Yardi Data Export Automation: Scheduled Reports and ETL

Yardi data export automation works on the tools Yardi actually exposes: scheduled reports, file exports, and SFTP delivery. Our standard pattern ingests Voyager exports on a schedule, normalizes fixed-width and CSV formats, and loads a warehouse for dashboards and alerts. This guide shows how we build it and where it gets tricky.

Rex Automaton is an AI implementation agency that builds and runs this kind of system for mid market operators.

We build Yardi reporting pipelines on this file-first pattern for property operators.

Yardi reporting automation is: a scheduled file-based pipeline that uses Yardi Report Scheduler or Task Runner to generate exports, moves them via SFTP, and loads cleaned data into a database for BI and operational alerts.

The problem it solves

Most teams try to wire Yardi like a modern SaaS and hit walls. Yardi's integration posture is partner-gated and file-first. There is no public self-serve API onboarding for Voyager or Breeze, and there is no documented webhook stream. You get scheduler-driven reports, text or CSV files, and SFTP delivery. That is enough to automate reporting when you embrace it as a batch ETL instead of fighting for real time.

How the automation works

We do not assume a public API. We lean on Yardi's documented export and scheduling features, then build a robust post-export pipeline.

• Report generation: Yardi Voyager supports CSV, variable-length ASCII, and fixed-length text exports. Report Scheduler can run reports on a schedule, and Yardi Task Runner is used to automate or batch ETL jobs. The output lands in a folder or is sent via SFTP.
• Secure file movement: an SFTP drop is the handoff. We pull new files, verify naming and date windows, and quarantine anything out of spec.
• Parse and normalize: fixed-width layouts get schemas with explicit column widths. CSVs get column-name normalization and dtype maps. We enrich with property and chart-of-accounts dimensions outside of Yardi when needed.
• Load to a warehouse: we stage raw rows, dedup with composite keys, and upsert to curated tables for BI tools or finance packets.
• Alerts and BI: variance checks raise alerts, and dashboards refresh off the curated tables. If Yardi adds Data Connect or Replicate later, we can swap the source and keep the same downstream.

Yardi scheduled reports: what makes them safe to automate against

If you searched for Yardi scheduled reports, the useful question is not only how to schedule one, but how to schedule it so a downstream pipeline can trust it. A scheduled report is only automatable if the file shows up predictably, is named predictably, and means the same thing every run. We check these points with whoever owns the Yardi schedule before writing any parser.

• One owner per schedule: decide whether accounting (Report Scheduler) or IT (Task Runner) owns timing, so changes are not made in two places.
• Fixed filename policy: report, entity, and date in the name, so the ingestor can validate the window without opening the file.
• Late enough to include postings: run after overnight activity is posted, and rerun after month-end corrections.
• Entity or property range in the output: this prevents cross-entity bleed when one drop serves several portfolios.
• Frozen report definition: treat any edit to a scheduled report as a layout change, because it can shift columns or widths.
• Failure signal: agree on what happens when a file does not arrive, because an absent file is the most common silent failure.

Scheduled report readiness checklist
1. Report name, owner, and schedule recorded
2. Output format confirmed (CSV, variable-length ASCII, or fixed-length text)
3. SFTP target and filename policy agreed
4. Layout version assigned (v1, v2, ...)
5. Expected arrival window and missing-file alert defined

Yardi ETL: how do you extract Voyager data without an API?

If you searched for Yardi ETL or how to automate Yardi Voyager data extraction, the short answer is: schedule the export inside Yardi, deliver it to SFTP, and run the transform and load yourself. The table shows the extraction options we weigh, and what we can and cannot state about each.

The design rule: keep the downstream warehouse and BI layer independent of the source. Then the extraction method can change without rework.

Yardi custom reporting and Data Connect: what to ask

Two search terms come up often: Yardi custom reporting and Yardi Data Connect. We do not state product specifics we cannot verify, so here is how we handle both in practice.

• Custom reporting: build or adjust the report definition inside Yardi, schedule it, and deliver it to SFTP. Our loader treats each custom report as a versioned layout (column widths for fixed-width, dtype map for CSV). The reporting itself then happens in your BI tool against curated tables, so accounting can change presentation names without touching Yardi.
• Data Connect: if your Yardi contract includes it, ask your Yardi representative exactly what data it exposes, how access is granted, and how often it refreshes. Our pipeline treats it as one more source feeding the same staging tables. If it is not licensed, the scheduled file export covers the same reporting need.

