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This report presents the forensic synthetic code analysis of DataTalksClub/data-engineering-zoomcamp, a Jupyter Notebook project with 43,555 GitHub stars. SynthScan v2.0 examined 31,936 lines of code across 316 source files, recording 177 pattern matches distributed across 14 syntactic categories. The overall adjusted score of 11.1 places this repository in the Low AI signal band.
The scanner applied 160+ deterministic lexical heuristics, multi-line block detectors, abstract syntax tree depth profilers, and a cross-file Jaccard similarity matrix to construct a statistically normalised synthetic code estimate. All matches are individually weighted by severity coefficient and contextual multiplier before summation, and the resulting headline score is temporally discounted to account for the repository's development history relative to the commercial emergence of large language model coding tooling (November 2022 onward).
This chart maps the temporal evolution of the adjusted synthetic code score across successive scan runs. An upward trajectory indicates ongoing incorporation of AI-generated code or expanding LLM-assisted scaffolding; a stable or declining trajectory may reflect active human refactoring, code removal, or the adoption of stricter authorship policies. The dashed secondary line (right axis) independently tracks total raw pattern hit count, which can diverge from the normalised score when codebase size changes significantly between scans.
Classifies detected patterns by their diagnostic confidence and structural impact. CRITICAL patterns (coefficient 10) represent definitive synthetic signatures — hallucinated imports, explicit LLM attribution metadata — virtually never produced by human authors. HIGH (5) indicates strong structural tells such as cross-file repetition or cross-linguistic idioms. MEDIUM (2) covers recognisable conversational padding and AI-specific vocabulary. LOW (1) captures subtle indicators like tautological comments and generic boilerplate that require density to carry independent signal.
This horizontal bar chart decomposes the repository's raw synthetic code score by top-level directory, allowing you to pinpoint precisely which modules or components carry the highest AI authorship density. Directories with disproportionately high scores relative to their size warrant targeted manual review: concentrated AI signatures often trace back to mass-generated configuration layers, auto-ported test suites, LLM-scaffolded boilerplate classes, or entire subsystems authored under heavy copilot assistance. Use this view to prioritise your human code-review effort.
The scanner identified 177 distinct pattern matches across 14 syntactic categories. Each entry below represents a discrete location in the source code where the engine recorded a statistically significant AI authorship indicator. Expand any category row to inspect the individual file paths, line numbers, code snippets, and the lexical context (CODE, COMMENT, or STRING) in which each match was detected.
Reading the findings table: The Severity column indicates the diagnostic confidence level (CRITICAL / HIGH / MEDIUM / LOW). The Context column identifies whether the match occurred inside executable code, an inline comment, or a string literal — comment-context matches receive a ×1.5 weight because LLMs systematically over-annotate. The ⚡ bolt icon marks clustered matches: three or more patterns within a 10-line window, each receiving an additional ×1.5 density multiplier as dense clusters constitute far stronger evidence of synthetic authorship than isolated hits.
