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Case Studies
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Challenge
Our client is a leading e-commerce solutions provider. They provide all business needs above and beyond mere supplies, IT, furniture, and essential equipment. They serve a wide range of customers, including online sellers, startups, SMEs, corporations, and organizations.
The project emerges from a complex landscape of data management, handling intricate ETL processes and seamless data flow between various sources. With the integration of state-of-the-art technologies and a unified approach to managing different aspects like scheduling, rerunning, logging, and secure data transfer, the project stands as a paradigm of efficiency and adaptability.
Client Profile
Our client is a leading e-commerce solutions provider. They provide all business needs above and beyond mere supplies, IT, furniture, and essential equipment. They serve a wide range of customers, including online sellers, startups, SMEs, corporations, and organizations.
Background
The project emerges from a complex landscape of data management, handling intricate ETL processes and seamless data flow between various sources. With the integration of state-of-the-art technologies and a unified approach to managing different aspects like scheduling, rerunning, logging, and secure data transfer, the project stands as a paradigm of efficiency and adaptability.
Our Approach
Data warehouse raw layer
- Data Ingestion: Handling the collection and ingestion of raw data, including complex nested structures, from numerous sources.
- Data Cleaning: Ensuring a clean and consistent foundation through the removal of inconsistencies and irrelevant information.
Data warehouse intermediate layers
- Data Transformation and Unnesting: Applying transformations to convert raw data into a structured form, including methods to unnest complex data structures, to create structured intermediate data.
- Environment Transition Optimization: Developing strategies for a smooth transition from development to production, focusing on scalability, performance, and consistency in Airflow and GBQ tables.
Scheduling and Task Control
- Task Scheduling and Validation: Implementing customized scheduling, including automated validation between development and production environments, to ensure integrity across the lifecycle.
- Task Rerunning and Failure Handling: Building mechanisms for efficient rerun and handling of failed tasks, with considerations for both environments.
- Real-Time Monitoring and Maintenance: Ensuring long-term system health through real-time monitoring tools and a structured maintenance plan.
Validation and Optimization
- Extensive Validation Checks: Performing rigorous validation between development and production environments, with special attention to GBQ tables and Airflow, ensuring data consistency and integrity.
- Optimization of Views and Queries: Facilitating data accessibility with the creation and optimization of various views in both environments, including adaptive rerunning and tuning mechanisms.
- Ongoing System Optimization: Implementing continuous maintenance and optimization protocols to ensure the efficiency, adaptability, and scalability of the project.
Solution
Data warehouse raw layer
- Data Ingestion: Handling the collection and ingestion of raw data, including complex nested structures, from numerous sources.
- Data Cleaning: Ensuring a clean and consistent foundation through the removal of inconsistencies and irrelevant information.
Data warehouse intermediate layers
- Data Transformation and Unnesting: Applying transformations to convert raw data into a structured form, including methods to unnest complex data structures, to create structured intermediate data.
- Environment Transition Optimization: Developing strategies for a smooth transition from development to production, focusing on scalability, performance, and consistency in Airflow and GBQ tables.
Scheduling and Task Control
- Task Scheduling and Validation: Implementing customized scheduling, including automated validation between development and production environments, to ensure integrity across the lifecycle.
- Task Rerunning and Failure Handling: Building mechanisms for efficient rerun and handling of failed tasks, with considerations for both environments.
- Real-Time Monitoring and Maintenance: Ensuring long-term system health through real-time monitoring tools and a structured maintenance plan.
Validation and Optimization
- Extensive Validation Checks: Performing rigorous validation between development and production environments, with special attention to GBQ tables and Airflow, ensuring data consistency and integrity.
- Optimization of Views and Queries: Facilitating data accessibility with the creation and optimization of various views in both environments, including adaptive rerunning and tuning mechanisms.
- Ongoing System Optimization: Implementing continuous maintenance and optimization protocols to ensure the efficiency, adaptability, and scalability of the project.
Solution
Data warehouse raw layer
- Data Ingestion: Handling the collection and ingestion of raw data, including complex nested structures, from numerous sources.
- Data Cleaning: Ensuring a clean and consistent foundation through the removal of inconsistencies and irrelevant information.