Which Yardi reports should you export first

Start with the report families that unlock daily operations and owner packets. Prefer CSV when available, otherwise document fixed-width layouts.

• Rent roll: daily CSV. Keys: property_id, unit, tenant_code, as_of. Uses curated.rent_roll.
• Delinquency or AR aging: daily fixed-width or CSV. Keys: property_id, as_of, tenant_code. Uses curated.delinquency.
• GL trial balance: monthly CSV. Keys: org, period, account, subaccount. Uses curated.gl_trial_balance.
• AP open items: weekly CSV. Keys: vendor_id, invoice_no, period. Feeds AP packet checks.
• Bank activity or cash summary: daily or weekly CSV. Feeds cash variance alerts.

If you need a comparison playbook for similar stacks, see how we structure Power BI models in our AppFolio to Power BI guide and how we run report-driven pipelines in other systems in our Buildium reports integration post.

Suggested initial schedule window
- Daily core: rent roll, delinquency, cash summary 06:30-07:00 local
- Weekly: AP open items every Monday 05:30
- Month-end: GL trial balance day 1 04:30, rerun day 3 after late postings

Yardi Report Scheduler vs Task Runner: when to use each

Both generate files. Choose based on how your environment is set up and who operates it.

• Report Scheduler: best for business-owned cadence. Configure per report, send to SFTP, simple to reason about. Use when accounting owns timing and scope.
• Task Runner: best for IT-owned batches. Chain multiple jobs, coordinate dependencies, and land files in one atomic window. Use when you need strict ordering or a single daily cut.

Tip: whichever you use, align on filename policy and retention. Then your downstream code stays identical.

Step-by-step: how to build it

1) How do we schedule the Yardi exports?

Use Yardi's Report Scheduler to run the reports you need and deliver files to an SFTP location your IT team controls. If your environment uses Task Runner, have it generate and transfer the files on the same cadence. Name files predictably so we can validate windows.

Key gotcha: schedule late enough that overnight postings are included, and include the accounting entity or property range in the output to avoid cross-entity bleed.

Example report names and cadence
- rent_roll_yyyyMMdd.csv           daily 06:30
- delinquency_yyyyMMdd.txt        daily 06:35 (fixed-width)
- gl_trial_balance_yyyyMM.csv     monthly day 1 04:30
SFTP path: /exports/yardi/prod/
Filename policy: <report>_<entity>_<yyyymmdd>.ext

2) How do we pull files from SFTP safely?

We run a small Python job every 10 to 15 minutes during the window. It lists the SFTP directory, matches new files, atomically downloads, and moves processed files to an archive folder. We treat partial transfers as quarantined.

import os, re, time, hashlib
import paramiko
from pathlib import Path
 
SFTP_HOST = os.getenv("SFTP_HOST")
SFTP_USER = os.getenv("SFTP_USER")
SFTP_PKEY = os.getenv("SFTP_PKEY")
REMOTE_DIR = "/exports/yardi/prod"
ARCHIVE_DIR = "/exports/yardi/archive"
LOCAL_INBOX = "/data/inbox"
FILE_RE = re.compile(r"^(rent_roll|delinquency|gl_trial_balance)_.+\.(csv|txt)$")
 
def connect():
    key = paramiko.RSAKey.from_private_key_file(SFTP_PKEY)
    t = paramiko.Transport((SFTP_HOST, 22))
    t.connect(username=SFTP_USER, pkey=key)
    return paramiko.SFTPClient.from_transport(t)
 
def safe_get(sftp, remote_path):
    local_tmp = Path(LOCAL_INBOX) / (Path(remote_path).name + ".part")
    local_final = Path(LOCAL_INBOX) / Path(remote_path).name
    sftp.get(remote_path, str(local_tmp))
    # simple integrity guard: nonzero and stable size
    if local_tmp.stat().st_size == 0:
        raise RuntimeError("empty transfer")
    local_tmp.replace(local_final)
    return local_final
 
def main():
    sftp = connect()
    for fname in sftp.listdir(REMOTE_DIR):
        if not FILE_RE.match(fname):
            continue
        rpath = f"{REMOTE_DIR}/{fname}"
        lpath = safe_get(sftp, rpath)
        sftp.rename(rpath, f"{ARCHIVE_DIR}/{fname}")
        print("downloaded", lpath)
    sftp.close()
 
if __name__ == "__main__":
    main()

Gotcha: do not process files in place. Always download to .part, then rename. Treat zero-byte or mid-transfer files as quarantined.

