Modern data warehouses efficiently meet the scalability, flexibility, and analytical requirements of contemporary business enterprises and enable them to gain deeper insights through historical and real-time analysis of data. The modernization of the data warehousing infrastructure also empowers organizations to get a holistic view of their enterprise data to support global decision-making and drive their business intelligence (BI) and analytics initiatives collaboratively.
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Challenges with Traditional On-premises Data Warehouses
- Traditional data warehouses that are built on-premises present scalability challenges and restrict business evolution.
- The high infrastructure management costs and expensive upgrades associated with on-premises data warehouses impact the overall modernization goals of a company.
- Data silos in traditional data warehouses limit cross-functional collaboration, critical for holistic decision-making.
- Data governance challenges pose data security and regulatory compliance risks, vital in the modern business environment.
- Meeting the demand for real-time data analysis becomes a struggle with traditional data systems, impacting decision-making for operational excellence, customer experience, and overall business growth.
Features and Benefits of a Modern Data Warehouse
- Handling Large Data Volumes
- Supporting Diverse Data Sources and Formats
- Scalability and Flexibility
- Fast Data Processing
- Data Virtualization
- Data Democratization
- Advanced Analytics
Advantages of a Modern Data Warehouse
Building a Flexible Modern Data Warehouse Architecture
- Massively Parallel Processing (MPP) Architecture: In the MPP architecture, also called the ‘’loosely coupled’’ or ‘’shared nothing’’ storage structure, multiple processors, each with its own operating system and memory, collaboratively perform a set of coordinated computations in a program. Speed and scalability are the two key advantages of a modern data warehouse employing an MPP architecture. The Snowflake Data Cloud for instance processes queries using MPP, offering the ease of use of a shared-disk architecture and performance benefits of a shared-nothing architecture.
- Lambda Architecture: The Lambda architecture featuring three layers – the speed or stream layer, the batch layer, and the serving layer – is common in big data applications. The speed layer supports real-time data processing for prompt insights. The batch layer handles raw data storage and processing of large volumes of historical data. The serving layer makes the results of the batch processing layer available for querying. While the Lambda architecture is complex, it offers several benefits such as it requires no management, is highly scalable, and allows businesses to react to situations in real-time.
- Hybrid Architecture: The hybrid architecture combines the best of both on-premises and cloud infrastructure for flexibility, scalability, governance, and optimization of various data processing and analytics tasks to meet specific business requirements.
Migrating to a Modern Data Warehouse: 5 Key Considerations
- 1. Aligning with Business Stakeholders
- 2. Robust Metadata Management
- 3. Choosing the Right Tools and Technologies
- 4. Implementing Data Governance Policies
- 5. Adapting to Changing Business Requirements