Planning-Inspectorate/odw-synapse-workspace
Jul 2025 – Jan 2026
6 Months active
Languages Used
PythonSQLYAMLMarkdownMermaidJSONjsonpython
Technical Skills
Azure SynapseCloud Computing (Azure Synapse)Data EngineeringData ProcessingETLLogging
RohitShuklaPINS
PROFILE
Rohitshuklapins
Overall Statistics
Feature vs Bugs
98%Features
Repository Contributions
471Total
Bugs
4
Commits
471
Features
164
Lines of code
48,329
Activity Months6
Your Network
61 peopleSame Organization
@planninginspectorate.gov.uk48
Work History
January 2026
160 Commits • 41 Features
Jan 1, 2026Monthly summary for Planning-Inspectorate/odw-synapse-workspace (2026-01): Delivered comprehensive standardisation and automation across Green Specialist Case and Casework Local Plan pipelines, with corresponding notebooks, migrations, and release controls. This period focused on aligning deployment and validation flows, stabilising data pipelines, and improving post-validation communications and data integrity to support scalable growth and quicker time-to-value for new casework types.
160 Commits • 41 Features
Jan 1, 2026Monthly summary for Planning-Inspectorate/odw-synapse-workspace (2026-01): Delivered comprehensive standardisation and automation across Green Specialist Case and Casework Local Plan pipelines, with corresponding notebooks, migrations, and release controls. This period focused on aligning deployment and validation flows, stabilising data pipelines, and improving post-validation communications and data integrity to support scalable growth and quicker time-to-value for new casework types.
January 2026
December 2025
80 Commits • 13 Features
Dec 1, 2025December 2025: Delivered a set of standards-led data engineering improvements across odw-synapse-workspace, focusing on standardising post-validation checks, stabilising pipelines, and expanding automated governance. The work enhances data quality, validation reliability, and end-to-end data lineage while enabling scalable casework processing.
December 2025
80 Commits • 13 Features
Dec 1, 2025December 2025: Delivered a set of standards-led data engineering improvements across odw-synapse-workspace, focusing on standardising post-validation checks, stabilising pipelines, and expanding automated governance. The work enhances data quality, validation reliability, and end-to-end data lineage while enabling scalable casework processing.
November 2025
18 Commits • 4 Features
Nov 1, 2025Month: 2025-11 — The Planning-Inspectorate/odw-synapse-workspace delivered a set of reliability, observability, and resource-management features that strengthen data integrity, debugging efficiency, and cost control across the ingestion and notebook pipelines. Key outcomes include robust Delta table schema enhancements with dynamic column support and ValidFrom/ValidTo metadata, enabling accurate historical queries and safer schema evolution; centralized autotune configuration and debug lifecycle management across PySpark notebooks with dynamic JSON schema paths for autotune configurations; notebook data processing enhancements for py_Inspector_Specialisms, including improved timestamp handling, execution tracking, and metadata capture for authors/dates with cleanup of extraneous cells; and a resource scaling/memory configuration overhaul that clarifies memory settings and aligns configurations with the main branch, reducing auto-scaling surprises and improving resource predictability. These changes collectively improve data reliability, traceability, debugging speed, and cost-efficient resource usage, delivering tangible business value and technical maturity.
18 Commits • 4 Features
Nov 1, 2025Month: 2025-11 — The Planning-Inspectorate/odw-synapse-workspace delivered a set of reliability, observability, and resource-management features that strengthen data integrity, debugging efficiency, and cost control across the ingestion and notebook pipelines. Key outcomes include robust Delta table schema enhancements with dynamic column support and ValidFrom/ValidTo metadata, enabling accurate historical queries and safer schema evolution; centralized autotune configuration and debug lifecycle management across PySpark notebooks with dynamic JSON schema paths for autotune configurations; notebook data processing enhancements for py_Inspector_Specialisms, including improved timestamp handling, execution tracking, and metadata capture for authors/dates with cleanup of extraneous cells; and a resource scaling/memory configuration overhaul that clarifies memory settings and aligns configurations with the main branch, reducing auto-scaling surprises and improving resource predictability. These changes collectively improve data reliability, traceability, debugging speed, and cost-efficient resource usage, delivering tangible business value and technical maturity.
