Resolve 3 Dysfunctional Airflow DAGs (Databricks)

@mayankinnovative

I understand the priority is to diagnose and fix three existing Airflow DAGs that orchestrate Databricks notebooks and PostgreSQL control/checkpoint workflows, then prove all three execute successfully end-to-end. I’ll start from Airflow task logs and error traces, tracing each failure through DAG dependencies, Variables/Connections, parameters, Databricks notebook paths, permissions and PostgreSQL control-table state. I’ll inspect the PySpark/Python notebook logic and config-driven processing to distinguish orchestration issues from notebook-level defects, then implement the smallest reliable fixes without disrupting working components. After each fix, I’ll rerun the affected tasks and complete full DAG executions, validating batch acquire → processing → batch complete and checkpoint updates. I’ll document root causes, changes made and any configuration requirements for clean handoff. 1. Are all three DAGs failing at the same task/stage or differently? 2. Which Airflow and Databricks runtime versions are currently used? 3. Are recent failure logs already available for review? 4. Are DAG/notebook changes managed through Git or another deployment process? I’ll systematically isolate the root causes and get all three pipelines running reliably.

@vishal1145

Your DAGs are failing because Airflow is either passing malformed parameters to Databricks tasks or your checkpoint tables are locking due to concurrent reads. This will cause silent failures that look like "success" in logs but produce no output. Quick questions - are your DAGs using Airflow Variables or hardcoded notebook paths, and are the PostgreSQL checkpoint tables using row-level locking or optimistic concurrency? Here is the architectural approach: - AIRFLOW DEBUGGING: Trace task logs to isolate whether failures originate in Airflow's DatabricksRunNowOperator config or inside the notebooks themselves, then fix parameter serialization. - DATABRICKS NOTEBOOK FIXES: Review PySpark error handling in batch acquire/complete steps and ensure notebook widgets match the parameters Airflow is sending. - POSTGRESQL CHECKPOINT REPAIR: Validate that control table queries use proper transaction isolation to prevent race conditions when multiple DAG runs execute concurrently. I've debugged 40+ production Airflow pipelines orchestrating Databricks workloads across fintech and healthcare clients. Let's hop on a 15-minute screen share so I can see your logs and give you a fix timeline today.

@vinod150987

Hi, I can help diagnose and fix the failures in all three Airflow DAGs — Profile Load, Load, and PAU — and get them running successfully end-to-end. I have hands-on experience with Apache Airflow, Databricks, DBT, Snowflake, PostgreSQL, and data engineering pipelines, including troubleshooting production workflows involving DAG orchestration, notebook execution, configuration-driven processing, and control/checkpoint tables. I can start by reviewing the failure logs for the Profile Load, Load, and PAU DAGs, identify the first failing component in each execution chain, and work through the issues systematically until all three pipelines complete successfully. Regards

@Cruz1217

Hi, I notice the three Airflow DAGs are failing on execution, which suggests a deeper issue with the task sequences or notebook interactions. I'd approach this by reviewing the failure logs and error traces for each DAG, focusing on potential config mismatches or permission issues. My first step would be to check the PostgreSQL-backed control tables for any inconsistencies. I've seen similar issues with Databricks notebook tasks where parameter mismatches between tasks cause failures. In my experience, identifying the root cause of each failure is crucial, and I'd propose fixes based on the specific issues found. The client can expect a thorough review of the DAGs, identification of the root causes, and implementation of fixes to ensure all three DAGs run successfully.

@robertn248

With a deep understanding of Apache Airflow, Databricks, and SQL-based control/checkpoint table patterns, I am well-equipped to resolve the issues in your 3 dysfunctional DAGs. Over my years of experience, I have sharpened my skills in debugging, task logs, variable/connection troubleshooting -- all of which will be vital in identifying and tackling the root cause behind each failure. Being proficient in Python, I can effectively work with your Databricks notebooks-- whether it's debugging a notebook-level bug or proposing necessary configuration changes. Additionally, my ability to meticulously review failure logs/error traces is an advantageous skill that will help me swiftly identify even the most cryptic issues. Furthermore, my commitment to clean code and reliable backend systems ensures that not only will I fix the current problems but also put measures in place to avoid such issues in the future. My passion for turning complex problems into solutions makes me the ideal candidate for this project. So let's join forces and bring back smooth functioning to your DAGs!

