Databricks Migration Tool

This is a migration package to log all Databricks resources for backup and/or migrating to another Databricks workspace. Migration allows a Databricks organization to move resources between Databricks Workspaces, to move between different cloud providers, or to move to different regions / accounts.

This package is based on python 3.6 and DBR 6.x+ releases.
Python 3.7 or above is recommended if one is also exporting/importing MLflow objects.

Note: This tool does not support windows currently since path resolution is different than mac / linux.

This package also uses credentials from the Databricks CLI.

Pre-Requisites

To use this migration tool, you'll need:

An environment running linux with python, pip, git, and the databricks CLI installed.
Admin access to both the old and new databricks accounts in the form of a Personal Access Token.

Setup

Click to expand & collapse tasks

1. Generate Tokens

Generate Access Tokens for both the old and new databricks accounts

Click User Settings Icon Settings in the lower left corner of your Databricks workspace
Click on Access Tokens tab
Click on Generate New Token button.
Copy the generated token and store in a secure location.

Be sure to keep a file with the url for both the old and new databricks account Add the old and new token and the old and new Instance ID if applicable. You'll need easy access to all of these things when running the migration tool.

2. Setup databricks-cli profiles

In order to run the migration tool from your linux shell

Create a profile for the old workspace by typing:

databricks configure --token --profile oldWS

In this case oldWS is the profile name you'll refer to for running the migration tool export_db.py file within the old databricks account.

When you use the databricks cli configure command, you'll be prompted for 2 things

Databricks Host (should begin with https://): When this happens, enter the old databricks workspace URL that you captured in your file above.
Token: When this happens, paste in the token you generated for the old databricks account.

Repeat the steps above for the new databricks account and change the oldWS profile name to something like newWS in order to keep track of which account you're exporting FROM and which account you're inporting TO.

Create a profile for the New workspace by typing:

databricks configure --token --profile newWS

In this case newWS is the profile name you'll refer to for running the migration tool import_db.py file within the new databricks account.

3. Install package dependencies

In order to set up the python environment, clone this repository and python3 setup.py install from the top-level project directory.

Migration Components

To use the migration tool see the details below to start running the tool in the order recommended to properly migrate files.

Support Matrix for Import and Export Operations:

Component	Export	Import
Users / Groups	Supported	Supported
Clusters (w/ ACLs)	Supported	Supported
Notebooks	Supported	Supported
Notebooks ACLs	Supported	Supported
Metastore	Supported	Supported
Jobs (w/ ACLs)	Supported	Supported
Libraries	Supported	Unsupported
Secrets	Supported	Supported
Table ACLs	Supported	Supported
ML Models	Supported*	Supported*

Note on MLFlow Migration:
MLFlow asset migration is currently in alpha.

Note on DBFS Data Migration:
DBFS is a protected object storage location on AWS and Azure. Please contact your Databricks support team for information about migrating DBFS resources.

Note on User Migration:
During user / group import, users will be notified of the new workspace and account by default. To disable this behavior, please contact your Databricks account team.

Import using pipeline

The recommended method of exporting and importing is by using the Pipeline contained in migration_pipeline.py. This pipeline performs all export and import steps sequentially, and includes checkpointing parallelization features.

Pipeline parameters

python migration_pipeline.py -h
usage: migration_pipeline.py [-h] [--profile PROFILE] [--azure] [--silent] [--no-ssl-verification] [--debug] [--set-export-dir SET_EXPORT_DIR]
                             [--cluster-name CLUSTER_NAME] [--notebook-format {DBC,SOURCE,HTML}] [--overwrite-notebooks] [--archive-missing]
                             [--repair-metastore-tables] [--metastore-unicode] [--skip-failed] [--session SESSION] [--dry-run] [--export-pipeline] [--import-pipeline]
                             [--validate-pipeline] [--validate-source-session VALIDATE_SOURCE_SESSION] [--validate-destination-session VALIDATE_DESTINATION_SESSION]
                             [--use-checkpoint] [--skip-tasks SKIP_TASKS [SKIP_TASKS ...]] [--num-parallel NUM_PARALLEL] [--retry-total RETRY_TOTAL]
                             [--retry-backoff RETRY_BACKOFF] [--start-date START_DATE]
                             [--exclude-work-item-prefixes EXCLUDE_WORK_ITEM_PREFIXES [EXCLUDE_WORK_ITEM_PREFIXES ...]]

