@Manjusha , Great foundation from @pradeep_singh. Let me build on that with some specifics, especially around the time series and row ordering challenge.

Where Polars actually runs in Databricks

The statement you heard in training is about this boundary: once you call spark_df.toPandas() (or collect(), or create a Polars DataFrame directly), all subsequent work happens on the driver process, not the workers. Polars, like pandas, has no integration with Spark's execution engine. It doesn't matter how fast Polars is on a single node. Spark can't distribute it.

Spark distributes Python code only when that code is expressed as Spark transformations: built-in functions, SQL expressions, UDFs, pandas UDFs, applyInPandas, etc. Anything outside of Spark DataFrames/RDDs stays on the driver.

So: Polars on Databricks = single-node (driver) compute, same as pandas.

Why SparkR gave unexpected results

In a distributed system there is no inherent global row order. Spark splits your data across worker nodes, and unless you explicitly define the ordering, you'll get nondeterministic results. That's almost certainly what happened with your SparkR port. The fix is to encode your time series assumptions explicitly rather than relying on implicit row order.

What to try first: window functions

Before reaching for anything else, see how far you can get with native PySpark window functions. These are fully distributed, performant, and map directly to most time series patterns from R:

from pyspark.sql import functions as F, Window

w = Window.partitionBy("entity_id").orderBy("timestamp")

df_with_lag = (
    df
    .withColumn("prev_value", F.lag("value", 1).over(w))
    .withColumn("diff", F.col("value") - F.col("prev_value"))
)

Common mappings: lag, lead, row_number, dense_rank, cumulative sums, and rolling windows via rangeBetween on a timestamp or sequence column. Partition by whatever constitutes a "series" in your domain (device, account, sensor, etc.) and order by the time column.

Second option: pandas API on Spark

If your transformations are closer to pandas/R idioms and hard to express in pure window functions, pandas API on Spark gives you a pandas-like syntax with distributed execution under the hood. It supports groupby + aggregation, sort_values, and rolling operations. Worth checking whether the specific methods you rely on have equivalents. Documentation: https://docs.databricks.com/aws/en/pandas/pandas-on-spark

Where something isn't available in pandas API on Spark, you can fall back to native PySpark window functions for that piece.

Fallback for complex per-group logic: applyInPandas

If your per-entity logic is complicated enough that window functions and pandas API on Spark can't express it cleanly, groupBy().applyInPandas is the hybrid pattern. Spark partitions data by your group key, ships each group to a worker, and runs your Python function in parallel:

import pandas as pd

def process_series(pdf: pd.DataFrame) -> pd.DataFrame:
    pdf = pdf.sort_values("timestamp")
    # your time series logic here
    return pdf

schema = ...  # Spark schema of the returned DataFrame

result = (
    df
    .groupBy("entity_id")
    .applyInPandas(process_series, schema=schema)
)

You get parallelism across groups without losing ordering within each group. You can even convert to Polars inside the function (pandas to Polars, run your logic, convert back to pandas), though there's conversion overhead per group.

Documentation: https://docs.databricks.com/en/pandas/pandas-function-apis.html

If your data is one single time series with no natural grouping

That's the harder case. If the entire dataset is one ordered sequence with no way to partition into independent groups, distributed processing doesn't help because the work is inherently serial. In that situation, a larger single-node driver running Polars or pandas is the pragmatic answer.

Putting it together

The decision tree:

1. Try native Spark window functions first. They're fully distributed and handle most time series patterns.
2. If the syntax is awkward, try pandas API on Spark for a more familiar API with distributed execution.
3. If the logic is too complex for either, use applyInPandas to run arbitrary Python per group, in parallel.
4. If data has no natural grouping and is inherently serial, size up the driver and use Polars directly.

That gives you correct time series behavior (because ordering and partitioning are explicit), Spark parallelism where possible, and a Python-first environment.

Hope this helps, Louis.