Modern Centralized Data Repository

Comprehensive Data Repository Solutions

Building modern, scalable data repositories with enterprise-grade capabilities

Azure Data Lake Architecture

Expert design and implementation of Azure Data Lake Storage Gen2 with hierarchical namespace, fine-grained access control, and optimized performance for big data analytics.

• Hierarchical namespace design
• POSIX-compliant access control
• Hot, cool, and archive tiers
• Integration with Azure services

Delta Lake Implementation

Implementation of Delta Lake for ACID transactions, schema enforcement, and time travel capabilities, providing reliability and performance for data lakes.

• ACID transaction support
• Schema evolution and enforcement
• Time travel and versioning
• Optimized query performance

Apache Iceberg Integration

Advanced table format implementation with Apache Iceberg for efficient data management, schema evolution, and partition pruning in large-scale analytics.

• Hidden partitioning
• Schema evolution without downtime
• Snapshot isolation
• Time travel queries

Technology Stack

Leveraging cutting-edge technologies for optimal performance

Apache Spark

Unified analytics engine for large-scale data processing with built-in modules for streaming, SQL, machine learning and graph processing.

Trino (PrestoSQL)

Fast distributed SQL query engine for running interactive analytic queries against data sources of all sizes.

Apache Flink

Stream processing framework for high-throughput, low-latency, and exactly-once stream processing applications.

Parquet Format

Columnar storage file format optimized for use with big data processing frameworks and analytics workloads.

Azure Databricks

Unified analytics platform that combines data engineering, data science, and business analytics capabilities.

Unity Catalog

Unified governance solution for data and AI assets across multiple clouds and platforms.

Implementation Process

Systematic approach to building your data repository

1. Assessment & Design

Comprehensive analysis of data sources, volume, velocity, and variety requirements. Design scalable architecture with proper data modeling and governance frameworks.

2. Infrastructure Setup

Provision Azure Data Lake Storage, configure security policies, set up network access controls, and establish monitoring and alerting systems.

3. Data Ingestion & Processing

Implement data pipelines using Apache Spark and Flink for real-time and batch processing. Configure Delta Lake tables with optimized partitioning strategies.

4. Query & Analytics Layer

Deploy Trino clusters for interactive queries, configure Databricks workspaces, and implement machine learning integration with MLflow and AutoML capabilities.

Key Features & Benefits

Auto-Scaling Capabilities

Dynamic resource allocation based on workload demands with automatic scaling policies and cost optimization features.

Concurrent Request Handling

Optimized for handling thousands of concurrent queries with load balancing and query optimization techniques.

Machine Learning Integration

Native integration with ML frameworks including Spark MLlib, AutoML, and custom model deployment capabilities.

Data Governance

Comprehensive data lineage tracking, quality monitoring, and compliance management with Unity Catalog integration.

Security & Compliance

Enterprise-grade security with encryption at rest and in transit, RBAC, and compliance with GDPR, HIPAA, and SOX requirements.

Performance Optimization

Advanced optimization techniques including data partitioning, indexing, caching, and query acceleration for sub-second response times.