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| HIGH | …s/2023/week_6_stream_processing/streaming_confluent.py | 0 | take a spark streaming df and parse value col based on <schema>, return streaming df cols in schema | STRING |
| HIGH | …ing/extras/python/streams-example/pyspark/streaming.py | 0 | take a spark streaming df and parse value col based on <schema>, return streaming df cols in schema | STRING |
| HIGH | …ng/extras/python/streams-example/redpanda/streaming.py | 0 | take a spark streaming df and parse value col based on <schema>, return streaming df cols in schema | STRING |
| HIGH | 07-streaming/workshop/README.md | 0 | create table {table_name} ( pulocationid integer, dolocationid integer, trip_distance double, total_amount double, tpep_ | STRING |
| HIGH | 07-streaming/workshop/live/src/job/pass_through_job.py | 0 | create table {table_name} ( pulocationid integer, dolocationid integer, trip_distance double, total_amount double, tpep_ | STRING |
| HIGH | 07-streaming/workshop/src/job/pass_through_job.py | 0 | create table {table_name} ( pulocationid integer, dolocationid integer, trip_distance double, total_amount double, tpep_ | STRING |
| HIGH | 07-streaming/workshop/README.md | 0 | create table {table_name} ( pulocationid integer, dolocationid integer, trip_distance double, total_amount double, picku | STRING |
| HIGH | 07-streaming/workshop/live/src/job/pass_through_job.py | 0 | create table {table_name} ( pulocationid integer, dolocationid integer, trip_distance double, total_amount double, picku | STRING |
| HIGH | 07-streaming/workshop/src/job/pass_through_job.py | 0 | create table {table_name} ( pulocationid integer, dolocationid integer, trip_distance double, total_amount double, picku | STRING |
| HIGH | 07-streaming/workshop/README.md | 0 | insert into {postgres_sink} select pulocationid, dolocationid, trip_distance, total_amount, to_timestamp_ltz(tpep_pickup | STRING |
| HIGH | 07-streaming/workshop/live/src/job/pass_through_job.py | 0 | insert into {postgres_sink} select pulocationid, dolocationid, trip_distance, total_amount, to_timestamp_ltz(tpep_pickup | STRING |
| HIGH | 07-streaming/workshop/src/job/pass_through_job.py | 0 | insert into {postgres_sink} select pulocationid, dolocationid, trip_distance, total_amount, to_timestamp_ltz(tpep_pickup | STRING |
| HIGH | 07-streaming/workshop/README.md | 0 | create table {table_name} ( pulocationid integer, dolocationid integer, trip_distance double, total_amount double, tpep_ | STRING |
| HIGH | 07-streaming/workshop/live/src/job/aggregation_job.py | 0 | create table {table_name} ( pulocationid integer, dolocationid integer, trip_distance double, total_amount double, tpep_ | STRING |
| HIGH | 07-streaming/workshop/src/job/aggregation_job.py | 0 | create table {table_name} ( pulocationid integer, dolocationid integer, trip_distance double, total_amount double, tpep_ | STRING |
| HIGH | 07-streaming/workshop/src/job/aggregation_job_demo.py | 0 | create table {table_name} ( pulocationid integer, dolocationid integer, trip_distance double, total_amount double, tpep_ | STRING |
| HIGH | 07-streaming/workshop/README.md | 0 | create table {table_name} ( window_start timestamp(3), pulocationid int, num_trips bigint, total_revenue double, primary | STRING |
| HIGH | 07-streaming/workshop/live/src/job/aggregation_job.py | 0 | create table {table_name} ( window_start timestamp(3), pulocationid int, num_trips bigint, total_revenue double, primary | STRING |
| HIGH | 07-streaming/workshop/src/job/aggregation_job.py | 0 | create table {table_name} ( window_start timestamp(3), pulocationid int, num_trips bigint, total_revenue double, primary | STRING |
| HIGH | 07-streaming/workshop/src/job/aggregation_job_demo.py | 0 | create table {table_name} ( window_start timestamp(3), pulocationid int, num_trips bigint, total_revenue double, primary | STRING |
