Every Fortnite season list on the internet looks the same: a long table, a handful of ads, and a date range you have to scroll past twenty times to find. If all you want is a quick reference, our own season tracker build or the chronological season archive already covers that. This tutorial is for something else. That works fine if you want one fact. It falls apart the moment you want to ask something slightly harder, like “which season ran the longest” or “how many days on average does Epic Games give a Battle Pass before rotating it out.” This tutorial skips the static list and builds the thing behind it instead: a real, queryable database of every Fortnite chapter and season, wrapped in a small REST API you can run on your own machine in under an hour.

By the end you will have a SQLite database seeded with 41 real season records pulled from Chapter 1’s Pre-Season in September 2017 through Chapter 7 Season 4: Override, which Epic launched on August 20, 2026 and is still live as of this writing on September 27, 2026. You will also have a command-line tool and a FastAPI web server that can answer questions like “what season was live on March 1, 2021” in milliseconds, instead of forcing you to eyeball a spreadsheet.

Why a Season List Isn’t the Same as a Season Database

A blog table is a snapshot. A database is a tool. The difference matters once you start building anything on top of Fortnite season history, whether that is a Discord bot that announces new Battle Passes, a stat tracker that cross-references match history against season windows, or a simple personal project to practice backend skills with real, messy data instead of another to-do list app.

Fortnite’s own season calendar is a genuinely good practice dataset for this. It has irregular durations (28 days for the Chapter 2 Remix event, 128 days for Chapter 2 Season 1), inconsistent naming conventions across chapters, at least one crossover event that does not fit neatly into the “season” column, and a live, currently-running record with no confirmed end date yet. That is closer to what real production data looks like than a tidy tutorial dataset with clean, evenly spaced rows.

There is also a practical reason to prefer a local database over repeatedly checking a website: latency and reliability. A SQLite file on your own disk answers a query in well under a millisecond and works offline. A community tracker page depends on that site staying up, staying unblocked by your network, and not changing its table layout the next time it gets redesigned. None of that is a knock on the sites doing this work well today, it is just a different tradeoff, and for a project you plan to build other tools on top of, owning the data outright is usually the better call.

Epic Games does not publish an official, documented public API that returns the full historical season calendar. What exists instead is a small ecosystem of community-run services, most prominently fortnite-api.com and the cosmetic and season database at fnbr.co, plus wiki-maintained tables. None of them agree perfectly on start and end times, partly because Epic sometimes rolls out a new season at slightly different times across regions and platforms. Building your own local database, sourced once and version-controlled, solves that problem for your own project: you get one source of truth you control, instead of five sources that quietly disagree.

Prerequisites: What You Need Before You Start

This project uses Python’s built-in sqlite3 module for storage, so there is no database server to install. The API layer runs on FastAPI with Uvicorn as the server. Every version below was confirmed available on PyPI at the time of writing.

ToolVersion used in this tutorialPurpose
Python3.12 or newerRuns every script and the API
sqlite3 moduleBundled with Python (no install)Stores the season dataset
fastapi0.141.1Serves the REST endpoints
uvicorn0.54.0ASGI server that runs FastAPI
requests2.34.2Optional, for pulling live data from third-party APIs
A terminalBash, zsh, or PowerShellRuns the setup commands

You do not need prior SQL experience. You do need basic comfort with running Python scripts from a terminal and editing plain text files. Total build time, including testing, runs about 60 minutes if you copy the code blocks directly and closer to 90 if you type everything by hand.

Step 1 and 2: Set Up Your Project and Install Dependencies

Start with a clean folder and an isolated virtual environment. Skipping the virtual environment is the single most common reason these kinds of tutorials break for people a week later, because a system-wide package upgrade silently changes behavior underneath a project that has no pinned environment of its own.

mkdir fortnite-season-db
cd fortnite-season-db
python3 -m venv venv
source venv/bin/activate   # on Windows: venv\Scripts\activate

pip install fastapi==0.141.1 uvicorn==0.54.0 requests==2.34.2

Create three empty files now so the project structure is visible from the start: schema.sql, seed_data.py, query.py, and api.py. You will fill each one in over the next several steps. Keeping the seeding logic, the query logic, and the API logic in separate files makes it much easier to test each layer on its own before wiring them together.