Export user(s) workspace artifacts from Databricks

optional arguments:
  -h, --help            show this help message and exit
  --profile PROFILE     Profile to parse the credentials
  --azure               Run on Azure. (Default is AWS)
  --silent              Silent all logging of export operations.
  --no-ssl-verification
                        Set Verify=False when making http requests.
  --debug               Enable debug logging
  --set-export-dir SET_EXPORT_DIR
                        Set the base directory to export artifacts
  --cluster-name CLUSTER_NAME
                        Cluster name to export the metastore to a specific cluster. Cluster will be started.
  --notebook-format {DBC,SOURCE,HTML}
                        Choose the file format to download the notebooks (default: DBC)
  --overwrite-notebooks
                        Flag to overwrite notebooks to forcefully overwrite during notebook imports
  --archive-missing     Import all missing users into the top level /Archive/ directory.
  --repair-metastore-tables
                        Repair legacy metastore tables
  --metastore-unicode   log all the metastore table definitions including unicode characters
  --skip-failed         Skip retries for any failed hive metastore exports.
  --session SESSION     If set, pipeline resumes from latest checkpoint of given session; Otherwise, pipeline starts from beginning and creates a new session.
  --dry-run             Dry run the pipeline i.e. will not execute tasks if true.
  --export-pipeline     Execute all export tasks.
  --import-pipeline     Execute all import tasks.
  --validate-pipeline   Validate exported data between source and destination.
  --validate-source-session VALIDATE_SOURCE_SESSION
                        Session used by exporting source workspace. Only used for --validate-pipeline.
  --validate-destination-session VALIDATE_DESTINATION_SESSION
                        Session used by exporting destination workspace. Only used for --validate-pipeline.
  --use-checkpoint      use checkpointing to restart from previous state
  --skip-tasks SKIP_TASKS [SKIP_TASKS ...]
                        List of tasks to skip from the pipeline.
  --num-parallel NUM_PARALLEL
                        Number of parallel threads to use to export/import
  --retry-total RETRY_TOTAL
                        Total number or retries when making calls to Databricks API
  --retry-backoff RETRY_BACKOFF
                        Backoff factor to apply between retry attempts when making calls to Databricks API
  --start-date START_DATE
                        start-date format: YYYY-MM-DD. If not provided, defaults to past 30 days. Currently, only used for exporting ML runs objects.
  --exclude-work-item-prefixes EXCLUDE_WORK_ITEM_PREFIXES [EXCLUDE_WORK_ITEM_PREFIXES ...]
                        List of prefixes to skip export for log_all_workspace_items

Exporting the Workspace

To export a workspace, run:

python3 migration_pipeline.py --profile $SRC_PROFILE --export-pipeline --use-checkpoint [--session $SESSION_ID]

Where $SRC_PROFILE is the Databricks profile for the source workspace, as configured during Setup, and $SESSION_ID is an optional session identifier used for subsequent checkpoint runs. All data is exported to a folder named according to the $SESSION_ID value under the logs folder - “logs/$SESSION_ID”. If $SESSION_ID is not specified, a random value will be generated.

Recommended parameters and checkpointing

As a starting point, we recommend using the following parameter values:

retry-total=30
num-parallel=8
retry-backoff=1.0

These can be adjusted per your scenario if needed; in general, if API limits are being hit, you can increase retry-backoff, decrease num-parallel, or both.

If script failure occurs, you can safely rerun the same command with --use-checkpoint and --session $SESSION_ID to let the migration pick up from the previous checkpoint and rerun.

