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Introduction

  • What is Kedro?
    • Learn how to use Kedro
    • Assumptions

First steps

  • Set up Kedro
    • Summary
    • Installation prerequisites
    • Create a virtual environment for your Kedro project
      • How to create a new virtual environment using conda
      • How to create a new virtual environment without using conda
    • How to install Kedro using pip
    • How to verify your Kedro installation
    • How to upgrade Kedro
    • How to install a development version of Kedro
  • Create a new Kedro project
    • Summary
    • Create a new empty project
    • Create a new project from a configuration file
    • Create a new project containing example code
    • Run the project
    • Visualise a Kedro project
    • Where next?
    • More information about the pandas-iris example project
      • Background information
      • Iris example: visualisation
  • Kedro concepts
    • Summary
    • Node
    • Pipeline
    • Data Catalog
    • Kedro project directory structure
      • conf
        • conf/base
        • conf/local
      • data
      • src

Next steps: Tutorial

  • Introducing spaceflights
    • Scenario
    • Get help
    • Terminology
      • Project root directory
      • Dependencies
      • Standard development workflow
  • Set up the spaceflights project
    • Create a new project
    • Install project dependencies
      • Install the dependencies
    • Optional: logging and configuration
      • Configuration best practice to avoid leaking confidential data
  • Set up the data
    • Project datasets
    • Dataset registration
      • Test that Kedro can load the data
    • Further information
      • Custom data
      • Supported data locations
  • Create a data processing pipeline
    • Introduction
    • Data preprocessing node functions
    • The data processing pipeline
    • Test the example
    • Preprocessed data registration
    • Create a table for model input
    • Model input table registration
    • Test the example again
    • Visualise the project
    • Checkpoint
  • Create a data science pipeline
    • Data science nodes
    • Input parameter configuration
    • Model registration
    • Data science pipeline
    • Test the pipelines
      • Slice a pipeline
    • Modular pipelines
      • Optional: Extend the project with namespacing and a modular pipeline
      • How it works: the modular pipeline() wrapper
        • How do namespaces affect parameters?
    • Optional: Kedro runners
  • Package an entire Kedro project
    • Add documentation to a Kedro project
      • Set up the Sphinx project files
      • Build HTML documentation
      • Documentation from docstrings
    • Package a Kedro project
      • Package recipients
      • Docker, Airflow and other deployment targets
  • Spaceflights tutorial FAQs
    • How do I resolve these common errors?
      • DataSet errors
        • DataSetError: Failed while loading data from data set
        • DataSetNotFoundError: DataSet not found in the catalog
        • DataSetError: An exception occurred when parsing config for DataSet
      • Pipeline run

Visualisation with Kedro-Viz

  • Get started with Kedro-Viz
    • Visualise the spaceflights project
      • Exit an open visualisation
    • Automatic visualisation updates
    • Visualise layers
    • Share a pipeline visualisation
  • Visualise charts in Kedro-Viz
    • Visualisation with Plotly
      • Update the dependencies
      • Configure the Data Catalog
      • Create the template reporting pipeline
      • Add the Plotly reporting nodes
      • Update the reporting pipeline code
      • Run the pipeline
    • Visualisation with Matplotlib
      • Update the dependencies
      • Configure the Data Catalog
      • Add another node
      • Update the pipeline
      • Run the pipeline
  • Experiment tracking in Kedro-Viz
    • Experiment tracking demonstration using Kedro-Viz
    • Kedro versions supporting experiment tracking
    • When should I use experiment tracking in Kedro?
    • Set up a project
      • Install Kedro and Kedro-Viz
      • Install the dependencies for the project
    • Set up the session store
    • Set up experiment tracking datasets
    • Modify your nodes and pipelines to log metrics
    • Generate the run data
    • Access run data and compare runs
    • View and compare plots
      • Update the dependencies
      • Add a plotting node
    • View and compare metrics data