Step 3: Design the Fortnite Season Database Schema

A season record needs six fields to be genuinely useful: a stable ID, the chapter label, the season label, the display name Epic gave the season, and two dates. Store dates as ISO 8601 strings (YYYY-MM-DD). According to the official SQLite documentation, the engine has no native date type, and ISO strings sort correctly as plain text, which saves you from writing custom comparison logic later.

-- schema.sql
CREATE TABLE IF NOT EXISTS seasons (
    id INTEGER PRIMARY KEY AUTOINCREMENT,
    chapter TEXT NOT NULL,
    season_label TEXT NOT NULL,
    display_name TEXT NOT NULL,
    start_date TEXT NOT NULL,
    end_date TEXT,
    is_current INTEGER NOT NULL DEFAULT 0
);

CREATE INDEX IF NOT EXISTS idx_seasons_chapter ON seasons(chapter);
CREATE INDEX IF NOT EXISTS idx_seasons_start_date ON seasons(start_date);

Two design choices matter here. First, end_date is nullable, because the currently live season does not have a confirmed end date until Epic ships the next update. Forcing a fake date into that column would corrupt every duration calculation downstream. Second, the two indexes exist because you will frequently filter by chapter and sort by date once the dataset grows past a few dozen rows, and an unindexed table forces SQLite to scan every row for those lookups.

Step 4: Create the SQLite Database

With the schema written, generating the actual database file is a one-line operation from the terminal, or three lines from Python if you want it scripted as part of a larger setup routine.

sqlite3 fortnite_seasons.db < schema.sql

# verify the table exists
sqlite3 fortnite_seasons.db ".tables"

Output example:

seasons

If that command prints nothing, or errors with command not found, see the troubleshooting section further down. Most systems ship with a sqlite3 CLI already installed alongside Python, but not all of them do.

The Complete Fortnite Chapter and Season Dataset

This is the data you are about to load. It covers every numbered season, the Pre-Season, Chapter 1's "Season X," the Chapter 4 "Fortnite OG" bonus season, and the Chapter 2 Remix crossover event, through Chapter 7 Season 4: Override, which began August 20, 2026. Durations are calculated in days, not estimated.

One honest caveat before you rely on this for anything precise: community trackers do not fully agree on the window between Chapter 6 Season 3 ("Galactic Battle," which ended June 7, 2025) and Chapter 6 Season 4 ("Shock 'N Awesome," which started August 7, 2025). That 61-day gap likely reflects an unlisted mid-chapter event or an update Epic did not brand as a full numbered season, and sources vary on how they log it. The dataset below uses the most consistently reported dates across trackers rather than guessing at a fix.

