According to a report from Forrester in 2023, majority of organizations will adopt a centralized data warehouse by 2025, to gain better insights into business operations.
The client had an initial concept of a centralized repository to reduce administrative overhead with data processing capabilities through seamless data analytics and reporting.
A cloud-based data warehouse implemented with data engineering service to manage and analyze large volumes of data to improve productivity within the data ecosystem.
Big Data Analytics & Reporting
Data Aggregation & Movement
Data-Driven Decision Making
Data Quality with Consistency
Automated QA Testing Framework
Data Analytics & Predictive Modelling
The client is a well-known car sharing company with convenient and affordable car services.
A centralized data warehouse with QA engineering framework was implemented with real-time data streaming capabilities through big data analytics & visualization.
Decision Making with Predictive Analytics
Enhanced Car Availability for Allocation
Better Accuracy in Demand Forecasting
A robust cloud-based data warehouse that reduces current & historical data redundancy and provides data normalization with historical insights for business analytics.
Below is the solution overview diagram of the centralized data warehouse:
Following are the key stages that are involved in implementation of data warehouse:
➡️ Accumulation of data:
The initial stage in implementation of data warehouse is to identify & collect data from various data sources and remove errors & inconsistencies.
➡️ Azure Data Factory – Extract, Load & Transform:
The data will be extracted from source system then load into data warehouse & aggregate for the purpose of analysis.
➡️ Snowflake –Centralized Data Warehouse:
A cloud-based data warehouse that accumulate large volume of data at a centralized place to integrate & analyse data with scalable & secure data warehouse.
➡️ Azure Analysis Services – Data Modeling:
The azure analytics services combine data from various sources & define metrics in single data model for analysis.
➡️ Power BI: Data Analysis & Reporting:
Power BI enables data-driven decision making with data analysis & reporting with a centralized & secure data-ecosystem.
“A centralized data warehouse can help businesses to gain a competitive advantage by providing them with a single source of truth for all data.”
– James Goodnight, CEO, SAS
The implementation of a centralized data warehouse leads to the following data driven significance through real-time analytics & data visualization:
The big data analytics & reporting through centralized data warehouse is one of the key modules of solution, the holistic solution implementation also involves the QA testing automation, Let’s discover some key highlights of the QA Test Automation Framework.
A comprehensive QA engineering framework was designed to incorporate test automation with a holistic set-up of benchmarks to improve system assessments & mitigate biases in an efficient way to sync Development teams and QA Teams.
The QA engineering team overcame the sprint cycle challenges of automation regression by using a CI/CD pipeline to automatically deploy the test automation code to the production environment.
A robust reservation system was implemented to track the progress of the QA cycle with effective stockholders’ management to ensure testing is aligned with the needs of business.
The development team has primarily focused on the core products, whereas we manage the QA test automation with the purpose of no quality biases.
