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description= Dask and Coiled make it easy to access, manipulate, and transform large tabular datasets in the cloud. Dask DataFrame scales out pandas, and is a natural and modern replacement for Spark. There ar...;
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dataframe workloads with coiled coiled documentation skip to main content ctrl k start for free coiled user guide examples blog contact us sign in start for free overview home user guide examples blog contact us sign in start for free ctrl k section navigation dataframes dataframe workflows tpc h benchmarks exploratory analysis with cloud parquet and dask dataframe production etl with prefect and serverless functions duckdb with serverless functions polars with serverless functions interactive data visualization with dask and datashader machine learning machine learning with coiled scale xgboost training with dask parallel hyperparameter optimization with dask gpu pytorch with batch jobs distributed training with accelerate gpu pytorch with serveless functions experiment tracking with mlflow run mlflow on coiled geospatial geospatial workflows with coiled analyze 6tb of zarr data with xarray and dask process 250 tb with xarray and dask non python jobs with batch jobs churn through s3 files with serverless functions schedule jobs with prefect and serverless functions general python general computing with coiled hello world estimate pi with parallel serverless functions churn through s3 files with parallel serverless functions process files on s3 with dask dataframe workloads with coiled dask and coiled make it easy to access manipulate and transform large tabular datasets in the cloud dask dataframe scales out pandas and is a natural and modern replacement for spark there are many ways to use coiled for data analysis and transformation this page collects a few common patterns and points you to useful documentation and examples this page may be helpful for you if you want to transform terabytes of parquet data query massive tables with python run python jobs on a schedule you re pained by running out of memory spark s ux making you cry slow startup poor observability in this post we ll go through some common workflows and make a general case for coiled for tabular data analysis and transformation workloads dask does what pandas does but bigger and better performance at scale regular production jobs metrics and observability data access cloud architecture dask dataframe does what pandas does just bigger and better dask dataframe is composed of many small pandas dataframes spread across a cluster of machines this makes it trivial for your team to adopt dask if your team already uses python and pandas pandas import pandas as pd orders pd read_parquet orders parquet accounts pd read_parquet accounts parquet df orders merge accounts on id df to_parquet merged parquet easy and familiar api combines well with other python code kinda slow breaks when data gets too big dask dataframe import dask dataframe as dd orders dd read_parquet s3 my bucket orders parquet accounts dd read_parquet s3 my bucket accounts parquet df orders merge accounts on id df to_parquet s3 my bucket merged parquet easy and familiar api combines well with other python code pretty fast scales comfortably from 10 gib to 10 tib to learn more about dask dataframe see dask dataframe documentation or see the uber lyft exploratory analysis example alternatively coiled supports other stuff too including spark clusters duckdb polars performance at scale there are many options today for dataframe libraries including spark dask duckdb polars and others dask is robustly fast at scale when calculating on the popular tpc h benchmark suite dask wasn t always the fastest especially at small scale when polars and duckdb are really impressive but it was fastest at large scale easily beating spark and it was the most robustly performant across scales local macbook pro 10 gb 100 gb cloud aws 100 gb 1 tb 10 tb 128 cpus 10 tb 1280 cpus for more information read about tpc h benchmarks regular production jobs q how do i use coiled in my ci cd pipeline for regular production jobs a because you can use coiled from anywhere you run python coiled composes naturally with all job schedulers just keep using whatever you re using today other frameworks tie you into a certain job scheduling frameworks ahem databricks or depend on explicit third party integrations that may be what you want or it may be terrible for your use case fortunately because of coiled s environment synchronization you can use coiled from anywhere that lets you call python code here are some examples but the point is that coiled works from anywhere prefect from prefect import task flow import coiled task def my_job_task with coiled cluster as cluster see prefect documentation for more information and different ways to use the projects dagster from dagster import op import coiled op def my_regular_job with coiled cluster as cluster see example dagster and coiled for production for a worked example cron 0 python process py process py import coiled dask with coiled cluster as cluster for more extensive examples see also prefect and coiled dagster and coiled django and coiled github actions and coiled because coiled operates as a library you can use it from anywhere the examples above are not exhaustive they re demonstrative that explicit integrations are not necessary for a more fully featured example that uses coiled prefect delta table see example easy scalable production etl metrics and observability things break it s your job to fix them we can help jobs fail for all sorts of reasons fortunately coiled ships with an excellent suite of metrics and observability dashboards that can help you track down failures quickly coiled gives you an integrated timeline of your code metrics and logs this combination helps you quickly track down annoying failures and attribute them to hardware or code failures prefer your own logging stack no worries we dump logs directly into your normal log store like aws cloudwatch and tag all resources so your existing systems should continue working just fine data access because of the rich history of python and pandas dask and coiled can read data from pretty much anywhere parquet import dask dataframe as dd df dd read_parquet path s3 my bucket parquet snowflake import dask_snowflake df read_snowflake query select from mytable connection_kwargs delta table import dask_deltatable df dask_deltatable read_deltalake path mytable datetime 2020 01 01 google bigquery import dask_bigquery ddf dask_bigquery read_gbq table_id project_id dataset_id for more information read dask dataframe documentation on loading data additionally because of coiled s environment synchronization data access credentials are automatically transmitted from wherever you re calling coiled a user s machine or a ci job to the cloud vms this means that you don t need to do anything special for data credentials beyond what you re already doing for small data cases cloud architecture coiled s raw cloud architecture offers several advantages over other data transformation engineering platforms that we ve seen historically minute long cluster spin up times means that users quickly spin things up and spin them down making things both responsive and cost efficient in contrast emr and databricks users experience 5 15 minute spin up times which causes a behavior of leaving clusters on and idling all day leading to high costs zero architecture to maintain means that the top engineer who s managing the cluster can go do more interesting work coiled only spins up vms and only when you re actively using them there are no kubernetes clusters or node pools or other standing infrastructure to keep healthy any instance type means that you can use any hardware type to optimize for the job at hand need fast disk gpus fast networking big memory machines for your job no problem for more information read manage hardware examples dataframes at scale comparison tpc h copyright 2026 coiled computing inc
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