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Data Architecture Services

Data Architecture Services

Feeling Snowed Under Data Warehouse, Data Lake, Data Lakehouse, Data Mesh, Data Fabric? We Clear Path Above, Beneath and Ahead.

In the world of data, the art of simplification always wins. Because, a wise man once said, data storage is like a garbage truck. You would better not unpack and know each bin bag before loading it. Rather, focus on making it reach the right place, the right destination, and the right pipeline with pre-defined SOPs, and infrastructure. At Azilen, we specialize in simplifying data architecture. Our expert data architecture services provide a clear direction forward, beneath, and upward. Whether it’s strategizing, implementing new solutions, or optimizing existing systems, we ensure your data ecosystem becomes a valuable asset.

Azilen's Modern Data Architecture Services.

Data Architecture Modernization and Transformation
Transforming data architecture into a modern powerhouse.
Data Architecture Automation and Orchestration
Experience unparalleled efficiency and agility.
Data Architecture Cost Optimization and Management
Data Architecture Optimization and Performance Tuning
Data Architecture Health Checks and Audits

1. Data Architecture Modernization and Transformation

Data Fabric Implementation

Hyper-converged Data Platforms

Blockchain-based Data Architectures

Cloud-native Data Architecture Design

2. Data Architecture Automation and Orchestration

Cognitive Workflow Automation

Predictive Auto-scaling Models

Adaptive Infrastructure Orchestration

Self-Healing Data Systems

3. Data Architecture Cost Optimization and Management

Advanced Cloud Cost Modeling

Data Lifecycle Cost Optimization

Cost Reduction AI Algorithms

Real-time Cost Forecasting

4. Data Architecture Optimization and Performance Tuning

Real-time Workload Balancing

Automated Memory Management

GPU-accelerated Processing

Dynamic Data Cache Optimization

5. Data Architecture Health Checks and Audits

AI-driven Data Quality Assurance

Continuous Performance Health Monitoring

Cognitive Data Lineage Tracking

Automated Anomaly Detection

Different Data Architectures We Work With.

Data Mesh Architecture 
Data mesh architecture decentralizes data ownership and management by treating data as a product. It involves organizing data into domain-specific data products, each managed by a dedicated team. This approach enables scalability, and agility.
Data Fabric Architecture
Data fabric architecture provides a unified and consistent data layer that spans distributed data sources and environments. It enables seamless data integration, access, and governance across different environments.
Blockchain-based Architecture
Blockchain-based architecture leverages distributed ledger technology to create transparent, tamper-proof, and decentralized data systems. It ensures data integrity, immutability, and trustworthiness.
Graph-based Architecture
Graph-based architecture models data as interconnected nodes and edges, allowing for complex relationships and patterns to be represented and analyzed. This approach is well-suited for applications such as social networks.
Federated Architecture
The federated architecture allows disparate data sources to remain independent while providing a unified view for analysis and decision-making. It involves querying data across multiple distributed systems in real-time.

Top Data Architecture Frameworks We Employ for Structured Methodologies, Guidelines, and Best Practices.

TOGAF provides a structured approach to designing, planning, implementing, and governing enterprise architectures, including data architecture. It consists of a set of guidelines, methods, and tools for developing architecture artifacts, such as data models, architecture views, and governance frameworks.

The Zachman Framework organizes enterprise architecture perspectives (What, How, Where, Who, When, and Why) into a matrix structure, facilitating analysis and communication of enterprise artifacts. It helps organizations understand the relationships between different perspectives and develop comprehensive architecture solutions.

DMBOK (The Data Management Body of Knowledge) is a comprehensive guide to data management principles and practices, covering areas such as data governance, data quality, data architecture, and data integration. It provides a common body of knowledge for data management professionals and serves as a reference for best practices in data management.

Harvest Data Architecture Excellence.

Our Strategic Roles as Modern Data Architecture Service Provider That Go Beyond Mere Architecting.

Business-Centric Data
Strategy Formulation
Organizational Change for
Data Transformation
Innovative Data
Management Solutions
Data Architecture
Education and Training
Cross-Functional
Data Collaboration
Complex Data
Problem Solving
Risk Management
in Data Initiatives
Demonstrating Data
Investment Value
Scalable Data
Infrastructure Planning
Data Asset Lifecycle
Optimization
Data Culture
Cultivation
Data Architecture
Performance Benchmarking
Data Architecture Vendor
Evaluation and Selection
Crisis Management
Performance Benchmarking
& Reporting

Our Data Engineering Talent Solution and its Value System.

Our Team
Data
Engineers
Data
Architects
ETL
Developers
Governance
Specialists
Data
Scientists
DevOps
Engineers
Value We Provide
  • Continuous Improvement
  • Dynamic Scalability
  • Enhanced Data Security
  • Data Democratization
  • Contextual Insights
  • Real-time Decision Making
  • Data-driven Culture
  • Operational Resilience

Forging the Path to Innovation With Proven Data Architecture Modelling, Implementation and Testing Process.

1

Planning and
Analysis
  • Conduct Stakeholder Workshops
  • Perform Data Landscape Assessment
  • Define Architectural Principles
  • Develop Business Case & ROI Analysis

2

Design and
Prototyping
  • Architectural Modeling
  • Building Data Catalogs & Metadata Management
  • Designing Scalable Data Processing
  • Prototyping Advanced Data Analytics/Visualization

3

Development and
Implementation
  • Designing Modular and Scalable Components
  • Implementing Robust APIs and Data Interfaces
  • Ensuring Data Security and Compliance
  • Performance Tuning and Optimization

4

Testing and
Optimization
  • Advanced Data Quality Assurance
  • Performance Testing at Scale
  • Implementing A/B Testing for Optimization
  • Advanced Monitoring and Alerting

Frequently Asked Questions (FAQ's)

Still have Questions?

Top FAQs Around Our Modern Data Architecture Service.

Our approach to data architecture projects is collaborative and iterative. We begin by understanding your business goals and requirements, conducting a thorough analysis of your existing data landscape, and designing a scalable and flexible architecture that aligns with your objectives.

We are technology-agnostic and work with a variety of technologies and platforms based on the specific needs of each project. Whether it’s traditional relational databases, NoSQL databases, cloud-based solutions, or emerging technologies, we tailor our approach to best suit your requirements.

Our data architecture designs incorporate multiple layers of security measures, including encryption, access controls, identity management, and auditing capabilities. We follow industry best practices and standards to safeguard data against unauthorized access, breaches, and cyber threats.

We leverage a combination of technologies and techniques, such as Extract, Transform, Load (ETL) processes, data virtualization, and data wrangling tools, to integrate data from diverse sources and formats. Our goal is to create a unified view of data that is consistent, accurate, and accessible for analysis and reporting.

Scalability and performance are critical factors in our data architecture designs. We design systems with scalability in mind, utilizing distributed computing technologies, horizontal scaling, and caching mechanisms to handle growing data volumes and user loads while maintaining optimal performance.

We provide ongoing support and maintenance services to ensure the continued success of our data architecture solutions. This includes monitoring system performance, addressing any issues or vulnerabilities, incorporating new technologies and best practices, and adapting to evolving business needs and data requirements.