Notebooks & IPython users

  • Kedro and Jupyter Notebooks
    • A custom Kedro kernel
    • Iris dataset example
      • catalog
      • context
      • pipelines
      • session
    • %reload_kedro line magic
    • %run_viz line magic
    • Convert functions from Jupyter Notebooks into Kedro nodes
    • Useful to know…
      • Managed services
      • IPython, JupyterLab and other Jupyter clients
    • Find out more
  • Kedro as a data registry
    • Usage

Kedro project setup

  • Kedro starters
    • How to use Kedro starters
    • Starter aliases
    • List of official starters
    • Starter versioning
    • Use a starter with a configuration file
    • How to create a Kedro starter
      • Configuration variables
      • Example Kedro starter
  • Dependencies
    • Project-specific dependencies
    • Install project-specific dependencies
    • Workflow dependencies
      • Install dependencies related to the Data Catalog
        • Install dependencies at a group-level
        • Install dependencies at a type-level
  • Configuration
    • Configuration root
    • Local and base configuration environments
    • Additional configuration environments
    • Template configuration
      • Globals
      • Jinja2 support
    • Configuration with OmegaConf
      • Templating for parameters
      • Custom template resolvers
      • Environment variables for credentials
    • Parameters
      • Load parameters
      • Specify parameters at runtime
      • Use parameters
    • Credentials
      • AWS credentials
  • Lifecycle management with KedroSession
    • Overview
    • Create a session
  • Project settings
    • Application settings
    • Project metadata

Data Catalog

  • The Data Catalog
    • Use the Data Catalog within Kedro configuration
    • Specify the location of the dataset
    • Data Catalog *_args parameters
      • Example 1: Provide the project value to the underlying filesystem class (GCSFileSystem) to interact with Google Cloud Storage (GCS)
      • Example 2: Load data from a local binary file using utf-8 encoding
      • Example 3: Save data to a CSV file without row names (index) using utf-8 encoding
    • Use the Data Catalog with the YAML API
      • Example 1: Loads / saves a CSV file from / to a local file system
      • Example 2: Loads and saves a CSV on a local file system, using specified load and save arguments
      • Example 3: Loads and saves a compressed CSV on a local file system
      • Example 4: Loads a CSV file from a specific S3 bucket, using credentials and load arguments
      • Example 5: Loads / saves a pickle file from / to a local file system
      • Example 6: Loads an Excel file from Google Cloud Storage
      • Example 7: Loads a multi-sheet Excel file from a local file system
      • Example 8: Saves an image created with Matplotlib on Google Cloud Storage
      • Example 9: Loads / saves an HDF file on local file system storage, using specified load and save arguments
      • Example 10: Loads / saves a parquet file on local file system storage, using specified load and save arguments
      • Example 11: Loads / saves a Spark table on S3, using specified load and save arguments
      • Example 12: Loads / saves a SQL table using credentials, a database connection, using specified load and save arguments
      • Example 13: Loads an SQL table with credentials, a database connection, and applies a SQL query to the table
      • Example 14: Loads data from an API endpoint, example US corn yield data from USDA
      • Example 15: Loads data from Minio (S3 API Compatible Storage)
      • Example 16: Loads a model saved as a pickle from Azure Blob Storage
      • Example 17: Loads a CSV file stored in a remote location through SSH
    • Create a Data Catalog YAML configuration file via CLI
    • Adding parameters
    • Feeding in credentials
    • Load multiple datasets with similar configuration
    • Transcode datasets
      • A typical example of transcoding
      • How does transcoding work?
    • Version datasets and ML models
    • Use the Data Catalog with the Code API
      • Configure a Data Catalog
      • Load datasets
        • Behind the scenes
      • View the available data sources
      • Save data
        • Save data to memory
        • Save data to a SQL database for querying
        • Save data in Parquet
  • Kedro IO
    • Error handling
    • AbstractDataSet
    • Versioning
      • version namedtuple
      • Versioning using the YAML API
      • Versioning using the Code API
      • Supported datasets
    • Partitioned dataset
      • Partitioned dataset definition
        • Dataset definition
        • Partitioned dataset credentials
      • Partitioned dataset load
      • Partitioned dataset save
      • Partitioned dataset lazy saving
      • Incremental loads with IncrementalDataSet
        • Incremental dataset load
        • Incremental dataset save
        • Incremental dataset confirm
        • Checkpoint configuration
        • Special checkpoint config keys