ChapterSeasonStart dateEnd dateDays
Pre-SeasonSeason 02017-09-122017-10-2543
Chapter 1Season 1: First Steps2017-10-262017-12-1348
Chapter 1Season 2: Fort Knights2017-12-142018-02-2169
Chapter 1Season 3: Meteor Strike2018-02-222018-04-3067
Chapter 1Season 4: Brace for Impact2018-05-012018-07-1272
Chapter 1Season 5: Worlds Collide2018-07-122018-09-2777
Chapter 1Season 6: Darkness Rises2018-09-272018-12-0569
Chapter 1Season 7: You Better Watch Out2018-12-062019-02-2884
Chapter 1Season 8: X Marks the Spot2019-02-282019-05-0970
Chapter 1Season 9: The Future Is Yours2019-05-092019-08-0184
Chapter 1Season X: Out of Time2019-08-012019-10-1373
Chapter 2Season 1: New World2019-10-152020-02-20128
Chapter 2Season 2: Top Secret2020-02-202020-06-17118
Chapter 2Season 3: Splashdown2020-06-172020-08-2771
Chapter 2Season 4: Nexus War2020-08-272020-12-0196
Chapter 2Season 5: Zero Point2020-12-022021-03-15103
Chapter 2Season 6: Primal2021-03-162021-06-0783
Chapter 2Season 7: Invasion2021-06-082021-09-1296
Chapter 2Season 8: Cubed2021-09-132021-12-0482
Chapter 3Season 1: Flipped2021-12-052022-03-19104
Chapter 3Season 2: Resistance2022-03-202022-06-0476
Chapter 3Season 3: Vibin'2022-06-052022-09-17104
Chapter 3Season 4: Paradise2022-09-182022-12-0376
Chapter 4Season 1: A New Beginning2022-12-042023-03-0894
Chapter 4Season 2: Mega2023-03-102023-06-0890
Chapter 4Season 3: Wilds2023-06-092023-08-2476
Chapter 4Season 4: Last Resort2023-08-252023-11-0269
Chapter 4Season OG: Fortnite OG2023-11-032023-12-0229
Chapter 5Season 1: Underground2023-12-032024-03-0896
Chapter 5Season 2: Myths & Mortals2024-03-082024-05-2477
Chapter 5Season 3: Wrecked2024-05-242024-08-1583
Chapter 5Season 4: Absolute Doom2024-08-162024-11-0278
Chapter 2 RemixRemix (crossover event)2024-11-022024-11-3028
Chapter 6Season 1: Hunters2024-12-012025-03-0291
Chapter 6Season 2: Lawless2025-03-022025-05-0261
Chapter 6Season 3: Galactic Battle2025-05-022025-06-0736
Chapter 6Season 4: Shock 'N Awesome2025-08-072025-11-29114
Chapter 7Season 1: Pacific Break2025-11-292026-03-19110
Chapter 7Season 2: Showdown2026-03-192026-06-0578
Chapter 7Season 3: Super2026-06-052026-08-1975
Chapter 7Season 4: Override (current, live)2026-08-20Scheduled ~2026-11-0173*

*The final row's duration is projected, not observed, since Season 4 is still live as of September 27, 2026. Everything else in the table is a completed, historical season.

Step 5: Load the Dataset Into SQLite

Rather than hand-writing 41 INSERT statements, store the data as a Python list of tuples and loop over it with executemany. This is faster to maintain and makes it trivial to add a new row the next time Epic ships a season.

# seed_data.py
import sqlite3

SEASONS = [
    ("Pre-Season", "Season 0", "Season 0", "2017-09-12", "2017-10-25", 0),
    ("Chapter 1", "Season 1", "First Steps", "2017-10-26", "2017-12-13", 0),
    ("Chapter 1", "Season 2", "Fort Knights", "2017-12-14", "2018-02-21", 0),
    # ... add every row from the dataset table above ...
    ("Chapter 7", "Season 3", "Super", "2026-06-05", "2026-08-19", 0),
    ("Chapter 7", "Season 4", "Override", "2026-08-20", None, 1),
]

def seed():
    conn = sqlite3.connect("fortnite_seasons.db")
    cur = conn.cursor()
    cur.executemany(
        """INSERT INTO seasons
           (chapter, season_label, display_name, start_date, end_date, is_current)
           VALUES (?, ?, ?, ?, ?, ?)""",
        SEASONS,
    )
    conn.commit()
    print(f"Inserted {cur.rowcount if cur.rowcount != -1 else len(SEASONS)} rows")
    conn.close()

if __name__ == "__main__":
    seed()

Run it with python3 seed_data.py. Note the last row: end_date is None, which SQLite stores as NULL, and is_current is set to 1. That flag is what lets your API answer "what season is live right now" without doing date-range math every single request.

$ python3 seed_data.py
Inserted 41 rows

Step 6: Write Reusable Query Functions

Keep every raw SQL statement in one file. This is the layer both the CLI tool and the API will call into, so any bug you fix here fixes it everywhere at once instead of in two places separately.

