Our Work

Case Studies

Project for ETL in Google Cloud Platform

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Numbers Speak For Themselves!

4800 +

Curated Products

Product Categories

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

Background

Our Approach

Data warehouse raw layer

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 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 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