| HIGH | 07-streaming/workshop/README.md | 0 | insert into {aggregated_table} select window_start, pulocationid, count(*) as num_trips, sum(total_amount) as total_reve | STRING |
| HIGH | 07-streaming/workshop/live/src/job/aggregation_job.py | 0 | insert into {aggregated_table} select window_start, pulocationid, count(*) as num_trips, sum(total_amount) as total_reve | STRING |
| HIGH | 07-streaming/workshop/src/job/aggregation_job.py | 0 | insert into {aggregated_table} select window_start, pulocationid, count(*) as num_trips, sum(total_amount) as total_reve | STRING |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | 04-analytics-engineering/setup/local_setup.md | 22 | ## Step 1: Install DuckDB | COMMENT |
| LOW | 04-analytics-engineering/setup/local_setup.md | 29 | ## Step 2: Install dbt | COMMENT |
| LOW | 04-analytics-engineering/setup/local_setup.md | 40 | ## Step 3: Configure dbt Profile | COMMENT |
| LOW | 04-analytics-engineering/setup/local_setup.md | 82 | ## Step 4: Download and Ingest Data | COMMENT |
| LOW | 04-analytics-engineering/setup/local_setup.md | 161 | ## Step 5: Test the dbt Connection | STRING |
| LOW | 04-analytics-engineering/setup/local_setup.md | 169 | ## Step 6: Install dbt Power User Extension (VS Code Users) | STRING |
| LOW⚡ | …-analytics-engineering/setup/duckdb_troubleshooting.md | 52 | ### Step 1: Adjust DuckDB memory settings in `profiles.yml` | COMMENT |
| LOW⚡ | …-analytics-engineering/setup/duckdb_troubleshooting.md | 60 | ### Step 2: Use `dbt retry` after a failure | COMMENT |
| LOW⚡ | …-analytics-engineering/setup/duckdb_troubleshooting.md | 70 | ### Step 3: Build models selectively with `--select` | COMMENT |
| LOW | …-analytics-engineering/setup/duckdb_troubleshooting.md | 84 | ### Step 4: Leverage incremental models | COMMENT |
| LOW | 04-analytics-engineering/setup/cloud_setup.md | 20 | ## Step 1: Verify Your BigQuery Setup | COMMENT |
| LOW | 04-analytics-engineering/setup/cloud_setup.md | 108 | ## Step 2: Sign Up for dbt Platform | COMMENT |
| LOW | 04-analytics-engineering/setup/cloud_setup.md | 112 | ## Step 3: Create a New dbt Project | COMMENT |
| LOW | 04-analytics-engineering/setup/cloud_setup.md | 123 | ## Step 4: Configure BigQuery Connection | COMMENT |
| LOW | 04-analytics-engineering/setup/cloud_setup.md | 170 | ## Step 5: Set Up Your Repository | COMMENT |
| LOW | 04-analytics-engineering/setup/cloud_setup.md | 179 | ## Step 6: Verify Your Development Environment | COMMENT |
| LOW | 04-analytics-engineering/setup/cloud_setup.md | 219 | ## Step 7: Start Developing | COMMENT |
| LOW | …2022/week_3_data_warehouse/airflow/docker-compose.yaml | 21 | # WARNING: This configuration is for local development. Do not use it in a production deployment. | COMMENT |
| LOW | …2022/week_2_data_ingestion/airflow/docker-compose.yaml | 21 | # WARNING: This configuration is for local development. Do not use it in a production deployment. | COMMENT |
| LOW | …eek_2_data_ingestion/airflow/docker-compose_2.3.4.yaml | 21 | # WARNING: This configuration is for local development. Do not use it in a production deployment. | COMMENT |
| LOW⚡ | …s/2023/week_6_stream_processing/streaming_confluent.py | 82 | # Step 1: Consume GREEN_TAXI_TOPIC and FHV_TAXI_TOPIC | COMMENT |
| LOW⚡ | …s/2023/week_6_stream_processing/streaming_confluent.py | 86 | # Step 2: Publish green and fhv rides to RIDES_TOPIC | COMMENT |
| LOW⚡ | …s/2023/week_6_stream_processing/streaming_confluent.py | 90 | # Step 3: Read RIDES_TOPIC and parse it in ALL_RIDE_SCHEMA | COMMENT |
| LOW⚡ | …s/2023/week_6_stream_processing/streaming_confluent.py | 94 | # Step 4: Apply Aggregation on the all_rides | COMMENT |
| LOW⚡ | …s/2023/week_6_stream_processing/streaming_confluent.py | 98 | # Step 5: Sink Aggregation Streams to Console | COMMENT |