# query.py
import sqlite3

DB_PATH = "fortnite_seasons.db"

def get_connection():
    conn = sqlite3.connect(DB_PATH)
    conn.row_factory = sqlite3.Row
    return conn

def get_all_seasons():
    with get_connection() as conn:
        rows = conn.execute("SELECT * FROM seasons ORDER BY start_date").fetchall()
        return [dict(r) for r in rows]

def get_current_season():
    with get_connection() as conn:
        row = conn.execute("SELECT * FROM seasons WHERE is_current = 1").fetchone()
        return dict(row) if row else None

def get_season_on_date(target_date):
    query = """
        SELECT * FROM seasons
        WHERE start_date <= ?
          AND (end_date IS NULL OR end_date >= ?)
        ORDER BY start_date DESC LIMIT 1
    """
    with get_connection() as conn:
        row = conn.execute(query, (target_date, target_date)).fetchone()
        return dict(row) if row else None

def get_seasons_by_chapter(chapter):
    with get_connection() as conn:
        rows = conn.execute(
            "SELECT * FROM seasons WHERE chapter = ? ORDER BY start_date",
            (chapter,),
        ).fetchall()
        return [dict(r) for r in rows]

Notice conn.row_factory = sqlite3.Row. Without it, SQLite returns plain tuples, and you would be reading data back by numeric index (row[3]) instead of by column name, which turns into an unreadable mess the moment you add or reorder a column.

Step 7: Build a Command-Line Lookup Tool

Before wrapping anything in a web server, prove the query layer works from a plain terminal command. This step also gives you a genuinely useful standalone tool even if you never build the API part.

# cli.py
import argparse
from query import get_current_season, get_season_on_date, get_seasons_by_chapter

def main():
    parser = argparse.ArgumentParser(description="Look up Fortnite season history")
    parser.add_argument("--current", action="store_true", help="Show the live season")
    parser.add_argument("--on-date", metavar="YYYY-MM-DD", help="Season live on a date")
    parser.add_argument("--chapter", metavar="NAME", help="List seasons in a chapter")
    args = parser.parse_args()

    if args.current:
        print(get_current_season())
    elif args.on_date:
        result = get_season_on_date(args.on_date)
        print(result if result else "No season found for that date")
    elif args.chapter:
        for season in get_seasons_by_chapter(args.chapter):
            print(f"{season['season_label']}: {season['display_name']}")
    else:
        parser.print_help()

if __name__ == "__main__":
    main()

Test it against a date you already know the answer to, like the day Chapter 2 launched:

$ python3 cli.py --on-date 2019-11-01
{'id': 12, 'chapter': 'Chapter 2', 'season_label': 'Season 1', 'display_name': 'New World', 'start_date': '2019-10-15', 'end_date': '2020-02-20', 'is_current': 0}

If that returns the right season, your schema, seed data, and query logic are all correct and you are ready to expose them over HTTP.

Step 8: Build a FastAPI Server to Expose the Data

FastAPI turns each of the functions from Step 6 into an HTTP endpoint with almost no extra code, and it generates interactive documentation automatically at /docs, which is worth pointing out to anyone new to the framework since it removes an entire category of "how do I test this" confusion.

# api.py
from fastapi import FastAPI, HTTPException
from query import get_all_seasons, get_current_season, get_season_on_date, get_seasons_by_chapter

app = FastAPI(title="Fortnite Season API", version="1.0.0")

@app.get("/seasons")
def list_seasons():
    return get_all_seasons()

@app.get("/seasons/current")
def current_season():
    season = get_current_season()
    if not season:
        raise HTTPException(status_code=404, detail="No current season flagged")
    return season

@app.get("/seasons/on/{target_date}")
def season_on_date(target_date: str):
    season = get_season_on_date(target_date)
    if not season:
        raise HTTPException(status_code=404, detail="No season found for that date")
    return season

@app.get("/seasons/chapter/{chapter}")
def seasons_by_chapter(chapter: str):
    return get_seasons_by_chapter(chapter)

Start it with Uvicorn:

uvicorn api:app --reload --port 8000

Open http://127.0.0.1:8000/docs in a browser and you get a full interactive test console for every route without writing a single line of frontend code.

