Investigating Low Data Volume In `fct_scheduled_trips`

Introduction

Hey guys, we've noticed something a bit off with our fct_scheduled_trips data, and we need to get to the bottom of it! Specifically, we've observed a drop in data volume starting in late July 2025. This is a crucial issue because the fct_scheduled_trips table is a cornerstone of our data infrastructure, providing essential information for understanding and analyzing scheduled trips. This data powers various dashboards and reports, helping us monitor transit operations, identify trends, and make informed decisions. The integrity and completeness of this data are paramount to ensuring the accuracy of our insights and the effectiveness of our analyses. So, let's dive into the details, figure out what's going on, and how we can fix it. We'll explore the symptoms, potential causes, and steps we can take to restore the data volume to its expected level. By addressing this issue promptly, we can maintain the reliability of our data and continue to provide valuable insights to our stakeholders. The following sections will guide you through the investigation process, highlighting the key findings and recommended actions. It is essential that we address this issue to ensure data quality.

The Bug: Data Volume Decline in fct_scheduled_trips

So, the main thing we're seeing is that the data volume in our fct_scheduled_trips table has taken a dip. To give you a clearer picture, we've got a dashboard (https://dashboards.calitp.org/question/3836-fct-scheduled-trips-last-90-days) that shows the trend over the last 90 days. You can easily see the decline there. This is super important because fct_scheduled_trips is a key table for us, and any drop in data volume can mess with our reporting and analysis. We rely on this table for a whole bunch of stuff, from tracking ridership to optimizing routes. If the data isn't complete, we're not getting the full picture, which can lead to some wrong decisions. What's even more interesting is that this might be linked to specific URLs. We've got another dashboard (https://dashboards.calitp.org/question/3837-fct-scheduled-trips-urls-last-90-days) that breaks down the data by URL, and it looks like some of them might be the culprits. This could mean that certain data sources are either not being processed correctly or are experiencing issues on their end. Identifying these URLs is the first step in diagnosing the problem and implementing a solution. Understanding the scope of the issue is critical for devising a targeted approach to resolve it.

How to Reproduce: Checking Row Counts by Day

Okay, so if you want to see this for yourself, it's pretty straightforward. Just head over to the Metabase questions I mentioned earlier. The key thing to look at is the count of rows by day for the fct_scheduled_trips table. This will give you a clear visual of the data volume over time. By examining this trend, you can pinpoint exactly when the decline started and how severe it has been. This is a crucial step in understanding the scope of the issue and its potential impact. We need to be able to reproduce the problem consistently to ensure that any fix we implement is effective. Here's a quick rundown of the steps: First, access the Metabase dashboard for fct_scheduled_trips (https://dashboards.calitp.org/question/3836-fct-scheduled-trips-last-90-days). Then, focus on the visualization that displays the row count per day. Look for any noticeable dips or drops in the data volume, especially around late July 2025. If you see a significant decrease, that confirms the issue we're discussing. This hands-on approach ensures that everyone on the team is on the same page and can contribute to finding a solution.

Expected Behavior: Consistent or Growing Data Volume

Ideally, guys, the fct_scheduled_trips volume should be pretty steady or even on the rise. We're always adding new URLs, so we'd expect to see the data growing over time. Of course, there might be some dips here and there if a feed gets deprecated or something, but generally, we should be seeing consistent growth. When the data volume is lower than expected, it can skew our insights and lead to inaccurate conclusions. This is why maintaining a stable data pipeline is so critical. The fct_scheduled_trips table is designed to capture all scheduled trip information, so any deviation from the expected trend needs to be investigated. We need to ensure that all feeds are being processed correctly and that there are no gaps in our data collection. By regularly monitoring the data volume, we can proactively identify and address any issues that may arise. This proactive approach helps us maintain data integrity and reliability.

