Description
Practical Use Case and User Story
As a data analyst, I need an Amazon QuickSight dashboard that integrates historical transaction data from Azure SQL Database and Azure Data Lake Storage. The dashboard should leverage time series forecasting models from Azure Machine Learning, such as Prophet, ARIMA, and tree-based models, to provide insights on demand fluctuations, SKU-level forecasts, and store performance. Materialized Views and Azure Data Factory ensure efficient querying and daily data updates. Interactive filters should allow users to explore forecasted demand by region, store, or product category. This will help in accurately predicting sales and managing stock levels effectively.
Tech Stack Involved
Data Collection & Integration
- APIs: REST, GraphQL (to collect data from external sources)
- Data Connectors: AWS Glue, Talend, Stitch (for integrating multiple data sources)
- Data Streams: Apache Kafka, AWS Kinesis (for real-time data streams)
- ETL/ELT
- ETL Tools: Apache Airflow, dbt (data transformations in the cloud)
- Cloud ETL Services: AWS Glue, Azure Data Factory (for scalable ETL pipelines)
- Data Processing: AWS Lambda (for event-driven data processing)
Databases & Data Storage
- Relational Databases: PostgreSQL, MySQL (for structured data storage)
- Data Warehousing: Amazon Redshift, Snowflake (for centralized data storage and fast queries)
- NoSQL Databases: DynamoDB, MongoDB (for unstructured or semi-structured data)
- Cloud Storage: Amazon S3, Azure Blob Storage (for storing large datasets or flat files)
Data Analytics & Visualization
Business Intelligence (BI) Tools:
- Amazon QuickSight: Scalable cloud-native BI service
- Microsoft Power BI: Comprehensive analytics and interactive dashboards
- Tableau: Popular for creating highly visual dashboards
- Google Data Studio: Free and integrated with Google services for basic dashboards
- Data Querying: SQL, PostgreSQL (for querying data for dashboarding tools)
Data Preparation & Transformation
- Data Wrangling Tools: Pandas, PySpark (for handling complex data transformations before visualization)
- Data Cleansing: Trifacta, OpenRefine (for preparing clean datasets for dashboarding)
Cloud Infrastructure
- Cloud Compute: AWS EC2, Azure VMs (for hosting dashboards or running backend services)
- Containerization: Docker (for packaging and deploying dashboard applications)
- Serverless Options: AWS Lambda, Azure Functions (for lightweight, event-driven tasks)
Collaboration & Version Control
- Version Control: GitHub, GitLab (to track dashboard development)
- CI/CD: Jenkins, GitLab CI (to automate the deployment of dashboards)