EndpointMethodReturns
/seasonsGETEvery season in the database, oldest first
/seasons/currentGETThe season currently flagged as live
/seasons/on/2021-06-01GETWhichever season was running on a given date
/seasons/chapter/Chapter%207GETAll seasons within one chapter

Step 9: Add a Duration and Analytics Endpoint

This is where a database starts paying off compared to a static page. Season length across Fortnite's history is genuinely uneven, and an analytics endpoint can compute that on demand instead of you recalculating it by hand every time someone asks.

# add to query.py
from datetime import date

def get_duration_stats():
    with get_connection() as conn:
        rows = conn.execute(
            "SELECT season_label, display_name, start_date, end_date FROM seasons WHERE end_date IS NOT NULL"
        ).fetchall()

    durations = []
    for r in rows:
        start = date.fromisoformat(r["start_date"])
        end = date.fromisoformat(r["end_date"])
        durations.append({
            "label": f"{r['season_label']}: {r['display_name']}",
            "days": (end - start).days,
        })

    longest = max(durations, key=lambda d: d["days"])
    shortest = min(durations, key=lambda d: d["days"])
    average = round(sum(d["days"] for d in durations) / len(durations), 1)

    return {"average_days": average, "longest": longest, "shortest": shortest, "count": len(durations)}
# add to api.py
from query import get_duration_stats

@app.get("/seasons/stats")
def duration_stats():
    return get_duration_stats()

Running this against the full 40 completed seasons in the dataset returns an average season length of 79.3 days, with Chapter 2 Season 1: New World as the longest completed season at 128 days, and the Chapter 2 Remix crossover event as the shortest at 28 days. Those numbers update automatically the moment you add a new row, which is the entire point of doing this as a database instead of a document.

Step 10: Test the Full API With curl and Python

With the server running, hit each route from a second terminal window to confirm everything responds correctly before you consider the build finished.

curl http://127.0.0.1:8000/seasons/current
curl http://127.0.0.1:8000/seasons/stats
curl "http://127.0.0.1:8000/seasons/on/2026-09-27"

Expected output for the date lookup, since September 27, 2026 falls inside Chapter 7 Season 4:

{"id":41,"chapter":"Chapter 7","season_label":"Season 4","display_name":"Override","start_date":"2026-08-20","end_date":null,"is_current":1}

If you would rather script the test instead of typing curl commands by hand, a short Python check works just as well and is easier to drop into a CI pipeline later:

import requests

resp = requests.get("http://127.0.0.1:8000/seasons/current")
assert resp.status_code == 200
assert resp.json()["chapter"] == "Chapter 7"
print("API smoke test passed")

Step 11 and 12: Keep the Database Current and Run the Full Project

Every Fortnite season eventually ends, which means this database goes stale unless you update it. Add two rows every time Epic transitions seasons: close out the old row with a real end_date and flip is_current to 0, then insert the new season with is_current set to 1 and end_date left as NULL. If you want a head start on what that next row might look like before Epic confirms it, our season-prediction build walks through the signals worth tracking.

# update_season.py
import sqlite3
conn = sqlite3.connect("fortnite_seasons.db")

conn.execute("UPDATE seasons SET end_date = ?, is_current = 0 WHERE is_current = 1",
             ("2026-11-01",))
conn.execute(
    """INSERT INTO seasons (chapter, season_label, display_name, start_date, end_date, is_current)
       VALUES (?, ?, ?, ?, NULL, 1)""",
    ("Chapter 7", "Season 5", "TBA", "2026-11-01"),
)
conn.commit()
conn.close()

To finish the build, run all three components together: seed the database if you have not already, start the API, and confirm the CLI and the web server both return the same answer for the same query. That agreement is your final proof the whole stack is wired correctly end to end.

python3 seed_data.py
uvicorn api:app --port 8000 &
python3 cli.py --current
curl http://127.0.0.1:8000/seasons/current

If the CLI output and the curl output describe the same season, the project is complete. You now have a working, queryable Fortnite season history that you control, instead of five browser tabs open to five slightly different lists.