3) How do we parse fixed-width and CSV exports?

Voyager exports include fixed-length text and CSV. We keep layout definitions in versioned JSON and use explicit widths. CSVs get dtype maps to prevent date and account code coercion.

import pandas as pd
from pandas.api.types import CategoricalDtype
 
# fixed-width layout for delinquency v1
FW_LAYOUT_V1 = {
    "widths": [10, 12, 30, 12, 12, 10],
    "cols":   ["property_id", "tenant_code", "tenant_name", "current", "over_30", "over_60"]
}
 
def parse_delinquency_fixed(path, layout=FW_LAYOUT_V1):
    df = pd.read_fwf(path, widths=layout["widths"], names=layout["cols"], dtype=str)
    for c in ["current", "over_30", "over_60"]:
        df[c] = (df[c].str.replace(",", "").str.strip().replace({"": "0"}).astype(float))
    df["load_date"] = pd.Timestamp.utcnow().date()
    return df
 
def parse_rent_roll_csv(path):
    dtypes = {
        "property_id": str,
        "unit": str,
        "tenant_code": str,
        "lease_start": str,
        "lease_end": str,
        "rent_amount": "float64"
    }
    df = pd.read_csv(path, dtype=dtypes, keep_default_na=False)
    # normalize headers
    df.columns = [c.strip().lower().replace(" ", "_") for c in df.columns]
    return df

Gotcha: fixed-width formats drift across environments. Version your layouts and add smoke tests that verify column sums and row counts by property.

4) How do we stage and upsert to a warehouse?

We load to staging tables 1:1 with the file, then upsert to curated tables with a composite key that matches how the report is identified in your finance process.

from sqlalchemy import create_engine, text
import pandas as pd
 
PG_DSN = os.getenv("PG_DSN")
engine = create_engine(PG_DSN)
 
def stage(df: pd.DataFrame, table: str):
    df.to_sql(table, engine, if_exists="append", index=False, method="multi", chunksize=1000)
 
UPSERT_SQL = """
insert into curated.rent_roll as t(
  property_id, unit, tenant_code, lease_start, lease_end, rent_amount, as_of
)
select property_id, unit, tenant_code, lease_start::date, lease_end::date, rent_amount, as_of::date
from staging.rent_roll
on conflict (property_id, unit, tenant_code, as_of)
  do update set
    lease_start = excluded.lease_start,
    lease_end   = excluded.lease_end,
    rent_amount = excluded.rent_amount,
    updated_at  = now();
"""
 
def upsert_curated():
    with engine.begin() as cx:
        cx.execute(text(UPSERT_SQL))

Gotcha: choose keys that match the accounting grain. For example, rent roll is usually keyed by property, unit, tenant, and as-of date. GL lines are keyed by org, period, account, subaccount, and source key.

5) How do we validate and alert on variance?

We run a set of checks after each load: row count by property, total balances by period, and monotonic trends where applicable. If something is off, we fail the run and notify.

/* example: total AR should equal the sum of aging buckets */
select p.property_id,
       sum(current + over_30 + over_60) as calc_ar,
       sum(total_ar_reported)           as reported_ar
from curated.delinquency p
where as_of = current_date
group by 1
having sum(current + over_30 + over_60) != sum(total_ar_reported);

Gotcha: do not auto-correct mismatches from files. Alert and quarantine. The source of truth is the export.

6) How do we surface it to BI and packets?

Point your BI tool at the curated schema and assemble owner or asset packets from those tables. If Yardi Data Connect or Replicate is licensed later, we can swap the source and keep the same curated layer and visuals.

BI model
- curated.rent_roll             property, unit, tenant, rent
- curated.delinquency           property aging buckets per day
- curated.gl_trial_balance     org, period, account balances
- dims.property                 metadata and groupings
- dims.coa                      rollups and presentation names

Gotcha: keep presentation names outside the loader so accounting can change them without a deploy.

Where it gets complicated

• API access is gated. Yardi positions a Commercial API behind a partner program. There is no public developer portal with self-serve onboarding. When read APIs are required, the path is the standard interface partnership route, otherwise we stay with file-based ETL.
• Real time is not the default. There is no documented webhook system. We design near real time with frequent scheduler runs where appropriate, or batch by day for finance-grade reliability.
• Fixed-width drift across tenants. Delinquency and trial balance widths can vary between environments. We version column layouts and ship smoke tests so a silent drift does not poison the warehouse.
• Header and type drift in CSV. The same report can add or rename columns after a Yardi upgrade. We normalize known names, keep optional columns nullable, and alert on unexpected additions.
• SFTP hygiene matters. Atomic downloads and an archive move are mandatory. Processing files in place will eventually bite you.
• Month-end edge cases. Late postings and corrections require upserts, not inserts. We include a backfill window job for the first three business days after close.

