- OLTP database - MySQL
- NoSql database - MongoDB
- Staging Data warehouse – PostgreSQL
- Big data platform - Hadoop
- Big data analytics platform – Spark
- Business Intelligence Dashboard - IBM Cognos Analytics and Tableau
- Data Pipelines - Apache Airflow
- OLTP
- We import the data in MySQL Database
- The loaded data along with sql query is retrieved and stored as a 'sql' file.
- NoSQL-MongoDB
- The data source is a .json file.
- The first step is connecting database and making a collection named 'electronics'
-This is done after establishing the connection with MongoAtlas and then running the db-conn.js file using mongo shell:
load('path/to/db-conn.js)
- The next step is to load the data in the collection and save a copy of slected fields using import-sxport.sh file:
./import-export.sh
- Finally we explore the loaded data using mongodb-queries.js file using mongo shell:
load('mongodb-queries.js)
- DataWarehousing-PostgreSQL
- This is for educational purposes only as a Data Warehouse requires reference data source and metadata.
- Dashboards-BI
- Data Visualization for Understanding:
- Dashboards present complex data in visually intuitive formats.
- Visualization enhances comprehension, making data accessible to diverse stakeholders.
- Real-Time Monitoring and Proactive Analysis:
- Dashboards offer real-time updates on key metrics for prompt decision-making.
- BI tools enable proactive problem identification by highlighting deviations and trends.
- Informed Decision-Making with Actionable Insights:
- Dashboards provide a centralized source for actionable insights.
- BI facilitates data-driven decisions, fostering accountability and optimizing processes.
- Data Visualization for Understanding:
- ETL
- ETL serves as the backbone for integrating diverse data sources, ensuring a unified and consistent view across the organization.
- ETL enhances operational efficiency, facilitating timely and accurate data delivery to support informed decision-making in business processes.