Solution Overview
Businesses need to explore diverse channels to adapt to market changes. Flexible operational planning is crucial for staying updated on market trends. Crafting corporate strategies challenges decision-makers, but making the right choices boosts the company’s competitive edge.
Due to increasing IT costs, growing data volumes, and competitive challenges, we require new approaches to analyze data effectively. These factors are fueling innovation in database technology and introducing new data extraction techniques. Data warehouses have emerged to integrate internal and external company data, offering decision-makers comprehensive information to support strategic decision-making.
Features of a Data Warehouse
- Optimized Cost, Performance, and Flexibility
Use flexible data models and storage, advanced machine learning, and user-friendly data analysis and visualization tools to implement enterprise data analysis practices.
- Accelerate data-driven innovation
Utilize standard toolsets and multiple languages such as SQL, Python, Ruby, and Java to enable efficient deep analysis. Combine analysis of large datasets with evolving data analytics applications to accelerate data-driven innovation.
- Perform essential business analysis tasks
Use advanced encryption, authentication, high availability, and redundancy features for efficient business analysis. Follow strict security and regulatory policies and Service Level Agreements (SLAs).
- Large-scale parallel MPP and share nothing databases
- An share noting architecture for executing data parallel processing and queries.
- PB-level parallel loads based on MPP Scatter/Gather Streaming™ technology.
- Cutting-edge database compression technology.
- Flexible storage and analysis
- The cost-based SQL query optimizer improves technical performance and executes complex join operations.
- Polymorphic Data Storage™ technology optimizes performance and storage efficiency.
- Partition tables at multiple levels with flexibility.
- Advanced Analytics Platform
- Optimized for high-capacity batch processing, low-latency interactive tasks, and high-throughput small-batch processing.
- Complex join operations in SQL implementation.
- Advanced analysis within large-scale parallel databases.
- Scalable framework for custom analytics and database functions.
- Robustness for enterprises
- Supports business continuity features such as high availability, fault detection, online recovery, backups, and disaster recovery.
- Security certification features help enterprises meet policy and regulatory requirements.
- Seamless integration
- Integrate with different Hadoop environments.
- Use OLAP extension software for comprehensive SQL support.
- Integrate with in-memory data grids and object storage for post-processing structured data.
- Top-tier Data Management Framework
- Adding servers while keeping the database and business operational.
- Performance monitoring frameworks separate hardware and software issues.
The process of building a data warehouse.
Data collection
The data warehouse collects data from daily operational databases such as ERP, CRM, MES, EDA, and other sources.
Consolidating data
Organize data through sets, tables, dimensions, values, and records for easier consolidation.
Data integration
Integrate and store diverse data sources with consistent naming conventions, units of measurement, and unified formats for future user access.
Loading and updating data
Schedule tasks to collect, clean, transform, load, and update data to meet integration goals. Monitor and manage the data warehouse to maintain integrity. Share data with other units through a data mart.