November 2025
October 2025
31 Commits • 15 Features
Oct 1, 2025October 2025 – Planning-Inspectorate/odw-synapse-workspace: Delivered foundational data modeling with ER diagrams and initial data model files; advanced data standardisation workflows via notebooks for listed buildings (including Rohit variant) and cleanup of obsolete components; expanded observability through Application Insights integration across notebooks; broad notebook maintenance to standardise data flows (raw_to_std, entraid, horizon harmonised docs). These efforts establish a scalable data model, reproducible pipelines, and improved monitoring, enabling faster analytics and higher data quality.
October 2025
31 Commits • 15 Features
Oct 1, 2025October 2025 – Planning-Inspectorate/odw-synapse-workspace: Delivered foundational data modeling with ER diagrams and initial data model files; advanced data standardisation workflows via notebooks for listed buildings (including Rohit variant) and cleanup of obsolete components; expanded observability through Application Insights integration across notebooks; broad notebook maintenance to standardise data flows (raw_to_std, entraid, horizon harmonised docs). These efforts establish a scalable data model, reproducible pipelines, and improved monitoring, enabling faster analytics and higher data quality.
August 2025
169 Commits • 89 Features
Aug 1, 2025August 2025 monthly summary for odw-synapse-workspace: Delivered extensive notebook updates across NSIP and Appeals domains, improved data standardization and traceability, and strengthened data curation pipelines. Focused on delivering concrete business value by standardizing raw data, enhancing logging, and improving insights throughput.
169 Commits • 89 Features
Aug 1, 2025August 2025 monthly summary for odw-synapse-workspace: Delivered extensive notebook updates across NSIP and Appeals domains, improved data standardization and traceability, and strengthened data curation pipelines. Focused on delivering concrete business value by standardizing raw data, enhancing logging, and improving insights throughput.
August 2025
July 2025
13 Commits • 2 Features
Jul 1, 2025July 2025 monthly summary for Planning-Inspectorate/odw-synapse-workspace focusing on delivering robust data transformation, observability, and pipeline reliability to improve data quality and operational efficiency. Key features delivered include Py_SB Raw to Standard Data Transformation and Observability Enhancements with added logging and pipeline integration, and Logging Utilities Enhancements Across Notebooks for unified diagnostics. Major bug fix: Deployment/Pipeline Configuration Reverts and SAP HR Data Trigger Fix to stabilize daily processing. Overall impact: improved data quality, end-to-end traceability, reduced toil, and safer deployments. Technologies/skills demonstrated include Python data pipelines, notebook-based ETL, logging/observability engineering, environment management, and DevOps practices.
July 2025
13 Commits • 2 Features
Jul 1, 2025July 2025 monthly summary for Planning-Inspectorate/odw-synapse-workspace focusing on delivering robust data transformation, observability, and pipeline reliability to improve data quality and operational efficiency. Key features delivered include Py_SB Raw to Standard Data Transformation and Observability Enhancements with added logging and pipeline integration, and Logging Utilities Enhancements Across Notebooks for unified diagnostics. Major bug fix: Deployment/Pipeline Configuration Reverts and SAP HR Data Trigger Fix to stabilize daily processing. Overall impact: improved data quality, end-to-end traceability, reduced toil, and safer deployments. Technologies/skills demonstrated include Python data pipelines, notebook-based ETL, logging/observability engineering, environment management, and DevOps practices.
Activity
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Quality Metrics
Correctness77.0%
Maintainability76.2%
Architecture75.8%
Performance75.2%
AI Usage25.0%
Skills & Technologies
Programming Languages
JSONMarkdownMermaidPythonSQLYAMLjsonpython
Technical Skills
API IntegrationApache SparkAzureAzure Data FactoryAzure SynapseAzure Synapse AnalyticsCSV handlingCloud Computing (Azure Synapse)Cloud MonitoringCloud ServicesCode CleanupData AnalysisData CurationData EngineeringData Loading
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