Migrating Hadoop and HANA to Snowflake for a Manufacturing Company

Background

A global manufacturing company aimed to modernize its data infrastructure to improve analytics performance, reduce costs, and streamline operations. The company had been using Hadoop for big data storage and SAP HANA for in-memory analytics. While effective for their time, these systems posed challenges such as high operational costs, scalability limitations, and complex maintenance. To address these challenges, the company decided to migrate its data and workloads to Snowflake, a cloud-based data platform known for its scalability, performance, and ease of use.

Objectives

  • Consolidate data into a single, unified platform.
  • Improve data access and analytics performance.
  • Enable scalability and flexibility for future growth.
  • Reduce infrastructure and maintenance costs.

Challenges

  • Migrating large volumes of data without disrupting ongoing operations.
  • Ensuring compatibility between the different architectures of Hadoop, HANA, and Snowflake.
  • Minimizing downtime during the migration process.
  • Optimizing the data models and queries for Snowflake's cloud-native architecture.

Approach

Assessment and Planning
  • Conducted a detailed assessment of the existing Hadoop and HANA environments.
  • Identified key data sources, workflows, and analytics processes to migrate.
  • Developed a phased migration strategy to minimize downtime.
Data Extraction and Transformation
  • Extracted data from Hadoop and HANA, ensuring data integrity throughout the process.
  • Transformed and optimized the data for Snowflake's architecture using ETL (Extract, Transform, Load) tools.
Migration to Snowflake
  • Leveraged Snowflake's capabilities, such as its virtual warehouses, to handle parallel processing during migration.
  • Migrated data incrementally, starting with non-critical datasets, followed by mission-critical workloads.
Validation and Optimization
  • Validated the migrated data for accuracy and completeness.
  • Optimized queries, data models, and schemas to maximize Snowflake's performance.
  • Conducted performance testing and resolved any bottlenecks.
Training and Adoption
  • Provided training to the company's data and analytics teams to ensure smooth adoption of Snowflake.
  • Developed documentation and best practices for managing the new platform.

Results

  • Improved Performance: Analytics queries that previously took hours to run on HANA now completed within minutes on Snowflake.
  • Cost Savings: Reduced infrastructure and maintenance costs by consolidating data into a single cloud-based platform.
  • Scalability: Enabled the company to scale its data processing capabilities based on demand without significant capital expenditure.
  • Enhanced Data Access: Simplified data access and sharing for business users and analytics teams, fostering a data-driven culture.

Conclusion

Enhanced Data Access: Simplified data access and sharing for business users and analytics teams, fostering a data-driven culture.

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