ENTERPRISE DATA WAREHOUSE

Your Performance Management Core

Store, transform, and extract your data from a single interface across all sources.

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WHAT WE DO

Enterprise Data Warehouse Service Offerings

Experience a complete performance management system with our robust enterprise data warehouse solutions

Data Integration

Utilize ETL processes to extract, transform, and load data from various sources, including transactional systems, external databases, and other data repositories, to enrich the data and provide a more comprehensive picture for analysis and decision-making.

Data Modeling

Assists in designing and implementing data models based on Data Vault, Star or Snowflakes Schema or that support the organization’s reporting and analytics requirements. This involves creating data schemas and dimensional models and defining relationships between data entities for optimal query performance and analysis.

Querying and Analysis

Enable organizations to run SQL queries, generate reports, create dashboards, and explore data using BI tools or data visualization platforms to perform complex queries, ad-hoc analysis, and reporting on their EDW data.

Scalability and Performance

Design an Enterprise Data Warehouse (EDW) to handle large-scale data volumes and accommodate growth over time. Employ optimization techniques like data partitioning, indexing, and parallel processing to ensure fast and efficient data retrieval and analysis.

Data Lifecycle Management

Implement performance optimization techniques, such as partitioning, indexing, and caching, to enhance data lake query performance and reduce latency.

Deep Learning and Artificial Intelligence

Manage the data lifecycle, including archiving, purging, and retention policies to ensure long-term data preservation, data relevancy, and up-to-date data alignment with business requirements.

Data Storage

Provides a centralized and scalable storage infrastructure for storing structured, semi-structured, and unstructured data. It ensures data integrity, consistency, and security while providing efficient storage mechanisms to handle data varieties and volumes.

Data Quality and Governance

Apply cleansing, validation, and enrichment processes to eliminate inconsistencies, inaccuracies, and duplicate records, as well as data governance practices to establish standards, policies, and management controls.

Data Security and Access Control

Security measures implementation to protect sensitive data from unauthorized access at different levels. These measures include role-based access controls, data masking, and encryption techniques.

Metadata Management

Provide context for data discovery, data lineage, and impact analysis by capturing and managing metadata, which includes information about the structure, attributes, and relationships between the data.

Data Migration and Upgrades

Migrate the data from legacy systems or previous versions of the data warehouse to new platforms or upgraded versions. It includes data mapping, data validation, and ensuring a smooth transition without data loss or disruption.