Overview
Over the last decade, organizations have made substantial investments in their data warehouses in terms of infrastructure, technology, and resources. Hadoop offers new possibilities for extending and optimizing these critical infrastructure components.
The Challenge
Companies use hundreds or thousands of ETL jobs to bring together data from different systems or applications. As data volumes grow, these traditional approaches face scalability and performance challenges.
Strategy for Hadoop Integration
When planning to offload ETL processes to Hadoop, ensure you have a well-defined strategy covering:
- Scope of offloading: What parts of your warehouse to migrate
- ETL workflow and technology: Which tools and frameworks to use for ETL processing
- Storage formats: Optimal formats for efficient querying on Hadoop data
- SQL-on-Hadoop solution: Tool selection for querying data stored in Hadoop
- BI tool integration: Strategy for connecting business intelligence tools to Hadoop data
Scalable Hadoop Solutions
The Hadoop ecosystem offers a wide range of platforms that can solve both storage and computational challenges while providing:
- Data consistency: Ensuring consistency of data across the infrastructure
- Flexible schema: Ability to ingest wide variety of data including sensor data, social media, and IoT data
- Scalability: Handling growing volumes of data efficiently
Real-World Applications
Industry leaders like Barclays bank have demonstrated how Hadoop integration transforms data warehouse architectures, enabling better data accessibility and analysis at scale.
Conclusion
Hadoop provides organizations with powerful tools to modernize their data warehouse infrastructure and handle the complexities of big data at enterprise scale.