What this actually changes

The goal of this pattern is to eliminate manual report runs and spreadsheet merges. Accounting and asset teams get daily dashboards and scheduled packets that reflect the same controlled source every time. The pipeline respects Yardi's supported surface and avoids brittle screen-scrapes or undocumented calls, so it is designed to keep running through upgrades.

One reason this matters: Gartner estimated that poor data quality costs organizations an average of 12.9 million dollars per year, per Gartner's data quality overview.

When exports flow on time, parse deterministically, and land in a curated model, downstream teams stop reconciling spreadsheets and start acting on consistent numbers. If you want to see the same curated-layer pattern in an adjacent stack, review our AppFolio to Postgres sync, our AppFolio to BigQuery reporting sync, and how we translate it to Power BI in our AppFolio to Power BI guide.

Frequently asked questions

Does Yardi have an official API?

Yardi lists a Commercial API for exporting commercial data from Voyager that is generally accessed through its partner program. There is no public developer portal with self-serve onboarding. We either go through the partner path for read access or rely on scheduled file exports when an API is not in scope.

What is Yardi Data Connect, and do I need it?

We do not assume Data Connect is available in your environment, and we do not state its specifics without confirmation from Yardi. Ask your Yardi representative what it exposes and how it is licensed. If it is not available, scheduled exports plus SFTP cover recurring reporting, and the same warehouse can accept Data Connect later.

How do I do Yardi ETL without an API?

Schedule reports in Report Scheduler or Task Runner, deliver them to SFTP, then parse, validate, and upsert into a warehouse. The Yardi side only produces files. Everything after the file drop is ordinary ETL, which means you can test it, version it, and monitor it like any other pipeline.

How do I automate Yardi Voyager data extraction?

Schedule the reports you need, deliver the files to SFTP, and let an ingestor pick them up, validate filenames and date windows, and load a warehouse. The extraction itself stays inside Yardi's supported export features, so there is nothing to scrape and nothing undocumented to break after an upgrade.

Can I connect Yardi to Zapier or Make?

Zapier has a native app for Yardi Kube. We have not seen native Zapier or Make apps for Voyager or Breeze. When a no-code connector is not available, we bridge with Report Scheduler, SFTP delivery, and a lightweight ETL service so your data still lands in Sheets, a warehouse, or your CRM.

Can this run in real time?

Yardi does not document webhooks. We design near real time by running the scheduler more frequently or by polling the SFTP drop within a window. For finance and owner packets, daily batch is often the right tradeoff. If your environment adds Data Connect or Replicate, we can tighten freshness without changing downstream models.

What does IT need to provide?

An SFTP endpoint or shared drop, service credentials, report definitions with their schedules, and outbound allowlists for the ETL host. We also align on filename policy, retention, and a safe place to archive processed files for audit.

How long does the first pipeline take to ship?

A typical first pipeline ships in two to four weeks: week 1 map reports and schedule, week 2 parser and staging, week 3 curated model and BI, week 4 variance checks and handoff. We start with one report family, then add others incrementally.

Can this push back into Yardi?

The automation here is read-oriented by design. When write-back is required, Yardi's official path emphasizes file-based ETL and partner interfaces. We scope that separately and only with supported methods.

What is Yardi data export automation?

It is a file-first integration that schedules Voyager reports with Report Scheduler or Task Runner, delivers them to SFTP, parses fixed-width or CSV formats, and loads a warehouse for BI and alerts. No public webhooks are required, which keeps the pipeline stable through upgrades.

What is Yardi reporting in this context?

Yardi reporting means producing recurring, validated outputs like rent roll, delinquency, and trial balance on a schedule. We standardize filenames and schema, upsert into curated tables, and feed dashboards and packets reliably. See a comparable approach for other PMS stacks in our Rent Manager integration guide and our Buildium reports post.

If you are on Yardi and want reporting that arrives on time without manual spreadsheets, this is the file-first pattern we build. See our related deep dive on AppFolio's surface in our AppFolio API guide, then map the same approach to Yardi's scheduler. To discuss your environment and constraints, read our notes on custom AI integration and book a 20 minute call.