@alfatrade2

Your three DAG descriptions are still template placeholders and no logs are attached, so I can't yet tell whether these are Databricks cluster errors or bad state in the checkpoint tables. A failed batch acquire that leaves a stale Postgres row will break every rerun, and that's often the real cause. I haven't run Airflow with Databricks itself, but I've built Python data pipelines and FastAPI services. Can you paste the first failed task log for each DAG?

@thinkbotictech

Hi, I can diagnose and fix all 3 Airflow DAGs by tracing task logs, checking Variables/Connections, Databricks notebook parameters, paths, permissions and PostgreSQL control/checkpoint logic. I’ll implement the required fixes and validate each DAG end-to-end until the full workflow runs successfully. 1. Can you provide the current Airflow task logs and error traces for the three failing DAGs? 2. Are the Databricks notebooks shared through Jobs/Workflows, or are they triggered directly from Airflow? if looking for expert then lets connect (expereince 7 yrs in development)

@NEHABHAT92

As an experienced web and mobile app developer, I am well-versed in Python - a crucial skill for this project where the core framework is Apache Airflow. With over 9 years in the industry, I have also come across and resolved diverse glitches. This makes me capable of handling the task at hand - debugging and fixing issues encountered on your trio of dysfunctional airflow DAGS. With access to the necessary tools, including Airflow UI access, Databricks workspace access, relevant notebook and DAG codes – I will evaluate all failure logs/error traces for each of the DAGs on Airflow, identifying their core problems and proffering appropriate fixes that can span from config mismatches to permission issues, path/naming problems, parameter match-ups, or even possible notebook-level bugs. Furthermore, my extensive experience with SQL-based control/checkpoint table patterns perfectly syncs with your databases using PostgreSQL-backed control/checkpoint tables. Beyond resolving these errors successfully within a reasonable time frame, you'd enjoy post-resolution support from me gratis for three months - just to ensure everything keeps running smoothly. Choose me and let's get your DAGs back on track!

@indreshpnk

Hello, I understand you need to debug and fix 3 failing Airflow DAGs that orchestrate Databricks notebook tasks and PostgreSQL-backed control/checkpoint tables. The goal is to identify the root cause of each failure and get all three DAGs running end-to-end successfully. Here’s what I can provide: Review Airflow task logs, error traces, DAG dependencies, Variables, and Connections to identify failures. Debug Databricks Python/PySpark notebooks, parameters, paths, configurations, and task-to-task data flow. Validate PostgreSQL control/checkpoint queries and implement reliable fixes with end-to-end testing. I bring over 4+ years of experience in Python, SQL, REST APIs, databases, ETL workflows, and cloud-based applications. I’ve worked with backend systems, data processing, API integrations, and AWS deployments, with a strong focus on debugging and reliable execution. Just to clarify a few things: Can you share the current Airflow error logs and DAG code after access is provided? Are the Databricks notebooks and PostgreSQL control tables already confirmed to work independently? Please come to the chat box to discuss more about your project. Best regards Indresh Kushwaha

@ronakkataria1

All three DAGs share one pattern, batch acquire, then config-driven steps, then batch complete, off a Postgres control table. That pattern has a failure mode that fixing the code alone won't clear. When a run dies midway, the batch it acquired stays marked in progress. The next run's acquire step either finds nothing to pick up or trips on the stale lock, so the DAG keeps failing after the real bug is fixed. Every fix I make comes with a check of the checkpoint rows and a reset of any batch left hanging, or the first rerun fails the same way. The other usual suspects. Parameters passed from Airflow into a notebook arrive as strings, so a batch id of None or a date in the wrong format passes Airflow fine and breaks inside the notebook. An expired Databricks token or a changed cluster policy fails every task the same way, which looks like three bugs but is one. And a notebook path or widget name renamed on one side only. I'll read the task logs for all three first. If they share a root cause I'll say so, rather than treat it as three separate jobs. After the fixes each DAG runs end to end in front of you, with a short note per DAG on what broke and what changed. Are the DAGs using DatabricksSubmitRunOperator with notebook tasks, or triggering existing jobs with DatabricksRunNowOperator? Ronak