Additional Context: Potential Causes and Investigation

Alright, so let's dig a bit deeper into what might be causing this. One thing that's always a possibility is that feeds are being published with future start dates. This means that they technically exist, but they won't show any activity until that future date rolls around. It's a perennial issue, and it could definitely be playing a role here. We also need to consider the possibility that some feeds aren't downloading properly, or there's some other technical glitch preventing the data from making it into our system. This is where we need to put on our detective hats and start digging into the logs and data pipelines. Another angle to explore is whether there have been any changes to the feeds themselves. Have agencies updated their data formats, or are they experiencing technical difficulties on their end? To get a clearer picture, we might want to take a look at fct_daily_schedule_feeds and other related tables. This can help us track down any specific feeds that are causing the problem. Remember, data quality is a team effort, and by working together, we can uncover the root cause of this issue. This collaborative approach ensures that we consider all possible angles and arrive at the most effective solution.

Future Start Dates: The Perennial Issue

One of the common culprits behind data discrepancies is the issue of feeds being published with future start dates. This means that while the feed is technically available, it won't contain any active trip information until the specified start date arrives. This can lead to a temporary dip in the data volume, as the system waits for the feed to become active. It's a perennial problem because it's often a result of how transit agencies schedule their data releases. Sometimes, agencies upload their schedules well in advance, but the actual trips won't occur until a later date. This discrepancy between the publication date and the effective date can cause confusion and impact data analysis. To mitigate this issue, we need to have robust processes in place to identify and handle these future-dated feeds. This might involve setting up alerts or reports to flag feeds with unusually distant start dates. We can also explore ways to filter out or postpone the processing of these feeds until they become active. By addressing this issue proactively, we can minimize its impact on our data volume and ensure that our analyses are based on accurate and timely information. This attention to detail is crucial for maintaining data integrity.

Feed Download Issues: Investigating Technical Glitches

Another potential cause for the decline in data volume could be issues with the feed download process itself. This could involve a variety of technical glitches, such as network connectivity problems, server outages, or errors in the data ingestion pipeline. If feeds are not being downloaded properly, the corresponding data will not be available in our system, leading to a decrease in the fct_scheduled_trips table. To investigate this possibility, we need to examine the logs and monitoring systems associated with our data ingestion pipeline. These logs can provide valuable insights into any errors or failures that may have occurred during the download process. We should also check the status of the servers and networks involved in data transfer to rule out any infrastructure-related issues. If we identify any specific feeds that are consistently failing to download, we can focus our efforts on those particular data sources. This might involve contacting the agency responsible for the feed or implementing changes to our download process to handle the issue. By thoroughly investigating and addressing any feed download problems, we can ensure a consistent flow of data into our system.

fct_daily_schedule_feeds and Related Tables: A Deeper Dive

To gain a more comprehensive understanding of the issue, it's crucial to explore related tables such as fct_daily_schedule_feeds. This table provides valuable information about the daily status of our schedule feeds, including details on their last update, any errors encountered, and other relevant metrics. By analyzing this data, we can identify any patterns or trends that might shed light on the decline in fct_scheduled_trips volume. For example, we can check if there's been a recent increase in the number of feeds failing to update or experiencing errors. We can also compare the data in fct_daily_schedule_feeds with the data in fct_scheduled_trips to identify any specific feeds that are contributing to the problem. This cross-referencing of data can help us pinpoint the root cause and develop targeted solutions. In addition to fct_daily_schedule_feeds, we might also want to examine other related tables that provide information about data quality and feed health. This holistic approach ensures that we consider all available data points and make informed decisions. This comprehensive analysis is vital for effective troubleshooting and problem-solving.

Conclusion: Next Steps and Collaboration

So, guys, we've covered a lot of ground here. We've identified the issue, looked at ways to reproduce it, discussed potential causes, and explored additional context. The next step is to roll up our sleeves and start investigating those potential causes. We need to dive into the logs, check the feed statuses, and maybe even reach out to some agencies if necessary. This is a team effort, and the more eyes we have on this, the faster we'll get it sorted. Remember, data is the lifeblood of our operations, and we need to keep it flowing smoothly. By working together and staying focused, we can resolve this issue and ensure that our data remains accurate and reliable. This collaborative approach is key to maintaining the quality of our data and the effectiveness of our analyses. Let's keep the lines of communication open and share any findings or insights as we progress. Together, we can tackle this challenge and ensure the continued integrity of our data ecosystem.