Updating the AWS Account ID

If your source and destination workspaces are in different accounts, you will need to update the Instance Profile ARN accordingly during the migration. To do this, run the following command after exporting the workspace assets:

python3 export_db.py --profile $SRC_PROFILE --use-checkpoint --old-account-id $OLD_AWS_ACCT_ID --update-account-id $NEW_AWS_ACCT_ID --set-export-dir $EXPORT_DIR/$SESSION_ID

Where EXPORT_DIR/SESSION_ID is the directory and session ID used by your export job, SRC_PROFILE is the profile used to export the source workspace, OLD_AWS_ACCT_ID is the source AWS account ID, and NEW_AWS_ACCT_ID is the destination AWS account ID. Note that this will only update the ARN in the Instance Profiles; the same instance profiles must still exist in the destination workspace.

Importing the Workspace

To import into a target workspace, run:

python3 migration_pipeline.py --profile $DST_PROFILE --import-pipeline --use-checkpoint [--session $SESSION_ID]

The same recommended parameters as above apply in the import workflow, and similarly, if a failure occurs, --use-checkpoint can be used to rerun from the last checkpoint.

Validation

Simple workspace object validation can be performed once the import is completed by first exporting the contents of the target workspace:

python3 migration_pipeline.py --profile $DST_PROFILE --export-pipeline --use-checkpoint --cluster-name

And then running the validate_pipeline.sh script:

./validate_pipeline.sh $SRC_EXPORT_SESSION_ID $DST_EXPORT_SESSION_ID

Once this completes, check the console summary, as well as the logs folder (where a new folder should be generated).

Import using step-by-step tools (not recommended)

If desired, export_db.py and import_db.py can be run in a stepwise fashion. This is the legacy mode of running the tools, and in general is not recommended. If running the scripts separately, the following order of operations applies:

Export users and groups
Export cluster templates
Export notebook metadata (listing of all notebooks)
Export notebook content
Export job templates
Export Hive Metastore data
Export Table ACLs

By default, artifacts are stored in the logs/ directory, and azure_logs/ for Azure artifacts. This is configurable with the --set-export-dir flag to specify the log directory.

Export Help Text

$ python export_db.py --help
usage: export_db.py [-h] [--users] [--workspace]
                    [--notebook-format {DBC,SOURCE,HTML}] [--download]
                    [--libs] [--clusters] [--jobs] [--metastore] [--secrets]
                    [--metastore-unicode] [--cluster-name CLUSTER_NAME]
                    [--database DATABASE] [--iam IAM] [--skip-failed]
                    [--mounts] [--azure] [--profile PROFILE]
                    [--single-user SINGLE_USER] [--export-home EXPORT_HOME]
                    [--export-groups EXPORT_GROUPS] [--workspace-acls]
                    [--workspace-top-level-only] [--silent]
                    [--no-ssl-verification] [--debug] [--reset-exports]
                    [--set-export-dir SET_EXPORT_DIR] [--pause-all-jobs]
                    [--unpause-all-jobs]
                    [--update-account-id UPDATE_ACCOUNT_ID]
                    [--old-account-id OLD_ACCOUNT_ID]
                    [--replace-old-email REPLACE_OLD_EMAIL]
                    [--update-new-email UPDATE_NEW_EMAIL]
                    [--bypass-windows-check]
                    
