VedRock Consulting is here to help you to create a data architecture that simplifies complexity and amplifies data-driven insight leading you to smarter decisions and more efficient systems.
How can we help?
Data architecture is all about designing your data future. Here are a few examples!
Assessment and Strategy Development
- Conduct an assessment of the current data landscape, including inventorying data assets, dependencies, and existing systems.
- Collaborate with business stakeholders to define data architecture goals and align them with organizational objectives.
- Perform a gap analysis to identify current limitations and recommend solutions for scalability, performance, and data integration.
- Develop implementation roadmaps.
Data Architecture Design
- Design architecture blueprint with a layered structure, covering data ingestion, storage, transformation, and analytics.
- Define standards for data storage, including object storage for raw data, relational databases for processed data, and NoSQL solutions for low-latency use cases.
- Propose solutions for secure and compliant data handling, including encryption, access control, and adherence to industry regulations.
Data Governance and Data Quality Framework
- Create data ownership models to assign responsibility for data stewardship.
- Develop a data catalog and maintain a centralized metadata repository to ensure data is easily understood and accessible.
- Define policies and processes for ensuring data quality, security, and compliance.
- Define data quality standards, metrics, and processes to monitor, evaluate, and improve data reliability.
- Decide how access control mechanisms to protect sensitive information will look like.
Storage Layer Layout
- Define the architecture for data storage to support the business's data needs and analytics requirements efficiently considering how to store raw data, processed data, and analytics data.
- Decide how to differentiate between hot storage (e.g., Azure SQL Managed Instance for frequently accessed data) and cold storage (e.g., Azure Data Lake or Blob Archive) to balance cost and performance.
- Desing long-term data archiving strategy using Azure Blob Storage cool or archive tiers to store infrequently accessed historical data at a reduced cost.
Data Integration and Transformation Map
- Architect the flow of data across systems: source systems, ETL/ELT pipelines, storage layers, and analytics environments.
- Design ETL/ELT processes to transform, standardize, and enrich data, making it analysis ready.
- Establish master data management (MDM) solutions to unify and deduplicate records across the organization.
Data Modeling and Schema Blueprint
- Develop conceptual, logical, and physical data models.
- Decide on data models for analytics and reporting systems.
- Plan for flexible schema designs, including schema evolution for semi-structured and unstructured data.
Data Security and Access Foundation
- Design a security model and data access control mechanism to govern and control data access.
- For sensitive data create the encryption approach (at rest, in transit, or double encryption).
- When appropriate, design distinct access policies for separate zones (e.g., raw, staging, and curated zones in a data lake).
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