- Data Security: Implements encryption, authentication, and access control using OAuth, Kerberos, and Vault.
- Advanced Orchestration: Use Apache Airflow, Dagster, and Argo for managing complex workflows and data pipelines.
- Comprehensive Monitoring: Use Prometheus, Grafana, and ELK Stack for real-time monitoring and log analysis.
- Data Quality: Ensures data validation and quality using tools like Great Expectations and Deequ.
- Machine Learning Integration: Easy integration with machine learning platforms like TensorFlow and PyTorch for processing unstructured data.
- The platform is in active development with key features like data ingestion, real-time processing, CI/CD pipeline, and security already implemented.
- Ongoing work involves scaling components, improving security measures, integrating more data sources, and enhancing orchestration capabilities.