Export full workspace artifacts from Databricks

optional arguments:
  -h, --help            show this help message and exit
  --users               Download all the users and groups in the workspace
  --workspace           Log all the notebook paths in the workspace. (metadata
                        only)
  --notebook-format {DBC,SOURCE,HTML}
                        Choose the file format to download the notebooks
                        (default: DBC)
  --download            Download all notebooks for the environment
  --libs                Log all the libs for the environment
  --clusters            Log all the clusters for the environment
  --jobs                Log all the job configs for the environment
  --metastore           log all the metastore table definitions
  --metastore-unicode   log all the metastore table definitions including
                        unicode characters
  --table-acls          log all table ACL grant and deny statements
  --cluster-name CLUSTER_NAME
                        Cluster name to export the metastore to a specific
                        cluster. Cluster will be started.
  --database DATABASE   Database name to export for the metastore and table
                        ACLs. Single database name supported
  --iam IAM             IAM Instance Profile to export metastore entires
  --skip-failed         Skip retries for any failed hive metastore exports.
  --mounts              Log all mount points.
  --azure               Run on Azure. (Default is AWS)
  --profile PROFILE     Profile to parse the credentials
  --export-home EXPORT_HOME
                        User workspace name to export, typically the users
                        email address
  --export-groups EXPORT_GROUPS
                        Group names to export as a set. Includes group, users,
                        and notebooks.
  --workspace-acls      Permissions for workspace objects to export
  --workspace-top-level-only
                        Download only top level notebook directories
  --silent              Silent all logging of export operations.
  --no-ssl-verification
                        Set Verify=False when making http requests.
  --debug               Enable debug logging
  --reset-exports       Clear export directory
  --set-export-dir SET_EXPORT_DIR
                        Set the base directory to export artifacts
  --pause-all-jobs      Pause all scheduled jobs
  --unpause-all-jobs    Unpause all scheduled jobs
  --update-account-id UPDATE_ACCOUNT_ID
                        Set the account id for instance profiles to a new
                        account id
  --old-account-id OLD_ACCOUNT_ID
                        Old account ID to filter on
  --replace-old-email REPLACE_OLD_EMAIL
                        Old email address to update from logs
  --update-new-email UPDATE_NEW_EMAIL
                        New email address to replace the logs

Import Help Text

$ python import_db.py --help
usage: import_db.py [-h] [--users] [--workspace] [--workspace-top-level]
                    [--workspace-acls] [--notebook-format {DBC,SOURCE,HTML}]
                    [--import-home IMPORT_HOME] [--import-groups]
                    [--archive-missing] [--libs] [--clusters] [--jobs]
                    [--metastore] [--metastore-unicode] [--get-repair-log]
                    [--cluster-name CLUSTER_NAME] [--skip-failed] [--azure]
                    [--profile PROFILE] [--single-user SINGLE_USER]
                    [--no-ssl-verification] [--silent] [--debug]
                    [--set-export-dir SET_EXPORT_DIR] [--pause-all-jobs]
                    [--unpause-all-jobs] [--delete-all-jobs]
                                        
Import full workspace artifacts into Databricks

optional arguments:
  -h, --help            show this help message and exit
  --users               Import all the users and groups from the logfile.
  --workspace           Import all notebooks from export dir into the
                        workspace.
  --workspace-top-level
                        Import all top level notebooks from export dir into
                        the workspace. Excluding Users dirs
  --notebook-format {DBC,SOURCE,HTML}
                        Choose the file format of the notebook to import
                        (default: DBC)
  --workspace-acls      Permissions for workspace objects to import
  --import-home IMPORT_HOME
                        User workspace name to import, typically the users
                        email address
  --import-groups       Groups to import into a new workspace. Includes group
                        creation and user notebooks.
  --archive-missing     Import all missing users into the top level /Archive/
                        directory.
  --libs                Import all the libs from the logfile into the
                        workspace.
  --clusters            Import all the cluster configs for the environment
  --jobs                Import all job configurations to the environment.
  --metastore           Import the metastore to the workspace.
  --metastore-unicode   Import all the metastore table definitions with
                        unicode characters
  --table-acls          Import table acls to the workspace.
  --get-repair-log      Report on current tables requiring repairs
  --cluster-name CLUSTER_NAME
                        Cluster name to import the metastore to a specific
                        cluster. Cluster will be started.
  --skip-failed         Skip missing users that do not exist when importing
                        user notebooks
  --azure               Run on Azure. (Default is AWS)
  --profile PROFILE     Profile to parse the credentials
  --no-ssl-verification
                        Set Verify=False when making http requests.
  --silent              Silent all logging of import operations.
  --debug               Enable debug logging
  --set-export-dir SET_EXPORT_DIR
                        Set the base directory to import artifacts if the
                        export dir was a customized
  --pause-all-jobs      Pause all scheduled jobs
  --unpause-all-jobs    Unpause all scheduled jobs
  --delete-all-jobs     Delete all jobs