| LOW | cohorts/2026/workshops/dlt/README.md | 69 | ### Step 1: Create a New Project Folder | COMMENT |
| LOW | cohorts/2026/workshops/dlt/README.md | 78 | ### Step 2: Add the dlt MCP Server Config | COMMENT |
| LOW⚡ | cohorts/2026/workshops/dlt/README.md | 134 | ### Step 3: Install dlt Workspace | COMMENT |
| LOW⚡ | cohorts/2026/workshops/dlt/README.md | 140 | ### Step 4: Initialize the dlt Project | COMMENT |
| LOW⚡ | cohorts/2026/workshops/dlt/README.md | 150 | ### Step 5: Prompt the Agent to Build and Run the Pipeline | COMMENT |
| LOW | cohorts/2026/workshops/dlt/README.md | 171 | ### Step 6: Debug with the Agent | COMMENT |
| LOW | cohorts/2026/workshops/dlt/README.md | 175 | ### Step 7: Inspect Pipeline Data with the dlt Dashboard | COMMENT |
| LOW | cohorts/2026/workshops/dlt/README.md | 191 | ### Step 8: Inspect the Pipeline via Chat | COMMENT |
| LOW | cohorts/2026/workshops/dlt/dlt_homework.md | 30 | ### Step 1: Create a New Project (or Reuse Your Demo Project) | COMMENT |
| LOW | cohorts/2026/workshops/dlt/dlt_homework.md | 41 | ### Step 2: Set Up the dlt MCP Server (If Not Already Done) | COMMENT |
| LOW | cohorts/2026/workshops/dlt/dlt_homework.md | 97 | ### Step 3: Install dlt | COMMENT |
| LOW | cohorts/2026/workshops/dlt/dlt_homework.md | 103 | ### Step 4: Initialize the Project | COMMENT |
| LOW | cohorts/2026/workshops/dlt/dlt_homework.md | 115 | ### Step 5: Prompt the Agent | COMMENT |
| LOW | cohorts/2026/workshops/dlt/dlt_homework.md | 133 | ### Step 6: Run and Debug | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| MEDIUM | README.md | 94 | ### [Module 2: Workflow Orchestration](02-workflow-orchestration/) | COMMENT |
| MEDIUM | awesome-data-engineering.md | 53 | ### Workflow orchestration | COMMENT |
| MEDIUM | 02-workflow-orchestration/README.md | 14 | - [2.1 - Introduction to Workflow Orchestration](#21-introduction-to-workflow-orchestration) | CODE |
| MEDIUM⚡ | 02-workflow-orchestration/README.md | 511 | ### 2.5.4 Bonus: Retrieval Augmented Generation (RAG) | COMMENT |
| MEDIUM⚡ | 02-workflow-orchestration/README.md | 515 | #### What is RAG? | COMMENT |
| MEDIUM⚡ | 02-workflow-orchestration/README.md | 524 | #### How RAG Works in Kestra | COMMENT |
| MEDIUM | 02-workflow-orchestration/README.md | 571 | #### RAG Best Practices | COMMENT |
| MEDIUM | 02-workflow-orchestration/README.md | 665 | * 2024: [notes](../cohorts/2024/02-workflow-orchestration#community-notes) and [videos](../cohorts/2024/02-workflow-orch | COMMENT |
| MEDIUM | 02-workflow-orchestration/README.md | 666 | * 2025: [notes](../cohorts/2025/02-workflow-orchestration/README.md#community-notes) and [videos](../cohorts/2025/02-wor | COMMENT |
| MEDIUM | cohorts/2022/week_2_data_ingestion/README.md | 14 | ### Introduction to Workflow orchestration | COMMENT |
| MEDIUM | cohorts/2025/02-workflow-orchestration/README.md | 450 | * 2024: [notes](../cohorts/2024/02-workflow-orchestration#community-notes) and [videos](../cohorts/2024/02-workflow-orch | COMMENT |
| MEDIUM | cohorts/2024/02-workflow-orchestration/README.md | 14 | * [2.2.1 - 📯 Intro to Orchestration](#221----intro-to-orchestration) | COMMENT |
| MEDIUM | cohorts/2023/week_2_workflow_orchestration/README.md | 16 | ### 1. Introduction to Workflow orchestration | COMMENT |
| MEDIUM | cohorts/2026/workshops/dlt/README.md | 47 | | [Windsurf](https://codeium.com/windsurf) | Alternative agentic IDE | | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | 02-workflow-orchestration/flows/02_python.yaml | 18 | def get_docker_image_downloads(image_name: str = "kestra/kestra"): | CODE |
| LOW | 04-analytics-engineering/setup/local_setup.md | 93 | def download_and_convert_files(taxi_type): | CODE |
