How I Achieved Scalability and Performance: Step-by-Step????

1. Modular Design
Method: Broke the ETL into logical modules:
Ingestion Layer: Raw data from S3 or APIs
Transformation Layer: Cleansing, enrichment, joining
Load Layer: Write to Redshift, S3
Validation Layer: Perform data quality checks after load

Advantages:
Simple to test or rerun one module
Exchange transformation logic without laying hands on ingestion.

 2. Parallel Processing with Spark on AWS Glue
Example: Utilized AWS Glue (Apache Spark) to process ~200M transactions per day.
# Spark parallel processing
df = spark.read.json(“s3://banking-data/raw/”)
df_filtered = df.filter(df.amount > 1000)
df_result = df_filtered.groupBy(“customer_id”).agg(sum(“amount”))
df_result.write.parquet(“s3://banking-data/cleaned/”)

Outcome:
Sliced ETL job time by 4 hours to less than 40 minutes.
Glue dynamically scaled to accommodate volume spikes.

 3. Data Partitioning
Method: Partitioned data by txn_date and region_code.
Example:
df.write.partitionBy(“txn_date”, “region_code”).parquet(“s3://cleaned/”)

Benefit:
Allowed Spark to read only applicable partitions, saving I/O.
Boosted query performance in Redshift and Athena by 80%

 4. Incremental Loading
Method: Utilized a last_updated_timestamp field and kept high-water mark stored.
Implementation:
SELECT * FROM source WHERE updated_at > ‘last_run_time’

Outcome:
Decreased daily load size from full dump (100M+) to only delta (~2M).
Saved on compute cost and run time.

 5. Strong Data Quality Checks
Checks:
Row count equality between two sources
Null checks in primary keys
Business rule checks

Automation:
Utilized custom validation scripts + CloudWatch metrics.
Notified via SNS when mismatches were found.

 6. Elastic Cloud Resources
Cloud Strategy:
Utilized AWS Glue with on-demand workers (serverless Spark).
Redshift Spectrum for direct querying from S3.
Utilized auto-scaling to support peak times.

Result:
Zero downtime at peak traffic
Cost-efficient at off-peak

 7. SQL Query Optimization
Example:
Applied DISTKEY and SORTKEY in Redshift for frequent joins
Avoided SELECT * and applied projection pruning
Utilized materialized views for heavy-hitting aggregates

Result:
Reduced dashboard load time by 60%
 

8. Automation and Orchestration
Tool: Utilized Apache Airflow (or in-house tool, if available) for orchestration.
DAG Example:
[Extract → Transform → Load → Validate → Notify]
Scheduled pipelines every hour
Retry policies, SLAs, and failure alerts integrated

9. Monitoring and Alerting
Monitoring Stack:
AWS CloudWatch for job metrics
SNS notifications for failures
Custom logs for every ETL stage stored in S3

Result:
Decreased time to detect & repair pipeline issues

10. Data Lineage and Impact Analysis
Tool Used: AWS Glue Data Catalog + internal metadata tracker

Example:
Tagged columns and datasets
Mapped lineage from raw → staging → production
Prior to schema change, performed impact analysis on dependent reports