Nodes and pipelines

  • Nodes
    • How to create a node
      • Node definition syntax
      • Syntax for input variables
      • Syntax for output variables
    • **kwargs-only node functions
    • How to tag a node
    • How to run a node
    • How to use generator functions in a node
  • Pipelines
    • How to build a pipeline
      • How to tag a pipeline
      • How to merge multiple pipelines
      • Information about the nodes in a pipeline
      • Information about pipeline inputs and outputs
    • Bad pipelines
      • Pipeline with bad nodes
      • Pipeline with circular dependencies
  • Modular pipelines
    • What are modular pipelines?
      • Key concepts
    • How do I create a modular pipeline?
      • What does kedro pipeline create do?
      • Ensuring portability
      • Providing modular pipeline specific dependencies
    • Using the modular pipeline() wrapper to provide overrides
    • Combining disconnected pipelines
    • Using a modular pipeline multiple times
    • How to use a modular pipeline with different parameters
  • The pipeline registry
    • Pipeline autodiscovery
  • Micro-packaging
    • Package a micro-package
    • Package multiple micro-packages
    • Pull a micro-package
      • Providing fsspec arguments
    • Pull multiple micro-packages
  • Run a pipeline
    • Runners
      • SequentialRunner
      • ParallelRunner
        • Multiprocessing
        • Multithreading
    • Custom runners
    • Load and save asynchronously
    • Run a pipeline by name
    • Run pipelines with IO
    • Output to a file
    • Configure kedro run arguments
  • Slice a pipeline
    • Slice a pipeline by providing inputs
    • Slice a pipeline by specifying nodes
    • Slice a pipeline by specifying final nodes
    • Slice a pipeline with tagged nodes
    • Slice a pipeline by running specified nodes
    • How to recreate missing outputs

Extend Kedro

  • Common use cases
    • Use Case 1: How to add extra behaviour to Kedro’s execution timeline
    • Use Case 2: How to integrate Kedro with additional data sources
    • Use Case 3: How to add or modify CLI commands
    • Use Case 4: How to customise the initial boilerplate of your project
  • Custom datasets
    • Scenario
    • Project setup
    • The anatomy of a dataset
    • Implement the _load method with fsspec
    • Implement the _save method with fsspec
    • Implement the _describe method
    • The complete example
    • Integration with PartitionedDataSet
    • Versioning
    • Thread-safety
    • How to handle credentials and different filesystems
    • How to contribute a custom dataset implementation
  • Kedro plugins
    • Overview
    • Example of a simple plugin
    • Extend starter aliases
    • Working with click
    • Project context
    • Initialisation
    • global and project commands
    • Suggested command convention
    • Hooks
    • CLI Hooks
    • Contributing process
    • Supported Kedro plugins
    • Community-developed plugins

Hooks

  • Hooks
    • Introduction
    • Concepts
      • Hook specification
        • CLI hooks
      • Hook implementation
        • Registering your Hook implementations with Kedro
        • Disable auto-registered plugins’ Hooks
    • Under the hood
  • Common use cases
    • Use Hooks to extend a node’s behaviour
    • Use Hooks to customise the dataset load and save methods
    • Use Hooks to load external credentials
  • Hooks examples
    • Add memory consumption tracking
    • Add data validation
      • V2 API
      • V3 API
    • Add observability to your pipeline
    • Add metrics tracking to your model
    • Modify node inputs using before_node_run hook

Logging

  • Logging
    • Default framework-side logging configuration
    • Project-side logging configuration
      • Disable file-based logging
      • Use plain console logging
      • Rich logging in a dumb terminal
      • Rich logging in Jupyter
    • Perform logging in your project

