HintsToday
Hints and Answers for Everything
recent posts
- Memory Management in PySpark- CPU Cores, executors, executor memory
- Memory Management in PySpark- Scenario 1, 2
- Develop and maintain CI/CD pipelines using GitHub for automated deployment, version control
- Complete guide to building and managing data workflows in Azure Data Factory (ADF)
- Complete guide to architecting and implementing data governance using Unity Catalog on Databricks
about
Archives: Projects
Conceptual Workflow Code Implementation 1. Sample Metadata Table Here’s how the metadata table might look (e.g., stored in a Hive table or a CSV): step_id step_type code_or_query output_view 1 SQL SELECT category, SUM(sales_amount) AS total_sales FROM sales_data GROUP BY category category_totals 2 SQL SELECT * FROM category_totals WHERE total_sales > 400 filtered_totals 3 DataFrame filtered_df…
ETL Workflow with Enhanced Logging and Snapshot Management Below is a refined ETL architecture and corresponding SQL and Python updates, based on your project structure and requirements. SQL Table Definitions Control Table The control table is used to manage ETL steps, track their sequence, and specify configurations such as write_mode and snapshot_mode. Log Table The…
2. Configuration Files config/base_config.json Database Setup control_table_setup.sql log_table_setup.sql Sample Control Table Data etl/execute_query.py etl/log_status.py etl/stage_runner.py etl/job_orchestrator.py 5. Utility Scripts utils/spark_utils.py utils/query_utils.py utils/error_handling.py scripts/run_etl.py
2. Configuration Files a. base_config.json b. pl_cibil750.json 3. Control Table a. control_table_setup.sql The control table includes the write_mode column to specify the operation type (temp_view, table, append, snapshot). This ensures flexibility in defining the desired operation. Program Name Stage Name Step Name Operation Type Query Temp View Name Table Name Write Mode CIBIL 750 Program CIBIL Filter Read CIBIL Data SQL SELECT *…
1. Project Structure 2. Configuration Files a. base_config.json b. pl_cibil750.json 3. Control Table a. control_table_setup.sql b. sample_control_table_data.sql 4. Scripts a. run_etl.sh b. run_etl.py c. etl/execute_query.py d. etl/log_status.py e. etl/stage_runner.py f. etl/job_orchestrator.py 5. Utilities a. utils/spark_utils.py b. utils/config_utils.py Missing Utility Files a. data_utils.py b. config_utils.py c. error_handling.py 2. Updated Control Table Data Control Table SQL 3.…
Configuration Files base_config.json (Static Environment Configurations) json product_configs/pl_cibil750.json (Individual Configs for Each Program) json Sample Control Table Data Load sample data into the control table. sql Dynamic Stage Execution Script (etl/execute_stage.py) This script will handle both creating temp views or writing to tables, and will manage final table writes in snapshot mode. python