Pablo Andres <br> Spring Flores, M.S.

Pablo Andres
Spring Flores, M.S.

Senior Data Analyst
Data Scientist
10 Years of Experience
(3 Years in the USA)

Strategic Migration: Transitioning Reporting Pipelines from Databricks to AWS at H1 Insight Inc

Company Name: H1 Insight Inc
Duration: Oct 2023 - March 2024

Project Keywords | cloud computing | data migration | ETL (Extract, Transform, Load) | data pipeline | data lake | big data | data warehouse | data governance | data integration | data analytics | serverless architecture | CI/CD (Continuous Integration/Continuous Deployment) | data quality | cost optimization | scalability | efficiency | Jira | data security | compliance | AWS EMR Studio | PySpark | Python | AWS Athena | S3 | serverless computing | AWS Lambda | cost-benefit analyses | data validation | stakeholder engagement | job scheduling | workflow orchestration | AWS Glue | cloud-native services | data engineering |

Introduction

As the Technical Lead and Expert at H1 Insight Inc, I spearheaded a critical project aimed at migrating and optimizing 100 QA reporting pipelines from Databricks to AWS, employing Python and PySpark. This initiative was designed to elevate data processing efficiency, scalability, and achieve considerable cost savings by harnessing AWS’s cloud computing capabilities.

Objectives

  • Migration and Scalability: Directed the migration of QA report pipelines to AWS EMR Studio, AWS Athena, and S3, focusing on enhancing system efficiency and scalability. This involved the strategic use of ETL processes and data pipeline optimizations to facilitate data integration and management in a cloud environment.
  • Performance Optimization: Led the conversion from Pandas to PySpark, aiming to improve the performance of our reporting pipelines. This transition was key to handling big data analytics, leveraging serverless architecture for scalable data processing.
  • Cost-Effectiveness: Managed to achieve notable cost optimization through streamlined data processing and management. Conducted thorough cost-benefit analyses to ensure the project’s financial viability while maintaining data quality and compliance.

Challenges

  • Conducting comprehensive cost-benefit analyses to identify efficient migration strategies that align with our financial goals and technical requirements.
  • Ensuring robust data governance and security compliance throughout the migration process, adhering to industry standards and regulations.
  • Coordinating with stakeholders to align the migration project with broader organizational objectives, facilitating effective communication and collaboration.
  • Overcoming scalability and performance improvement challenges due to the architectural differences between Databricks and AWS, requiring adjustments in data pipeline design and execution.

Approach and Implementation

  • Strategic Planning: Employed a phased development approach, emphasizing meticulous planning and stakeholder engagement to navigate the complexities of cloud migration.
  • Technology Selection: Utilized AWS EMR Studio, PySpark, Python, AWS Athena, and S3, incorporating serverless computing elements like AWS Lambda to enhance our data processing capabilities. This selection was critical for establishing a scalable and efficient CI/CD pipeline for continuous integration and deployment of data analytics workflows.
  • Leadership in Implementation: Oversaw the technical transition, developing new data pipelines and adapting existing logic to improve data analysis and reporting. This effort included implementing advanced data validation techniques to ensure high data quality and integrity.

Detailed Overview of Migrated Services

  • Migrated interactive data processing workflows to AWS EMR Studio, optimizing them for sophisticated data analytics tasks. This move was instrumental in leveraging cloud-native services for enhanced data engineering and analytics capabilities.
  • Transitioned data storage and lake management to AWS Lake Formation and S3, facilitating better data integration and scalability of our data lake infrastructure.
  • Replaced Databricks Scheduler with AWS Glue and Lambda, adopting a more flexible approach to job scheduling and workflow orchestration, critical for managing ETL tasks and serverless computing workloads efficiently.
  • Shifted SQL-based analytics to AWS Athena, taking advantage of its serverless query service for improved accessibility and analysis of data stored in Amazon S3, thereby enhancing our data warehouse capabilities.

Results and Impact

  • Secured a 50% increase in data throughput and a 30% improvement in computational efficiency, demonstrating the project’s success in enhancing our data processing infrastructure.
  • Enhanced data validation efforts led to a significant reduction in data anomalies, underscoring our commitment to maintaining high data quality standards.
  • Fostered improved collaboration among the data engineering and analytics teams, boosting overall productivity and team dynamics.
  • Achieved a 30% reduction in operational costs, evidencing the financial benefits of the migration and optimization efforts.

Lessons Learned

The project underscored the critical importance of thorough planning, effective stakeholder engagement, and the strategic use of new technologies for improving data processing scalability and efficiency. It highlighted how effective team management and collaboration are essential in overcoming the technical and organizational challenges associated with large-scale data migration projects.

General Note

The engineering team’s foundational support in infrastructure setup and provisioning was invaluable, allowing for a focus on strategic and technical leadership to ensure the project’s success. This collaboration was pivotal in navigating the complexities of cloud migration and optimizing our data analytics capabilities.

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