| LOW | cohorts/2022/week_2_data_ingestion/homework/solution.py | 45 | def donwload_parquetize_upload_dag( | CODE |
| LOW | …-data-warehouse/extras/web_to_gcs_with_progress_bar.py | 52 | def csv_to_parquet_with_progress( | CODE |
| LOW | …-data-warehouse/extras/web_to_gcs_with_progress_bar.py | 115 | def upload_to_gcs_with_progress(bucket: str, object_name: str, local_file: str): | CODE |
| LOW | 07-streaming/workshop/README.md | 799 | def create_events_source_kafka(t_env): | CODE |
| LOW | 07-streaming/workshop/README.md | 834 | def create_processed_events_sink_postgres(t_env): | STRING |
| LOW | 07-streaming/workshop/README.md | 1042 | def create_events_source_kafka(t_env): | STRING |
| LOW | 07-streaming/workshop/README.md | 1066 | def create_events_aggregated_sink(t_env): | STRING |
| LOW | 07-streaming/workshop/live/src/job/aggregation_job.py | 5 | def create_events_source_kafka(t_env): | CODE |
| LOW | 07-streaming/workshop/live/src/job/aggregation_job.py | 29 | def create_events_aggregated_sink(t_env): | STRING |
| LOW | 07-streaming/workshop/live/src/job/pass_through_job.py | 6 | def create_events_source_kafka(t_env): | CODE |
| LOW | 07-streaming/workshop/live/src/job/pass_through_job.py | 28 | def create_processed_events_sink_postgres(t_env): | STRING |
| LOW | 07-streaming/workshop/src/job/aggregation_job.py | 5 | def create_events_aggregated_sink(t_env): | CODE |
| LOW | 07-streaming/workshop/src/job/aggregation_job.py | 26 | def create_events_source_kafka(t_env): | STRING |
| LOW | 07-streaming/workshop/src/job/pass_through_job.py | 5 | def create_processed_events_sink_postgres(t_env): | CODE |
| LOW | 07-streaming/workshop/src/job/pass_through_job.py | 27 | def create_events_source_kafka(t_env): | STRING |
| LOW | 07-streaming/workshop/src/job/aggregation_job_demo.py | 14 | def create_events_source_kafka(t_env): | CODE |
| LOW | 07-streaming/workshop/src/job/aggregation_job_demo.py | 38 | def create_events_aggregated_sink(t_env): | STRING |
| LOW | …ing/extras/python/streams-example/pyspark/streaming.py | 20 | def parse_ride_from_kafka_message(df, schema): | CODE |
| LOW | …ng/extras/python/streams-example/redpanda/streaming.py | 20 | def parse_ride_from_kafka_message(df, schema): | CODE |
| LOW | 07-streaming/extras/pyflink/src/job/aggregation_job.py | 6 | def create_events_aggregated_sink(t_env): | CODE |
| LOW | 07-streaming/extras/pyflink/src/job/aggregation_job.py | 26 | def create_events_source_kafka(t_env): | STRING |
| LOW | 07-streaming/extras/pyflink/src/job/start_job.py | 5 | def create_processed_events_sink_postgres(t_env): | CODE |
| LOW | 07-streaming/extras/pyflink/src/job/start_job.py | 24 | def create_events_source_kafka(t_env): | STRING |
| LOW | 07-streaming/extras/pyflink/src/job/taxi_job.py | 5 | def create_taxi_events_sink_postgres(t_env): | CODE |
| LOW | 07-streaming/extras/pyflink/src/job/taxi_job.py | 42 | def create_events_source_kafka(t_env): | STRING |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| MEDIUM | …2022/week_3_data_warehouse/airflow/docker-compose.yaml | 39 | # Feel free to modify this file to suit your needs. | COMMENT |
| MEDIUM | …2022/week_3_data_warehouse/airflow/docker-compose.yaml | 44 | # In order to add custom dependencies or upgrade provider packages you can use your extended image. | COMMENT |
| MEDIUM | …2022/week_2_data_ingestion/airflow/docker-compose.yaml | 39 | # Feel free to modify this file to suit your needs. | COMMENT |
| MEDIUM | …2022/week_2_data_ingestion/airflow/docker-compose.yaml | 44 | # In order to add custom dependencies or upgrade provider packages you can use your extended image. | COMMENT |
| MEDIUM | …eek_2_data_ingestion/airflow/docker-compose_2.3.4.yaml | 39 | # Feel free to modify this file to suit your needs. | COMMENT |