The Complete Project, File by File

It helps to see the finished layout in one place before you go back and fill in any step you skipped. Six files, one database, zero external services required to run it locally.

fortnite-season-db/
├── venv/                  # isolated Python environment, not checked into version control
├── schema.sql             # table definition and indexes (Step 3)
├── seed_data.py           # the 41-row dataset and the insert logic (Step 5)
├── query.py               # every SQL query, shared by the CLI and the API (Step 6, 9)
├── cli.py                 # command-line lookup tool (Step 7)
├── api.py                 # FastAPI app and route definitions (Step 8, 9)
├── update_season.py       # closes the old season, opens the new one (Step 11)
└── fortnite_seasons.db    # generated automatically, do not edit by hand

Each file has exactly one job. query.py is the only file that touches SQL directly, which means if you ever swap SQLite for PostgreSQL down the line, that is the only file you need to rewrite. api.py and cli.py stay untouched because they only ever call functions, never raw queries. That separation is a small thing on a 41-row hobby project, but it is the exact pattern that keeps a larger project from turning into a tangle of duplicated SQL strings six months from now.

If you want to confirm the whole thing is genuinely portable, copy the folder to a second machine, recreate the virtual environment, reinstall the three dependencies, and run the seed script fresh. A clean run there, producing the same 41 rows and the same current-season answer, is the real test of whether this project is reproducible or whether it quietly depends on something specific to your first machine.

Common Pitfalls When Building a Season Database

  • Storing dates as raw strings without a fixed format. Mixing "2026-08-20" and "August 20, 2026" in the same column breaks every sort and comparison. Pick ISO 8601 and never deviate.
  • Forcing a fake end date onto the live season. Setting the current season's end date to today's date instead of NULL will quietly corrupt your average-duration math the moment you run it.
  • Not closing database connections. Use the with get_connection() as conn pattern everywhere. Leaving connections open across a long-running API process is a common source of "database is locked" errors under load.
  • Confusing season numbering across chapters. Season numbers reset at the start of each new chapter, so "Season 4" alone is ambiguous. Always store chapter and season label together, never season number in isolation.
  • Skipping indexes because the table is small. Forty rows do not need an index. If you later merge in cosmetic data, match history, or Battle Pass tiers against this table, an unindexed join gets slow fast.
  • Trusting a single third-party source blindly. Community APIs disagree on edge-case dates, as shown by the Chapter 6 gap earlier in this article. Cross-check anything you did not personally verify before shipping it publicly.
  • Hardcoding the database path. A relative path like "fortnite_seasons.db" only resolves correctly if you always run scripts from the same working directory. Use an absolute path or an environment variable once you deploy this anywhere.

Troubleshooting Guide

  • "ModuleNotFoundError: No module named 'fastapi'": your virtual environment is not activated. Run the source venv/bin/activate command again in the current terminal session.
  • "sqlite3.OperationalError: database is locked": another process, often a second terminal running the CLI tool, still has the file open. Close other connections or wrap access in short-lived with blocks.
  • "Address already in use" when starting Uvicorn: port 8000 is occupied by a previous run that did not shut down cleanly. Either kill that process or start on a different port with --port 8001.
  • Empty results from a chapter lookup that should return data: chapter names are case- and space-sensitive in the WHERE clause. "chapter 7" will not match "Chapter 7."
  • CORS errors when calling the API from a browser-based frontend: FastAPI blocks cross-origin requests by default. Add fastapi.middleware.cors.CORSMiddleware and explicitly allow your frontend's origin.
  • "command not found: sqlite3": the SQLite command-line shell is not installed separately from Python's sqlite3 module. Install it via your OS package manager, or skip the CLI verification step and let seed_data.py create the file directly.
  • JSON serialization error on a date field: this happens if you accidentally return a Python date object instead of a string. Keep all dates as plain strings throughout the query layer, and only convert to date objects inside the analytics function where you need to do math.
  • The current-season flag returns nothing: you likely have zero or more than one row with is_current = 1. Add a uniqueness check in your update script so exactly one row ever holds that flag.
  • Windows virtual environment activation fails silently: PowerShell's execution policy sometimes blocks the activation script. Run PowerShell as administrator once and execute Set-ExecutionPolicy RemoteSigned, then retry.