Data warehouse intermediate layers
- Data Transformation and Unnesting: Applying transformations to convert raw data into a structured form, including methods to unnest complex data structures, to create structured intermediate data.
- Environment Transition Optimization: Developing strategies for a smooth transition from development to production, focusing on scalability, performance, and consistency in Airflow and GBQ tables.
Scheduling and Task Control
- Task Scheduling and Validation: Implementing customized scheduling, including automated validation between development and production environments, to ensure integrity across the lifecycle.
- Task Rerunning and Failure Handling: Building mechanisms for efficient rerun and handling of failed tasks, with considerations for both environments.
- Real-Time Monitoring and Maintenance: Ensuring long-term system health through real-time monitoring tools and a structured maintenance plan.
Validation and Optimization
- Extensive Validation Checks: Performing rigorous validation between development and production environments, with special attention to GBQ tables and Airflow, ensuring data consistency and integrity.
- Optimization of Views and Queries: Facilitating data accessibility with the creation and optimization of various views in both environments, including adaptive rerunning and tuning mechanisms.
- Ongoing System Optimization: Implementing continuous maintenance and optimization protocols to ensure the efficiency, adaptability, and scalability of the project.
Challenges & Achievements
Challenges
- Task Failure Handling: Developing fail-safe mechanisms to handle and recover from unexpected task failures.
- Data Source Integration Complexity: Tackling the challenge of integrating over a hundred different data sources, some with complex nested structures.
- Data Validation Between Environments: Ensuring seamless validation between development and production environments, with exact consistency and performance tuning.
- Monitoring and Log Management: Implementing a unified and efficient system to manage logs and monitor the state of various data processes.
- Scalability and Performance Optimization: Crafting a system that could adapt to growing data needs, with a focus on balancing scalability with performance.
Achievements
- Streamlined Rerun and Recovery System: Created an intuitive system for rerunning tasks and recovering from failures, significantly enhancing operational efficiency.
- Effective Integration of Various Data Sources: Achieved seamless integration for all diverse data sources, including complex and nested ones.
- Optimized Scalability and Performance: Designed and implemented a highly scalable architecture, maintaining optimal performance in both development and production environments.
- Robust Data Validation Framework: Established comprehensive validation methods between development and production environments, ensuring data consistency.
- Unified Log Management and Monitoring: Developed a streamlined process for log management and real-time monitoring, contributing to a more transparent and controlled operation.
- Credentials vault: The sensitive credentials are stored securely using Airflow's built-in feature. This safeguards authentication data, like API keys and service account files, ensuring strong data security practices.
Achievements
- Streamlined Rerun and Recovery System: Created an intuitive system for rerunning tasks and recovering from failures, significantly enhancing operational efficiency.
- Effective Integration of Various Data Sources: Achieved seamless integration for all diverse data sources, including complex and nested ones.
- Optimized Scalability and Performance: Designed and implemented a highly scalable architecture, maintaining optimal performance in both development and production environments.
- Robust Data Validation Framework: Established comprehensive validation methods between development and production environments, ensuring data consistency.
- Unified Log Management and Monitoring: Developed a streamlined process for log management and real-time monitoring, contributing to a more transparent and controlled operation.
- Credentials vault: The sensitive credentials are stored securely using Airflow's built-in feature. This safeguards authentication data, like API keys and service account files, ensuring strong data security practices.
Secondary Info
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Let's work together!
Conclusion
Through intelligent integration of Google Firestore and Google BigQuery, we delivered a system that not only meets the client’s current needs but is scalable for future growth. The project demonstrates our expertise in building real-time, data-driven solutions that enhance customer engagement.
Solution
Build a self-service Data Mart to service Standard & Ad-hoc Reporting
Client has been provided with a new level of performance optimization, scalability, and analytical depth by embracing advanced data modeling techniques as below:
- Multiple Transactional Big Data Source, data migrated to Google Cloud
- Dally ETLs developed to transform data on a regular basis
- Large Scale Power BI Data Marts developed for the self service reporting
- Additional standard dashboards developed on Power BI
- Incremental Refresh and Data Partitioning implemented on Power BI data models through Tabular Editor and SQL Server Management Studio
- Alerts and Data Quality Checks set up for proactive monitoring
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Brittany Foxx
Abu Dhabi
Frequently Asked Questions
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