Development

  • Set up Visual Studio Code
    • Advanced: For those using venv / virtualenv
    • Setting up tasks
    • Debugging
      • Advanced: Remote Interpreter / Debugging
    • Configuring the Kedro catalog validation schema
  • Set up PyCharm
    • Set up Run configurations
    • Debugging
    • Advanced: Remote SSH interpreter
    • Advanced: Docker interpreter
    • Configure Python Console
    • Configuring the Kedro catalog validation schema
  • Kedro’s command line interface
    • Autocompletion (optional)
    • Invoke Kedro CLI from Python (optional)
    • Kedro commands
    • Global Kedro commands
      • Get help on Kedro commands
      • Confirm the Kedro version
      • Confirm Kedro information
      • Create a new Kedro project
      • Open the Kedro documentation in your browser
    • Customise or Override Project-specific Kedro commands
      • Project setup
        • Build the project’s dependency tree
        • Install all package dependencies
      • Run the project
        • Modifying a kedro run
      • Deploy the project
      • Pull a micro-package
      • Project quality
        • Build the project documentation
        • Lint your project
        • Test your project
      • Project development
        • Modular pipelines
        • Registered pipelines
        • Datasets
        • Data Catalog
        • Notebooks
  • Debugging
    • Introduction
    • Debugging Node
    • Debugging Pipeline
  • Automated Testing
    • Introduction
    • Set up automated testing with pytest
      • Install pytest
      • Create a /tests directory
      • Test directory structure
      • Create an example test
      • Run your tests
    • Add test coverage reports with pytest-cov
      • Install pytest-cov
      • Configure pytest to use pytest-cov
      • Run pytest with pytest-cov
  • Linting
    • Introduction
    • Set up linting tools
      • Install linting tools
      • Run linting tools
    • Automating linting with pre-commit hooks
      • Install pre-commit
      • Add pre-commit configuration file
      • Install git hook scripts

Deployment

  • Deployment guide
    • Deployment choices
  • Single-machine deployment
    • Container-based
      • How to use container registry
    • Package-based
    • CLI-based
      • Use GitHub workflow to copy your project
      • Install and run the Kedro project
  • Distributed deployment
    • 1. Containerise the pipeline
    • 2. Convert your Kedro pipeline into targeted platform’s primitives
    • 3. Parameterise the runs
    • 4. (Optional) Create starters
  • Deployment with Argo Workflows
    • Why would you use Argo Workflows?
    • Prerequisites
    • How to run your Kedro pipeline using Argo Workflows
      • Containerise your Kedro project
      • Create Argo Workflows spec
      • Submit Argo Workflows spec to Kubernetes
      • Kedro-Argo plugin
  • Deployment with Prefect
    • Prerequisites
    • How to run your Kedro pipeline using Prefect
      • Convert your Kedro pipeline to Prefect flow
      • Run Prefect flow
  • Deployment with Kubeflow Pipelines
    • Why would you use Kubeflow Pipelines?
    • Prerequisites
    • How to run your Kedro pipeline using Kubeflow Pipelines
      • Containerise your Kedro project
      • Create a workflow spec
      • Authenticate Kubeflow Pipelines
      • Upload workflow spec and execute runs
  • Deployment with AWS Batch
    • Why would you use AWS Batch?
    • Prerequisites
    • How to run a Kedro pipeline using AWS Batch
      • Containerise your Kedro project
      • Provision resources
        • Create IAM Role
        • Create AWS Batch job definition
        • Create AWS Batch compute environment
        • Create AWS Batch job queue
      • Configure the credentials
      • Submit AWS Batch jobs
        • Create a custom runner
        • Set up Batch-related configuration
        • Update CLI implementation
      • Deploy
  • Deployment to a Databricks cluster
    • Prerequisites
    • Running Kedro project from a Databricks notebook
      • 1. Project setup
      • 2. Install dependencies and run locally
      • 3. Create a Databricks cluster
      • 4. Create GitHub personal access token
      • 5. Create a GitHub repository
      • 6. Push Kedro project to the GitHub repository
      • 7. Configure the Databricks cluster
      • 8. Run your Kedro project from the Databricks notebook
      • 9. Using the Kedro IPython Extension
      • 10. Running Kedro-Viz on Databricks
    • How to use datasets stored on Databricks DBFS
  • How to integrate Amazon SageMaker into your Kedro pipeline
    • Why would you use Amazon SageMaker?
    • Prerequisites
    • Prepare the environment
      • Install SageMaker package dependencies
      • Create SageMaker execution role
      • Create S3 bucket
    • Update the Kedro project
      • Create the configuration environment
      • Update the project settings
      • Update the data science pipeline
        • Create node functions
        • Update the pipeline definition
      • Create the SageMaker entry point
    • Run the project
    • Cleanup
  • How to deploy your Kedro pipeline with AWS Step Functions
    • Why would you run a Kedro pipeline with AWS Step Functions
    • Strategy
    • Prerequisites
    • Deployment process
      • Step 1. Create new configuration environment to prepare a compatible DataCatalog
      • Step 2. Package the Kedro pipeline as an AWS Lambda-compliant Docker image
      • Step 3. Write the deployment script
      • Step 4. Deploy the pipeline
    • Limitations
    • Final thought
  • How to deploy your Kedro pipeline on Apache Airflow with Astronomer
    • Strategy
    • Prerequisites
    • Project Setup
    • Deployment process
      • Step 1. Create new configuration environment to prepare a compatible DataCatalog
      • Step 2. Package the Kedro pipeline as an Astronomer-compliant Docker image
      • Step 3. Convert the Kedro pipeline into an Airflow DAG with kedro airflow
      • Step 4. Launch the local Airflow cluster with Astronomer
    • Final thought
  • Deployment to a Dask cluster
    • Why would you use Dask?
    • Prerequisites
    • How to distribute your Kedro pipeline using Dask
      • Create a custom runner
      • Update CLI implementation
      • Deploy
        • Set up Dask and related configuration