@MrShortFlow

Profile Load, Load, and PAU DAGs failing on their Databricks notebook orchestration steps, likely due to config/parameter mismatches or checkpoint table state. Delivery: pull task logs for each DAG, trace the failing notebook task and its inputs against the PostgreSQL control/checkpoint tables, isolate root cause per DAG (config, permissions, path/param mismatch, or notebook bug), apply fixes, then trigger full end-to-end runs to confirm success. v1 within 2 days: written diagnosis of all 3 root causes plus fix plan; remaining days for implementation and confirmed green runs. I do not promise to redesign the underlying pipeline logic if failures trace to a deeper architectural issue, only fix what causes the current failures. Question: can you share Airflow and Databricks access plus the failing task logs now. Price: 12500 INR fixed · Timeline: 5 working days from access/brief Konrad

@Kimme28

**Available now to get your 3 Databricks DAGs running clean today** I can review the failure logs for your Profile Load Load and PAU DAGs and trace each root cause whether config mismatch permissions path naming parameter drift or notebook bugs I will implement fixes across Airflow and Databricks and confirm all three run end to end with your Postgres control tables updating correctly Recent work: fixed 5 failing Airflow Databricks DAGs tied to checkpoint tables for a retail client, stabilized PySpark notebook chain with XCom parameter handoff Can you share the latest task error traces so I can start with the first failing DAG today

@TechizeBuilder

Dear Client, I read "Resolve 3 Dysfunctional Airflow DAGs (Databricks)" carefully and understand you want a gaming app that's fun to play and performs smoothly on real devices. My hands-on experience with Python, SQL, PostgreSQL aligns directly with what you need. I've built games with Unity/Unreal — smooth gameplay mechanics, multiplayer/real-time sync, in-app purchases and leaderboards — optimized for performance across device tiers. A few quick questions to get us started: 1. Which genre/gameplay style are you targeting, and do you have a game design doc? 2. Single-player, multiplayer, or both? 3. Which platforms — Android, iOS, or both — and do you need monetization (ads/IAP)? Thanks & Regards, Deepak

@prasannad38

Hi, I can diagnose and fix your 3 failing Airflow DAGs (Profile Load, Load, and PAU) and get them running successfully end-to-end within 24 to 48 hours. My approach to resolve these execution failures: 1. Log & Traceback Analysis: • Review Airflow task instance logs and Databricks driver run logs to isolate whether the failure stems from parameter passing, DB connections, or notebook runtime errors. 2. State & Checkpoint Validation: • Inspect the PostgreSQL control/checkpoint table queries and schema references to ensure batch acquire and completion hooks execute cleanly. 3. Parameter & Notebook Remediation: • Patch any configuration mismatches, missing Airflow Variables/Connections, or PySpark/Python path bugs across the three notebook orchestration steps. 4. End-to-End Verification: • Trigger clean test runs across all 3 DAGs to verify the full flow (batch acquire -> processing -> batch complete) finishes without errors. I am available to start immediately upon access grant. Let’s connect in chat so you can share the log traces or grant workspace access!

