Data Export

Saving financial data as CSV loses type information, bloats file size 5-10x, and makes every downstream read parse strings back into numbers - a tax paid on every pipeline run.

The Problem

CSV is human-readable but machine-hostile: no type information (dates become strings, integers become floats), no compression (a 100MB Parquet file becomes 500MB CSV), no predicate pushdown (you must read the entire file to filter one column), and no schema enforcement (a renamed column breaks silently). For financial data pipelines where the same dataset is read hundreds of times by different notebooks, the cumulative cost of CSV is enormous in both time and correctness.

The Pattern

WRONG

import pandas as pd

# CSV: no types, no compression, slow reads, 5x file size
df.to_csv("prices.csv", index=False)

# Every consumer must re-parse types
df = pd.read_csv("prices.csv", parse_dates=["date"])  # Hope the format matches
df["volume"] = df["volume"].astype(int)  # Was saved as float because of NaN

CORRECT

import polars as pl

# Enforce schema before writing
SCHEMA = {
    "timestamp": pl.Date,
    "symbol": pl.Utf8,
    "open": pl.Float64,
    "high": pl.Float64,
    "low": pl.Float64,
    "close": pl.Float64,
    "volume": pl.UInt64,
}

df = df.cast(SCHEMA)
df.write_parquet(
    "data/prices.parquet",
    compression="zstd",       # 3-5x smaller than uncompressed
    statistics=True,           # Enables predicate pushdown on read
    row_group_size=100_000,    # Balance between granularity and overhead
)

# Reader gets exact types, filters pushed down to storage layer
df = (
    pl.scan_parquet("data/prices.parquet")
    .filter(pl.col("timestamp") >= pl.lit("2020-01-01").str.to_date())
    .filter(pl.col("symbol") == "SPY")
    .collect()
)
# Only reads the row groups and columns needed - 10-100x faster than CSV

Partitioning Large Datasets

import polars as pl

df = df.with_columns(
    year=pl.col("timestamp").dt.year(),
)
for (year,), group in df.group_by("year"):
    path = f"data/prices/year={year}/data.parquet"
    group.drop("year").write_parquet(path, compression="zstd")

df_2024 = pl.read_parquet("data/prices/year=2024/data.parquet")

df = pl.scan_parquet("data/prices/**/data.parquet", hive_partitioning=True).filter(
    pl.col("year") >= 2020
).collect()

Schema Versioning

When schema evolves, write a sidecar .schema.json with the version, column types, and row count. Readers assert the expected version before loading.

Guardrails

• Never use CSV for production data pipelines - Parquet is strictly superior for typed columnar data
• Always enable statistics=True - it costs nothing on write and enables predicate pushdown on read
• zstd compression gives the best size/speed tradeoff; snappy is faster but larger
• Partition only when datasets exceed ~1GB or when you frequently filter by the partition key
• Schema changes must be explicit - a silently added column breaks downstream notebooks that assert schema

Production Implementation

from ml4t.data import DataManager
from ml4t.data.storage.backend import StorageConfig
from ml4t.data.storage.hive import HiveStorage

storage = HiveStorage(StorageConfig(base_path="./data", partition_granularity="year"))
dm = DataManager(storage=storage, use_transactions=True)
key = dm.load("SPY", "2015-01-01", "2024-12-31", provider="yahoo")
# Writes partitioned Parquet and returns the storage key

Checklist

• Parquet format used for all persisted data
• Schema enforced via cast() before writing
• Compression enabled (zstd default)
• Statistics enabled for predicate pushdown
• Large datasets partitioned by date or symbol
• Schema version tracked for evolving datasets