PySpark integration

  • Build a Kedro pipeline with PySpark
    • Centralise Spark configuration in conf/base/spark.yml
    • Initialise a SparkSession using a hook
    • Use Kedro’s built-in Spark datasets to load and save raw data
    • Spark and Delta Lake interaction
    • Use MemoryDataSet for intermediary DataFrame
    • Use MemoryDataSet with copy_mode="assign" for non-DataFrame Spark objects
    • Tips for maximising concurrency using ThreadRunner

Resources

  • Frequently asked questions
    • Kedro project development
    • Working with Jupyter
    • Datasets and the Data Catalog
    • Nodes and pipelines
    • Visualisation
  • Kedro glossary
    • Data Catalog
    • Data engineering vs Data science
    • Kedro
    • KedroContext
    • KedroSession
    • Kedro-Viz
    • Layers (data engineering convention)
    • Modular pipeline
    • Node
    • Node execution order
    • Pipeline
    • Pipeline slicing
    • Runner
    • Starters
    • Tags
    • Workflow dependencies

Contribute to Kedro

  • Introduction
  • Guidelines for contributing developers
    • Introduction
    • Before you start: development set up
    • Get started: areas of contribution
      • core contribution process
      • extras contribution process
    • Create a pull request
      • Pull request title conventions
      • Hints on pre-commit usage
      • Developer Certificate of Origin
    • Need help?
  • Backwards compatibility & breaking changes
    • When should I make a breaking change?
    • The Kedro release model
  • Contribute to the Kedro documentation
    • How do I rebuild the documentation after I make changes to it?
      • Set up to build Kedro documentation
      • Build the documentation
    • Extend Kedro documentation
      • Add new pages
      • Move or remove pages
      • Create a pull request
      • Help!
    • Kedro documentation style guide
      • Language
      • Formatting
      • Links
      • Capitalisation
      • Bullets
      • Notes
      • Kedro lexicon
      • Style
  • Join the Technical Steering Committee
    • Responsibilities of a maintainer
      • Product development
      • Community management
    • Requirements to become a maintainer
    • Application process
    • Voting process
      • Other issues or proposals
      • Adding or removing maintainers
  • How to deploy a development version of Kedro to Databricks
    • Introduction
    • Prerequisites
    • How to install a build of Kedro onto Databricks
      • Setup the Databricks CLI to test a Kedro build
      • How to use make databricks-build to test your Kedro build

