Tanush Aggarwal
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// PRODUCTION DEPLOYMENTS & ARCHITECTURAL CASE STUDIES · SECTION 04

Flagship Systems & Production Impact

Technical architecture breakdowns of production systems, projects, and repositories this candidate has attached to their dossier.

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SYSTEM 01 // PROJECTRole: FEATURED PROJECT

Hadoop HDFS to Databricks and Snowflake Migration

Moved Hadoop HDFS Spark pipelines to Databricks and Snowflake, integrating SQL Server, Excel, and flat files via Spark JDBC/PySpark, orchestrating ETL workflows using Apache Airflow, and optimising financial transaction joins.

TechnologyHadoop HDFS
StackDatabricks
PlatformSnowflake
Outcomereduced shuffle operations and improved query performance by 30 percentSelf-reported
Metric 02—Pending
Metric 03—Pending
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SYSTEM 02 // PROJECTRole: FEATURED PROJECT

Modular ADF ELT Pipeline

Built a modular ADF ELT pipeline with parent-child orchestration across 10+ datasets.

TechnologyAzure Data Factory
Stack—
Platform—
Outcomecutting pipeline duplication by 40 percent and achieving sub-minute event-driven ingestion across 10 plus datasetsSelf-reported
Metric 02—Pending
Metric 03—Pending
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SYSTEM 03 // PROJECTRole: FEATURED PROJECT

Data Migration from Hadoop to Cloud

Transitioned Spark ETL pipelines from Hadoop HDFS to Databricks and Snowflake, integrating SQL Server, Excel and flat files using Spark JDBC and PySpark transformations. Orchestrated PySpark ETL workflows through Apache Airflow, applying broadcast joins, salting and caching to reduce job execution time by 40%. Optimised financial transaction joins by applying filters and column pruning before the join, reducing the data shuffled and cutting join runtime significantly.

TechnologySpark
StackHadoop HDFS
PlatformDatabricks
Outcomereduce job execution time by 40%Self-reported
Metric 02—Pending
Metric 03—Pending
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SYSTEM 04 // PROJECTRole: FEATURED PROJECT

End-to-End Automated Data Pipeline

Created a modular ADF ELT pipeline with parent-child orchestration, reducing pipeline duplication across multiple environments. Automated event-driven ingestion across 10+ datasets using ADLS Gen2 triggers and Mapping Data Flows, processing new files automatically. Implemented parameterised datasets and linked services, enabling reusable pipeline deployment across 3 environments—Dev, QA and Production.

TechnologyADF
StackADLS Gen2
PlatformMapping Data Flows
Metric 01—Pending
Metric 02—Pending
Metric 03—Pending
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// RECRUITMENT & ADVISORY ENGAGEMENTS

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