@B1zt

Hi there, Three pipelines failing at once usually points at one shared cause rather than three separate bugs. They all follow the same batch acquire, process, batch complete pattern against the same control tables, so if one step writes something the next step does not expect, everything built on that pattern goes down together. What I would do: - Work through the failure logs for each of the three and pin down exactly where each stops - Check the settings and stored connections Airflow uses to reach Databricks and Postgres - Follow the values handed from one step to the next, and compare them against what the control and checkpoint tables actually hold - Fix the real causes rather than patching the symptoms, and flag anything that turns out to be a bug inside a notebook - Run all three start to finish and send you a short note on what was wrong in each INR 22,500, finished within 10 days of getting access. One question: do all three stop at the same step, or does each one break somewhere different? Thanks, Bhagya

@shailender18

Hello, I can diagnose and fix the 3 failing Airflow DAGs by tracing each execution from Airflow task logs through Databricks notebook jobs and PostgreSQL control/checkpoint tables, then validating the complete end-to-end flow. With 5+ years of Python, SQL and cloud/data engineering experience, I can investigate Airflow DAG definitions, task dependencies, Variables/Connections, parameters, permissions, notebook paths, configuration mismatches and PySpark/Python-level failures. I’ll isolate the root cause for each DAG rather than applying temporary workarounds. I’ll implement the required fixes across Airflow, Databricks notebooks or configuration as applicable, then rerun all three DAGs and verify the batch acquire → processing → batch complete sequence executes successfully with the expected checkpoint/order status. I’ll also provide a concise summary of the root cause, changes made and any configuration/deployment notes so the fixes remain maintainable. Best Regards, Shailender

@LuanCoetzee42

? I NOTICED SOMETHING IN YOUR PROJECT DESCRIPTION THAT MOST FREELANCERS WILL MISS In a recent project, I successfully debugged and fixed three failing Airflow DAGs for a major client, leading to a 100% success rate in task execution. This involved identifying configuration mismatches and notebook-level bugs, which enhanced their data pipeline efficiency significantly. I have over five years of experience with Apache Airflow, specializing in DAG debugging, task logs, and SQL-based control mechanisms. My proficiency with Databricks notebooks, particularly using PySpark and Python, enables me to troubleshoot effectively. I understand your urgency in getting these DAGs operational. My approach will include a thorough review of the failure logs, pinpointing root causes, implementing fixes, and confirming successful end-to-end runs for all three DAGs. Effective execution and clear communication are my priorities. I aim for long-term success and reliability in your data workflows. I am confident that my skills align perfectly with your needs. The difference between an average result and an exceptional one is usually decided before the work even begins. Regards, LuanCoetzee42

@abhilashr53

You need 3 broken Airflow-to-Databricks DAGs debugged and confirmed green end-to-end. Here's my approach: pull task logs for each DAG, trace failures across the batch acquire → processing → batch complete chain, check Airflow Variables/Connections, Databricks job configs, notebook parameter passing, and PostgreSQL checkpoint table reads. Fix root causes, validate each DAG runs clean. Strong fit: I've debugged Airflow/Databricks pipelines with PySpark notebooks and SQL checkpoint patterns extensively. Timeline: all 3 resolved within 2-3 days of access. Quick question: are all 3 DAGs failing at the same task stage, or at different points in the pipeline?

@pandaymano8

As a seasoned Full Stack Developer with extensive experience in both Apache Airflow and Databricks notebooks, I am confident that I am the ideal candidate for resolving the issues you're experiencing with your three disruptive DAGs. My expertise in Python will prove valuable in addressing any potential notebook-level bugs, config mismatches, or naming issues that you might be encountering along with ensuring optimum coordination between PostgreSQL-backed control/checkpoint tables and your DAG tasks Moreover, proficiency in SQL-based control/checkpoint table patterns is something that I can bring to the table seamlessly. This, combined with my problem-solving approach and attention to detail, will enable me to meticulously review the failure logs and error traces for each of your DAGs on the Airflow instance. I would then propose and implement fixes, ensuring that the updated DAGs execute successfully without any disruption. Rest assured, my solutions are always practical, user-friendly, and built-to-grow. Given my well-rounded skill set that spans from databaes to cloud computing and AI automation, I can guarantee not just a resolution for your immediate problem but also robustness for any future hurdles. Let me help your business recover swiftly and perform optimally by choosing me for this tasking project!