{"categories":[{"label":"Information Analysis","url":"https://skillfed.io/packages/category/scientific-engineering-information-analysis"}],"enrichment":{"capability":"Tempo provides time series operations on Spark DataFrames, including AS OF joins, rolling statistics, lagged feature generation, and Delta Lake optimization for time-partitioned data.","skillfed_tags":["time-series","databricks"],"use_cases":["Resample high-frequency sensor or event data into regular time buckets (e.g., 1-minute aggregates) for analysis or visualization.","Perform AS OF joins to enrich a fact table with the most recent values from a slowly-changing dimension or reference table.","Generate lagged features from time series for machine learning (e.g., prior 50 values for EMA-based prediction).","Compute rolling statistics (mean, min, max) over a time window to detect anomalies or trends in streaming data.","Interpolate missing time periods in sparse time series using forward-fill, backward-fill, or linear interpolation methods."],"what_it_does":"Tempo is a library that wraps DataFrames to simplify time series analysis. It provides a TSDF (Time Series DataFrame) abstraction that treats a DataFrame as a collection of time series, one per partition key, and exposes methods for common time series operations: resampling to different frequencies, AS OF joins to merge the latest records from a source table, rolling statistics over time windows, exponential and simple moving averages, Fourier transforms, and interpolation of missing values. The library also supports Delta Lake optimization on time and partition fields.\n\nTempo is built for data teams using Databricks who need to perform time series transformations at scale. It handles the complexity of windowing and sorting across partitions, allowing you to work with time-indexed data using a higher-level API. The entry point is always a TSDF object, which requires a distinguished timestamp column and optionally partition and sequence columns.","worth_installing":"Yes. Tempo is actively maintained, has no known vulnerabilities, uses a permissive MIT license, and zero runtime dependencies. It is well-suited for teams already using Databricks who need to simplify time series operations. Install it if you work with time-indexed data; skip it if you do not need time series transformations."},"id":"dbl-tempo","links":{"html":"https://skillfed.io/packages/dbl-tempo","md":"https://skillfed.io/packages/dbl-tempo.md","pypi":"https://pypi.org/project/dbl-tempo/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2025-09-15","license_spdx":"MIT","license_treatment":"permissive","name":"dbl-tempo","python_support":"supports_current","summary":"Tempo is timeseries manipulation for Spark. This project builds upon the capabilities of PySpark to provide a suite of abstractions and functions that make operations on timeseries data easier and highly scalable."},"popularity":{"monthly_downloads":4221234,"position":2360,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.1.30"}