Users and Groups

This section uses the SCIM API to export / import user and groups.
Instance Profiles API used to export instance profiles that are tied to user/group entitlements.
For AWS users, this section will log the instance profiles used for IAM access to resources.

To export users / groups, use the following: (The profile name DEMO will be replaced with the profile you defined for your old databricks account)

python export_db.py --profile DEMO --users

To import these users:

python import_db.py --profile NEW_DEMO --users

If you plan to use this tool to export multiple workspaces, you can set the --set-export-dir directory to log artifacts into separate logging directories.

Clusters

The section uses the Clusters APIs

python export_db.py --profile DEMO --clusters

This will export the following:

Cluster templates + ACLs
Instance pool definitions
Cluster policies + ACLs

python import_db.py --profile NEW_DEMO --clusters

If you experience errors when you try to import the clusters, it may be that you need to modify the clusters file from the logs directory to include the new instance profile if it's not the same as the one in the old databricks account.

To make changes to a cluster name to match the new databricks account

you must edit the clusters log file after export. You do this by looking at the clusters file and identifying the old cluster instance profile which will include the old account number and the name of the instance profile.

OLD profile text from an AWS Databricks account: arn:aws:iam::111111111111:instance-profile/profileName

The account number (111111111111) and profileName need to be found and replaced to migrate to the new account which may have a different account number and instance profile.

To modify the clusters.log file run this sed operation

sed -i 's/old-text/new-text/g' input.txt

https://unix.stackexchange.com/questions/32907/what-characters-do-i-need-to-escape-when-using-sed-in-a-sh-script

Notebooks

This section uses the Workspace API

This part is a 3 part process.

Download all notebook locations and paths
Download all notebook contents for every path
Download all workspace ACLs

python export_db.py --profile DEMO --workspace
python export_db.py --profile DEMO --download
python export_db.py --profile DEMO --workspace-acls

To import into a new workspace:

python import_db.py --profile NEW_DEMO --workspace [--archive-missing]
python import_db.py --profile NEW_DEMO --workspace-acls

If users have left your organization, their artifacts (notebooks / job templates) still exists. However, their user object no longer exists. During the migration, we can keep the old users notebooks into the top level directory /Archive/{username}@domain.com Use the --archive-missing option to put these artifacts in the archive folder.

Single User Export/Import
The tool supports exporting single user workspaces using the following command:

# export a single users workspace
python export_db.py --profile DEMO --export-home [email protected]

The corollary is the --import-home option:

python import_db.py --profile NEW_DEMO --import-home [email protected]

This will include notebooks, directories, and their corresponding ACLs.

Jobs

This section uses the Jobs API
Job ACLs are exported and imported with this option.

python export_db.py --profile DEMO --jobs

If we're unable to find old cluster ids that are no longer available, we'll reset the job template to use a new default cluster.

python import_db.py --profile NEW_DEMO --jobs

Imported jobs into the new workspace are paused by default. We do not want to have 2 jobs run simultaneously. Admins must pause their jobs with Databricks defined schedules using the following option:

python export_db.py --profile DEMO --pause-all-jobs

Un-pause all jobs in the new workspace:

python import_db.py --profile NEW_DEMO --unpause-all-jobs

Hive Metastore

This section uses an API to remotely run Spark commands on a cluster, this API is called Execution Context

By default, this will launch a small cluster in the data/ folder to export the Hive Metastore data. If you need a specific IAM role to export the metastore, use the --cluster-name option to connect to a specific cluster.