| MEDIUM | …eek_2_data_ingestion/airflow/docker-compose_2.3.4.yaml | 44 | # In order to add custom dependencies or upgrade provider packages you can use your extended image. | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | …22/week_3_data_warehouse/airflow/dags/gcs_to_bq_dag.py | 2 | CODE | |
| LOW | …k_2_data_ingestion/airflow/dags_local/ingest_script.py | 1 | CODE | |
| LOW | …ingestion/airflow/extras/data_ingestion_gcs_dag_ex2.py | 2 | CODE | |
| LOW | …ingestion/airflow/extras/data_ingestion_gcs_dag_ex2.py | 7 | CODE | |
| LOW | …ingestion/airflow/extras/data_ingestion_gcs_dag_ex2.py | 8 | CODE | |
| LOW | cohorts/2025/workshops/dynamic_load_dlt.py | 1 | CODE | |
| LOW | 06-batch/code/06_spark_sql.py | 6 | CODE | |
| LOW | 06-batch/code/06_spark_sql_big_query.py | 6 | CODE | |
| LOW | 07-streaming/workshop/src/producers/producer.py | 11 | CODE | |
| LOW | 07-streaming/extras/python/redpanda_example/consumer.py | 1 | CODE | |
| LOW | 07-streaming/extras/pyflink/src/job/aggregation_job.py | 2 | CODE | |
| LOW | 07-streaming/extras/pyflink/src/job/aggregation_job.py | 2 | CODE | |
| LOW | 07-streaming/extras/pyflink/src/job/start_job.py | 2 | CODE | |
| LOW | 07-streaming/extras/pyflink/src/job/start_job.py | 2 | CODE | |
| LOW | 07-streaming/extras/pyflink/src/job/taxi_job.py | 2 | CODE | |
| LOW | 07-streaming/extras/pyflink/src/job/taxi_job.py | 2 | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | …2022/week_3_data_warehouse/airflow/docker-compose.yaml | 1 | # Licensed to the Apache Software Foundation (ASF) under one | COMMENT |
| LOW | …2022/week_3_data_warehouse/airflow/docker-compose.yaml | 21 | # WARNING: This configuration is for local development. Do not use it in a production deployment. | COMMENT |
| LOW | …2022/week_3_data_warehouse/airflow/docker-compose.yaml | 141 | # healthcheck: | COMMENT |
| LOW | …2022/week_3_data_warehouse/airflow/docker-compose.yaml | 161 | # command: triggerer | COMMENT |
| LOW | …2022/week_3_data_warehouse/airflow/docker-compose.yaml | 261 | - airflow | COMMENT |
| LOW | …2022/week_2_data_ingestion/airflow/docker-compose.yaml | 1 | # Licensed to the Apache Software Foundation (ASF) under one | COMMENT |
| LOW | …2022/week_2_data_ingestion/airflow/docker-compose.yaml | 21 | # WARNING: This configuration is for local development. Do not use it in a production deployment. | COMMENT |
| LOW | …eek_2_data_ingestion/airflow/docker-compose_2.3.4.yaml | 1 | # Licensed to the Apache Software Foundation (ASF) under one | COMMENT |
| LOW | …eek_2_data_ingestion/airflow/docker-compose_2.3.4.yaml | 21 | # WARNING: This configuration is for local development. Do not use it in a production deployment. | COMMENT |
| LOW | …ingestion/airflow/extras/data_ingestion_gcs_dag_ex2.py | 21 | "depends_on_past": False, | COMMENT |
| LOW | …ingestion/airflow/extras/data_ingestion_gcs_dag_ex2.py | 61 | COMMENT | |
| LOW | 03-data-warehouse/extras/web_to_gcs.py | 101 | # web_to_gcs("2020", "yellow") | COMMENT |
| LOW | 07-streaming/workshop/live/README.md | 1 | # streaming-workshop | COMMENT |
| LOW | …ing/extras/python/redpanda_example/docker-compose.yaml | 41 | # command: | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | cohorts/2025/workshops/dynamic_load_dlt.py | 107 | except Exception as e: | CODE |
| LOW | cohorts/2025/03-data-warehouse/load_yellow_taxi_data.py | 40 | except Exception as e: | CODE |
| LOW | cohorts/2025/03-data-warehouse/load_yellow_taxi_data.py | 96 | except Exception as e: | CODE |
| LOW | …ts/2023/week_6_stream_processing/producer_confluent.py | 50 | except Exception as e: | CODE |
| LOW | cohorts/2026/03-data-warehouse/load_yellow_taxi_data.py | 40 | except Exception as e: | CODE |
