Six paired functions convert between Unix epoch numbers and timestamp columns at three precision levels: timestamp_seconds, timestamp_millis, and timestamp_micros go from a numeric column to a timestamp, and unix_seconds, unix_millis, and unix_micros go the other way. Reach for them when you're ingesting long epoch values from Kafka, log files, or APIs and need a real timestamp type to filter, format, or join on — or when you need to emit timestamps as numeric values for downstream systems.

unix_timestamp and from_unixtime are the bridge between human-readable timestamp strings and Unix epoch seconds. Reach for them when integrating with systems that store time as a long — Kafka payloads, log files, REST APIs — or when you need to do timestamp math that's easier in seconds than in calendar fields. to_unix_timestamp is the SQL-only sibling of unix_timestamp, called through expr().

to_date and to_timestamp parse string columns into proper date and timestamp types. They're the functions you reach for whenever raw data lands as text — CSV files, JSON payloads, or upstream queries — and you need to do anything date-related with it. Spark 3.4 also adds to_timestamp_ntz and to_timestamp_ltz for explicit time-zone semantics.

date_trunc and trunc round a date or timestamp column down to a coarser unit — the start of the hour, day, month, quarter, or year. They're the go-to functions for bucketing events into time windows for grouping, partitioning, or aligning data to a calendar boundary.

date_format converts a date, timestamp, or string column into a formatted string column using a pattern made of letters like yyyy, MM, dd, HH, and EEEE. Use it whenever you need a human-readable date label, a custom export format, or a partition-friendly column like 2026-01.

datediff returns the number of days between two date columns. It's the go-to function for computing things like "days to ship", "days since signup", or "age in days". Spark 3.4 added a SQL-only alias date_diff that does the same thing — useful when you're writing a SQL expression but otherwise interchangeable.

date_add, date_sub, and add_months shift a date column forward or backward by a fixed number of days or months. They're the bread-and-butter functions for computing things like "30 days from order date", "one month before renewal", or "next billing cycle". Spark 3.4 added a one-word alias, dateadd, that's only reachable through expr().

date_part, datepart, and extract are generic ways to pull a single field out of a date, timestamp, or interval column. They cover the same ground as the dedicated functions like year, month, dayofmonth and hour, minute, second, but with one function call where the field name is a parameter — useful when the field you need is decided at runtime or driven by config.

Spark provides hour, minute, and second for pulling the time-of-day components out of a timestamp column. They're the time-side counterparts to year, month, and day and are useful for bucketing events by hour of day, filtering work hours, or building time-based features.

Spark provides a small family of functions for pulling individual date parts — year, month, day, week, quarter — out of a date or timestamp column. They're the building blocks for grouping by month, filtering by quarter, or partitioning a table by year.