Advanced Tips: Caching, Auth, and Extending the API

Once the basic version works, a handful of upgrades make this project genuinely production-worthy rather than a one-off script. Add functools.lru_cache around get_duration_stats() since that computation never changes until you insert a new row, and recalculating it on every single request wastes cycles for no benefit.

If you plan to expose this publicly rather than run it locally, add a simple API key check via a FastAPI dependency before opening it to the internet, and put a rate limiter like slowapi in front of it. A public, unauthenticated endpoint returning your full dataset on every request is an easy target for scraping at a volume you did not intend to support.

For extending the schema, consider adding columns for Battle Pass tier count, map codename, and headline skin, since those are the fields most side projects built on top of season data actually want (our Chapter 7 loot tracker build covers a compatible schema pattern for item-level data if you want to merge the two projects). Wrap the whole thing in a single Docker container with a persistent volume for the SQLite file if you want it running as a small always-on service rather than something you start manually. Finally, write a handful of pytest cases around get_season_on_date() specifically, since date-boundary logic (does a season "end" on its listed end date or the day after) is the part of this project most likely to have an off-by-one bug hiding in it.

A visualization layer is a natural next add-on once the API is stable. A short script using matplotlib can pull every row from /seasons, plot duration as a bar chart ordered by start date, and immediately make patterns visible that are hard to spot in a table, like the way Chapter 2's early seasons ran noticeably longer than anything in Chapter 6 or Chapter 7. That kind of chart takes about fifteen lines of code once the data is already sitting in a clean, queryable format, which is really the whole argument for doing this as a database in the first place.

Version control the seed data itself, not just the code. Committing seed_data.py to a Git repository every time you update it for a new season gives you a free audit trail of exactly when each season transition happened in the real world, which turns out to be useful later if you ever need to cross-reference this against match history, esports tournament schedules, or Battle Pass pricing changes from the same period.

Frequently Asked Questions

How many Fortnite seasons are there in total?
Counting the Pre-Season, all numbered seasons across seven chapters, Chapter 1's "Season X," the Chapter 4 "Fortnite OG" bonus season, and the Chapter 2 Remix event, the dataset in this tutorial totals 41 entries through Chapter 7 Season 4. That number depends on convention: trackers that exclude special or bonus events land closer to 33-35 numbered seasons.

What chapter and season is Fortnite on right now, in September 2026?
Chapter 7, Season 4: Override, which started August 20, 2026 and is scheduled to run through roughly November 1, 2026.

Is there an official Epic Games API for season history?
No. Epic does not publish a documented public endpoint for historical season metadata. Community projects like fortnite-api.com fill that gap, but their schemas and completeness vary, which is the core reason this tutorial builds a self-hosted alternative.

Do I need to know SQL to follow this tutorial?
No. Every query in this project is written for you in the code blocks. Basic comfort reading Python is enough, since the SQL itself is limited to simple SELECT, INSERT, and UPDATE statements.

How do I add a new season when Epic announces one?
Run the pattern shown in Step 11: close the current row with a real end date, flip its flag to 0, and insert a new row with is_current set to 1 and end_date left NULL.

Can this run on a Raspberry Pi or a free hosting tier?
Yes. SQLite and FastAPI both have minimal resource requirements, and this dataset is a few kilobytes. It runs comfortably on a Raspberry Pi, a free-tier cloud VM, or even a serverless function with a bundled read-only copy of the database file.

Why does the average season length matter for anything practical?
It is a useful sanity check for content planning and Battle Pass value comparisons: at roughly 79 days per season on average, a season priced the same as a shorter one effectively costs more per day of access.

Can I extend this to also track Fortnite Battle Pass cosmetics or Chapter 7 map changes?
Yes, and it is a natural next step. Add a related table keyed by season ID for cosmetics or map notes, following the same pattern used for the seasons table itself, and join across them in a new query function. For competitive stats rather than cosmetics, our Fortnite Ranked vs FNCS breakdown is a good companion reference, and our broader esports coverage tracks how other titles structure similar season and ranking data.