| LOW | cohorts/2026/03-data-warehouse/load_yellow_taxi_data.py | 96 | except Exception as e: | CODE |
| LOW | 07-streaming/workshop/README.md | 1114 | except Exception as e: | STRING |
| LOW | 07-streaming/workshop/live/src/job/aggregation_job.py | 77 | except Exception as e: | STRING |
| LOW | 07-streaming/workshop/src/job/aggregation_job.py | 79 | except Exception as e: | STRING |
| LOW | 07-streaming/workshop/src/job/pass_through_job.py | 74 | except Exception as e: | STRING |
| LOW | 07-streaming/workshop/src/job/aggregation_job_demo.py | 88 | except Exception as e: | STRING |
| LOW | 07-streaming/extras/python/avro_example/producer.py | 69 | except Exception as e: | CODE |
| LOW | …ming/extras/python/streams-example/pyspark/producer.py | 46 | except Exception as e: | CODE |
| LOW | …ing/extras/python/streams-example/redpanda/producer.py | 46 | except Exception as e: | CODE |
| LOW | 07-streaming/extras/pyflink/src/job/aggregation_job.py | 85 | except Exception as e: | STRING |
| LOW | 07-streaming/extras/pyflink/src/job/start_job.py | 69 | except Exception as e: | STRING |
| LOW | 07-streaming/extras/pyflink/src/job/taxi_job.py | 104 | except Exception as e: | STRING |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| MEDIUM | …2022/week_3_data_warehouse/airflow/2_setup_nofrills.md | 113 | # ----------------------------------- | COMMENT |
| MEDIUM | …2022/week_3_data_warehouse/airflow/1_setup_official.md | 113 | # ----------------------------------- | COMMENT |
| MEDIUM | …2022/week_2_data_ingestion/airflow/2_setup_nofrills.md | 112 | # ----------------------------------- | COMMENT |
| MEDIUM | …2022/week_2_data_ingestion/airflow/1_setup_official.md | 111 | # ----------------------------------- | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW⚡ | …s/2023/week_6_stream_processing/streaming_confluent.py | 82 | # Step 1: Consume GREEN_TAXI_TOPIC and FHV_TAXI_TOPIC | COMMENT |
| LOW⚡ | …s/2023/week_6_stream_processing/streaming_confluent.py | 86 | # Step 2: Publish green and fhv rides to RIDES_TOPIC | COMMENT |
| LOW⚡ | …s/2023/week_6_stream_processing/streaming_confluent.py | 90 | # Step 3: Read RIDES_TOPIC and parse it in ALL_RIDE_SCHEMA | COMMENT |
| LOW⚡ | …s/2023/week_6_stream_processing/streaming_confluent.py | 94 | # Step 4: Apply Aggregation on the all_rides | COMMENT |
| LOW⚡ | …s/2023/week_6_stream_processing/streaming_confluent.py | 98 | # Step 5: Sink Aggregation Streams to Console | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| MEDIUM | …22/week_3_data_warehouse/airflow/dags/gcs_to_bq_dag.py | 70 | # Create a partitioned table from external table | COMMENT |
| MEDIUM | cohorts/2025/workshops/dynamic_load_dlt.py | 110 | # Create the pipeline | COMMENT |
| MEDIUM | …reaming/extras/pyflink/src/producers/load_taxi_data.py | 6 | # Create a Kafka producer | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | cohorts/2025/workshops/dynamic_load_dlt.py | 94 | CODE | |
| LOW | …-data-warehouse/extras/web_to_gcs_with_progress_bar.py | 30 | CODE | |
| LOW | 07-streaming/extras/python/redpanda_example/consumer.py | 14 | CODE | |
| LOW | 07-streaming/extras/python/json_example/consumer.py | 13 | CODE | |
| LOW | …ming/extras/python/streams-example/pyspark/consumer.py | 12 | CODE | |
| LOW | …ing/extras/python/streams-example/redpanda/consumer.py | 12 | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| HIGH | 02-workflow-orchestration/README.md | 483 | export GEMINI_API_KEY="your-api-key-here" | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | cohorts/2025/03-data-warehouse/load_yellow_taxi_data.py | 50 | # Check if the bucket belongs to the current project | COMMENT |
| LOW | cohorts/2026/03-data-warehouse/load_yellow_taxi_data.py | 50 | # Check if the bucket belongs to the current project | COMMENT |