By default, we will edit the cluster for every defined IAM role to loop through all failed exports in case the tool was missing IAM permissions. To disable looping through all failed exports, you can pass in --skip-failed

# export all metastore entries and brute force loop through all instance profiles / IAM roles
python export_db.py --profile DEMO --metastore

# export all metastore entries on the default cluster without retries
python export_db.py --profile DEMO --metastore --skip-failed 

# export all metastore entries on a specific cluster
python export_db.py --profile DEMO --metastore --cluster-name "Test"

# export all tables within a specific database
python export_db.py --profile DEMO --metastore --cluster-name "Test" --database "my_db"

import all metastore entries

python import_db.py --profile newDEMO --metastore

To find legacy Hive tables that need to be repaired after a successful import, run the following:

python import_db.py --profile newDEMO --get-repair-log

Once completed, it will upload a log to the destination location. Use this repair notebook to import into the destination environment to repair all tables.

Table ACLs

The Table ACLs component includes all objects to which access is controlled using DENY and GRANT SQL statements:

Catalog: included if all databases are exported
- Database: included
  - Table: included
  - View: included (they are treated like tables with ObjectType TABLE)
Anonymous Function: included
Any File: included

Unsupported object type:

User Function: not included yet

This section uses the API to run notebooks on a cluster to perform the export and import. (For details, please refer to the export table ACL notebook or the import table ACL notebook)

By default, this will launch a small cluster in the data/ folder with acls suffix to export the table ACL data. (This cluster needs to have table ACLs enabled, and it must be run with an admin user)

# export all table ACL entries 
python export_db.py --profile DEMO --table-acls

# export all table ACL entries within a specific database
python export_db.py --profile DEMO --table-acls --database "my_db"

For large workspaces it is not uncommon to encounter some ACLs that cause problems when exporting: in such cases, a special log entry is made (marked with ERROR_!!!) and the export continues. At the end error counts will be provided, and the notebooks mentioned above contain detailed information on how to investigate any issues. Most errors are encountered with objects that are no longer functional anyway.

Export Groups by Name

This functionality exports group(s), their members, and corresponding notebooks.
This assumes an empty export directory to simplify the number of operations needed.
This does not include IAM roles as those likely change while moving across workspaces.

# reset the export directory and export a set of groups
python export_db.py --reset-export && python export_db.py --profile DEMO --export-groups 'groupA,groupB'

# import the groups that were exported
python import_db.py --profile newDEMO --import-groups

Export / Import Top Level Notebooks

This will export all notebooks that are not a part of the /Users/ directories to help migrate notebooks that are outside of personal workspace directories. Usually, these will be notebooks in the '/Shared/' directory.

# reset the export directory and export the top level directories / notebooks
python export_db.py --profile DEMO --reset-export && python export_db.py --profile DEMO --workspace-top-level-only
# if ACLs are enabled, export the ACLs as well
python export_db.py --profile DEMO --workspace-acls

# import the groups that were exported
python import_db.py --profile newDEMO --workspace-top-level
# apply acls if needed 
python import_db.py --profile newDEMO --workspace-acls

Export / Import of Secrets

This will export secret to allow migration of secrets to a new workspace.
There is a limit to the size of the secret value which will print an error if this fails.

# to export you must use a cluster
python export_db.py --profile DEMO --secrets --cluster-name "my_cluster"
# to import, you do not need a cluster
python import_db.py --profile newDEMO --secrets

(Alpha version) Export / Import of MLFlow experiments, experiment permissions, and runs objects

Note: Registered model, model version, and metric history are not supported yet.