API documentation

  • kedro
    • kedro.config
      • kedro.config.ConfigLoader
      • kedro.config.TemplatedConfigLoader
      • kedro.config.OmegaConfigLoader
      • kedro.config.MissingConfigException
    • kedro.datasets
      • kedro.datasets.api.APIDataSet
      • kedro.datasets.biosequence.BioSequenceDataSet
      • kedro.datasets.dask.ParquetDataSet
      • kedro.datasets.email.EmailMessageDataSet
      • kedro.datasets.geopandas.GeoJSONDataSet
      • kedro.datasets.holoviews.HoloviewsWriter
      • kedro.datasets.json.JSONDataSet
      • kedro.datasets.matplotlib.MatplotlibWriter
      • kedro.datasets.networkx.GMLDataSet
      • kedro.datasets.networkx.GraphMLDataSet
      • kedro.datasets.networkx.JSONDataSet
      • kedro.datasets.pandas.CSVDataSet
      • kedro.datasets.pandas.ExcelDataSet
      • kedro.datasets.pandas.FeatherDataSet
      • kedro.datasets.pandas.GBQQueryDataSet
      • kedro.datasets.pandas.GBQTableDataSet
      • kedro.datasets.pandas.GenericDataSet
      • kedro.datasets.pandas.HDFDataSet
      • kedro.datasets.pandas.JSONDataSet
      • kedro.datasets.pandas.ParquetDataSet
      • kedro.datasets.pandas.SQLQueryDataSet
      • kedro.datasets.pandas.SQLTableDataSet
      • kedro.datasets.pandas.XMLDataSet
      • kedro.datasets.pickle.PickleDataSet
      • kedro.datasets.pillow.ImageDataSet
      • kedro.datasets.plotly.JSONDataSet
      • kedro.datasets.plotly.PlotlyDataSet
      • kedro.datasets.redis.PickleDataSet
      • kedro.datasets.spark.DeltaTableDataSet
      • kedro.datasets.spark.SparkDataSet
      • kedro.datasets.spark.SparkHiveDataSet
      • kedro.datasets.spark.SparkJDBCDataSet
      • kedro.datasets.svmlight.SVMLightDataSet
      • kedro.datasets.tensorflow.TensorFlowModelDataset
      • kedro.datasets.text.TextDataSet
      • kedro.datasets.tracking.JSONDataSet
      • kedro.datasets.tracking.MetricsDataSet
      • kedro.datasets.video.VideoDataSet
      • kedro.datasets.yaml.YAMLDataSet
    • kedro.extras
      • kedro.extras.datasets
        • kedro.extras.datasets.api.APIDataSet
        • kedro.extras.datasets.biosequence.BioSequenceDataSet
        • kedro.extras.datasets.dask.ParquetDataSet
        • kedro.extras.datasets.email.EmailMessageDataSet
        • kedro.extras.datasets.geopandas.GeoJSONDataSet
        • kedro.extras.datasets.holoviews.HoloviewsWriter
        • kedro.extras.datasets.json.JSONDataSet
        • kedro.extras.datasets.matplotlib.MatplotlibWriter
        • kedro.extras.datasets.networkx.GMLDataSet
        • kedro.extras.datasets.networkx.GraphMLDataSet
        • kedro.extras.datasets.networkx.JSONDataSet
        • kedro.extras.datasets.pandas.CSVDataSet
        • kedro.extras.datasets.pandas.ExcelDataSet
        • kedro.extras.datasets.pandas.FeatherDataSet
        • kedro.extras.datasets.pandas.GBQQueryDataSet
        • kedro.extras.datasets.pandas.GBQTableDataSet
        • kedro.extras.datasets.pandas.GenericDataSet
        • kedro.extras.datasets.pandas.HDFDataSet
        • kedro.extras.datasets.pandas.JSONDataSet
        • kedro.extras.datasets.pandas.ParquetDataSet
        • kedro.extras.datasets.pandas.SQLQueryDataSet
        • kedro.extras.datasets.pandas.SQLTableDataSet
        • kedro.extras.datasets.pandas.XMLDataSet
        • kedro.extras.datasets.pickle.PickleDataSet
        • kedro.extras.datasets.pillow.ImageDataSet
        • kedro.extras.datasets.plotly.JSONDataSet
        • kedro.extras.datasets.plotly.PlotlyDataSet
        • kedro.extras.datasets.redis.PickleDataSet
        • kedro.extras.datasets.spark.DeltaTableDataSet
        • kedro.extras.datasets.spark.SparkDataSet
