Anyone else find the reporting in Concur to be painfully slow with large datasets?

I’ve been running our Concur instance for a global engineering team of 500+ for the past three years, and the reporting performance has degraded from tolerable to actively obstructive. When pulling standard expense audit reports, custom cash advance reconciliations, or even basic department-level spend summaries that span multiple quarters, the system becomes unusable. We're talking about datasets that are not even that large by modern data platform standards—maybe 100,000 to 200,000 line items with attached images.

The primary pain points are consistent:

* **UI Timeouts:** The web interface will often spin for minutes before throwing a generic "request timed out" error. Refreshing or narrowing the date range feels like a guessing game.
* **Scheduled Report Delays:** Setting up a scheduled report for, say, last month's international travel should be trivial. Instead, the "generating" status can last for hours, and the promised email with the CSV often arrives long after the stakeholder meeting has ended.
* **Filtering is a Trap:** Attempting to use the built-in filters (by cost center, project code, employee) on an already-slow report seems to trigger a full re-query on the backend, multiplying the wait time. It's often faster to download the raw, unfiltered data and use `pandas` or even Excel to slice it, which defeats the purpose of a managed reporting tool.

From an infrastructure perspective, this reeks of either poor database indexing, inadequate caching layers, or both. The fact that performance scales inversely with dataset size suggests they're doing full table scans or have implemented inefficient joins between their transactional and reporting tables. There's no legitimate reason a simple aggregate SUM/GROUP BY on a few hundred thousand records should bring a system to its knees.

I’ve engaged SAP support multiple times. Their standard responses are to clear browser cache, try a different browser, or suggest we "archive old data" (which we do, aggressively). The root cause is never addressed.

My questions for the community:

* Are you experiencing similar latency with datasets >50k lines?
* Have you found any workarounds at the administrative level (e.g., specific report configurations, breaking reports into tiny pieces, using the API directly) that yield consistently better performance?
* For those who have moved away from Concur for reporting, what did you replace it with? Are you pulling data via Concur's APIs into a dedicated data warehouse (like Snowflake or BigQuery) and building reports in Looker/Tableau? If so, what does that pipeline look like, and how reliable are the syncs?

I'm particularly interested in the technical specifics of any external reporting setup. For example, a simple cron job using their `reporting/v2/` endpoints to dump data nightly into S3 for transformation.

```python
# Example of the kind of logic we're forced to implement externally
# because native reporting fails.
import requests
import csv
from datetime import datetime, timedelta

# Pseudocode: Break a quarterly report into weekly chunks to avoid timeouts
def chunk_report_concur_api(start_date, end_date):
delta = timedelta(days=7)
current_start = start_date
all_rows = []
while current_start < end_date:
current_end = min(current_start + delta, end_date)
params = {
'dateRangeStart': current_start.isoformat(),
'dateRangeEnd': current_end.isoformat(),
'limit': 1000
}
# API call here - often more stable than the UI, but still slow
response = requests.get(' https://www.concursolutions.com/api/v3.0/reporting/reports', params=params, auth=(user, pass))
data = response.json()
all_rows.extend(data['Items'])
current_start = current_end + timedelta(days=1)
return all_rows
```

This shouldn't be necessary. The tool is failing at a core function: providing timely access to financial data.