Background

In today's digital ecosystem, organizations are generating massive volumes of data from various sources—IoT devices, web services, social media, e-commerce platforms, logs, and sensors. Traditional search mechanisms are inadequate when dealing with petabyte-scale datasets that are often unstructured, distributed, and generated in real-time.

Security analysts, researchers, law enforcement agencies, data scientists, and even journalists often face challenges when they need to search and extract meaningful insights from such vast pools of information.

Problem Statement

Design and develop a scalable, intelligent, and high-performance Big Data Searching Tool that can perform fast and accurate searches across massive datasets in real-time, near-real-time and create relevant analysis reports. The tool should support multiple data types (structured, semi-structured, unstructured), advanced query filters, and AI-powered indexing or relevance ranking.

Key Objectives

• Enable fast full-text and metadata search across large and distributed datasets.
• Support intelligent filtering using AI/ML for semantic search, pattern detection, or anomaly identification.
• Allow real-time ingestion and indexing of streaming data (logs, events, etc.).
• Provide intuitive visualization and reporting of search results.
• Ensure efficient resource utilization (storage, compute) and scalability.

Functional Requirements

• Support for Boolean, Regex, and Natural Language queries.
• API support for data ingestion and search queries.
• User authentication and access control.
• Dashboard for visual analytics.
• Support for distributed processing frameworks (e.g., Hadoop, Spark, Elasticsearch).

Evaluation Criteria

• Accuracy and speed of search results.
• Innovation in indexing or searching methodology.
• Scalability and performance under load.
• UI/UX of search interface and visualization.
• Integration of AI/ML (e.g., semantic search, clustering).
• Relevance and real-world usability.

Suggested Tools/Technologies

Apache Spark, Apache Solr/Elasticsearch, Hadoop HDFS, Kafka, MongoDB, Python, Java, React.js, TensorFlow (optional for AI models)

Bonus Points

• Data deduplication and noise reduction capabilities.
• Real-time alert system for keyword triggers or anomalies.
• Multi-language search or NLP capabilities.

Deliverables

• Working prototype/demo of the tool.
• Documentation (architecture, usage guide, data model).
• Deployment instructions or Docker container (if possible).

Tool Design Guidelines

• Upload files (Database core content): FIR, CAF, CDR, ILD GATEWAY, 1930 TICKET DETAIL, IPDR, IP INFORMATION, GMAIL DATA, ANDROID DEVICE CONFIGURATION, APP DETAILS FROM GOOGLE, FACEBOOK DETAIL, INSTAGRAM, WHATSAPP, MICROSOFT MAIL DETAIL, CEIR PORTAL (EXCEL, PDF, IMAGE FILE, JSON FILE)
• Search criteria: NAME, DOB, AADHAR, PAN, PASSPORT (OR ANY OTHER DOCUMENTS), ADDRESS, PIN CODE, PHOTO, MOBILE NO., IMEI NO., IP ADDRESS, MAIL ID, BANK ACCOUNT NO., IFSC CODE, CELL ID, PHOTO
• OUTPUT: GUI BASED MAPPING OF ALL THE DETAILS WITH METADATA FOR SEARCH VALUE