        • kedro.extras.datasets.spark.SparkHiveDataSet
        • kedro.extras.datasets.spark.SparkJDBCDataSet
        • kedro.extras.datasets.svmlight.SVMLightDataSet
        • kedro.extras.datasets.tensorflow.TensorFlowModelDataset
        • kedro.extras.datasets.text.TextDataSet
        • kedro.extras.datasets.tracking.JSONDataSet
        • kedro.extras.datasets.tracking.MetricsDataSet
        • kedro.extras.datasets.yaml.YAMLDataSet
      • kedro.extras.extensions
        • kedro.extras.extensions.ipython
      • kedro.extras.logging
        • kedro.extras.logging.color_logger
    • kedro.framework
      • kedro.framework.cli
        • kedro.framework.cli.catalog
        • kedro.framework.cli.cli
        • kedro.framework.cli.hooks
        • kedro.framework.cli.jupyter
        • kedro.framework.cli.micropkg
        • kedro.framework.cli.pipeline
        • kedro.framework.cli.project
        • kedro.framework.cli.registry
        • kedro.framework.cli.starters
        • kedro.framework.cli.utils
      • kedro.framework.context
        • kedro.framework.context.KedroContext
        • kedro.framework.context.KedroContextError
      • kedro.framework.hooks
        • kedro.framework.hooks.manager
        • kedro.framework.hooks.markers
        • kedro.framework.hooks.specs
      • kedro.framework.project
        • kedro.framework.project.configure_logging
        • kedro.framework.project.configure_project
        • kedro.framework.project.find_pipelines
        • kedro.framework.project.validate_settings
      • kedro.framework.session
        • kedro.framework.session.session
        • kedro.framework.session.shelvestore
        • kedro.framework.session.store
      • kedro.framework.startup
        • kedro.framework.startup.bootstrap_project
        • kedro.framework.startup.ProjectMetadata
    • kedro.io
      • kedro.io.AbstractDataSet
      • kedro.io.AbstractVersionedDataSet
      • kedro.io.DataCatalog
      • kedro.io.LambdaDataSet
      • kedro.io.MemoryDataSet
      • kedro.io.PartitionedDataSet
      • kedro.io.IncrementalDataSet
      • kedro.io.CachedDataSet
      • kedro.io.Version
      • kedro.io.DataSetAlreadyExistsError
      • kedro.io.DataSetError
      • kedro.io.DataSetNotFoundError
    • kedro.ipython
      • kedro.ipython.load_ipython_extension
      • kedro.ipython.magic_reload_kedro
      • kedro.ipython.reload_kedro
    • kedro.pipeline
      • kedro.pipeline.node
      • kedro.pipeline.modular_pipeline.pipeline
      • kedro.pipeline.Pipeline
      • kedro.pipeline.node.Node
      • kedro.pipeline.modular_pipeline.ModularPipelineError
    • kedro.runner
      • kedro.runner.run_node
      • kedro.runner.AbstractRunner
      • kedro.runner.ParallelRunner
      • kedro.runner.SequentialRunner
      • kedro.runner.ThreadRunner
    • kedro.utils
      • kedro.utils.load_obj
Kedro
  • Docs »
  • Frequently asked questions
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Frequently asked questions¶

Kedro project development¶

  • How do I write my own Kedro starter projects?

Working with Jupyter¶

  • How can I convert functions from Jupyter Notebooks into Kedro nodes?

  • How do I connect a Kedro project kernel to other Jupyter clients like JupyterLab?

Datasets and the Data Catalog¶

  • Can I read the same data file using two different dataset implementations?

Nodes and pipelines¶

  • How do I create a modular pipeline?

  • Can I use generator functions in a node?

Visualisation¶

  • Can I annotate a Kedro-Viz visualisation to show different data layers?

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