This will export and import the specified MLflow objects. Because MLFlow objects depend on other object types such as workspace directories, notebooks, etc this command should run after the other objects are successfully exported/imported.

mlflow-runs are by default only exported for the past 30 days worth of data. The user can specify other dates but should be aware of the performance impacts.

export

python3 export_db.py --profile $SRC --mlflow-experiments --use-checkpoint --num-parallel 4 
python3 export_db.py --profile $SRC --mlflow-experiments-permissions --use-checkpoint --num-parallel 4
python3 export_db.py --profile $SRC --mlflow-runs --use-checkpoint --num-parallel 4 --start-date 2022-02-26

import

python3 import_db.py --profile $DST --src-profile $SRC --mlflow-experiments --use-checkpoint --num-parallel 4
python3 import_db.py --profile $DST --src-profile $SRC  --mlflow-experiments-permissions --use-checkpoint --num-parallel 4 
python3 import_db.py --profile $DST --src-profile $SRC  --mlflow-runs --use-checkpoint --num-parallel 4

Limitations:

Note: To disable ssl verification pass the flag --no-ssl-verification. If still getting SSL Error add the following to your current bash shell:

export REQUESTS_CA_BUNDLE=""
export CURL_CA_BUNDLE=""

Instance profiles (AWS only): Group access to instance profiles will take precedence. If a user is added to the role directly, and has access via a group, only the group access will be granted during a migration.
Clusters: Cluster creator will be seen as the single admin user who migrated all the clusters. (Relevant for billing purposes)
- Cluster creator tags cannot be updated. Added a custom tag named OriginalCreator with the original cluster creator for DBU tracking.
Jobs: Job owners will be seen as the single admin user who migrate the job configurations. (Relevant for billing purposes)
- Jobs with existing clusters that no longer exist will be reset to the default cluster type
- Jobs with older legacy instances will fail with unsupported DBR or instance types. See release notes for the latest supported releases.

Name		Name	Last commit message	Last commit date
Latest commit History 289 Commits
.github/workflows		.github/workflows
data		data
dbclient		dbclient
jumpbox		jumpbox
pipeline		pipeline
tasks		tasks
test		test
validate		validate
.gitignore		.gitignore
CONTRIBUTING.md		CONTRIBUTING.md
LICENSE		LICENSE
METASTORE.md		METASTORE.md
NOTICE		NOTICE
README.md		README.md
__init__.py		__init__.py
checkpoint_service.py		checkpoint_service.py
export_db.py		export_db.py
import_db.py		import_db.py
logging_utils.py		logging_utils.py
migration_pipeline.py		migration_pipeline.py
setup.py		setup.py
test_connection.py		test_connection.py
thread_safe_writer.py		thread_safe_writer.py
threading_utils.py		threading_utils.py
validate_pipeline.sh		validate_pipeline.sh
validation_metastores.log		validation_metastores.log
validation_notebooks.log		validation_notebooks.log
wmconstants.py		wmconstants.py

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Databricks Migration Tool

Table of Contents

Pre-Requisites

Setup

Migration Components

Import using pipeline

Pipeline parameters

Exporting the Workspace

Recommended parameters and checkpointing

Updating the AWS Account ID

Importing the Workspace

Validation

Export Help Text

Import Help Text

Users and Groups

Clusters

Notebooks

Jobs

Hive Metastore

import all metastore entries

Table ACLs

Export Groups by Name

Export / Import Top Level Notebooks

Export / Import of Secrets

(Alpha version) Export / Import of MLFlow experiments, experiment permissions, and runs objects

Limitations:

About

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arunpamulapati/migrate

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Repository files navigation

Databricks Migration Tool

Table of Contents

Pre-Requisites

Setup

Migration Components

Import using pipeline

Pipeline parameters

Exporting the Workspace

Recommended parameters and checkpointing

Updating the AWS Account ID

Importing the Workspace

Validation

Export Help Text

Import Help Text

Users and Groups

Clusters

Notebooks

Jobs

Hive Metastore

import all metastore entries

Table ACLs

Export Groups by Name

Export / Import Top Level Notebooks

Export / Import of Secrets

(Alpha version) Export / Import of MLFlow experiments, experiment permissions, and runs objects

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