The target-redshift Meltano loader sends data into Amazon Redshift data warehouse after it was pulled from a source using an extractor.

Getting Started

Prerequisites

If you haven't already, follow the initial steps of the Getting Started guide:

  1. Install Meltano
  2. Create your Meltano project
  3. Add an extractor to pull data from a source

Installation and configuration

Using the Command Line Interface

  1. Add the target-redshift loader to your project using meltano add :

    meltano add loader target-redshift --variant transferwise
  2. Configure the settings below using meltano config .

Using Meltano UI

  1. Start Meltano UI using meltano ui:

    meltano ui
  2. Open the Loaders interface at http://localhost:5000/loaders.
  3. Click the “Add to project” button for “Amazon Redshift”.
  4. Choose "Add variant 'transferwise'".
  5. Configure the settings below in the “Configuration” interface that opens automatically.

Next steps

Follow the remaining steps of the Getting Started guide:

  1. Run a data integration (EL) pipeline

If you run into any issues, learn how to get help.

Settings

target-redshift requires the configuration of one of the following groups of settings:

These and other supported settings are documented below. To quickly find the setting you're looking for, use the Table of Contents at the top of the page.

host

Redshift host

How to use

Manage this setting using Meltano UI, meltano config, or an environment variable:

meltano config target-redshift set host <host>

export TARGET_REDSHIFT_HOST=<host>

port

Redshift port

How to use

Manage this setting using Meltano UI, meltano config, or an environment variable:

meltano config target-redshift set port 5439

export TARGET_REDSHIFT_PORT=5439

Database Name (dbname)

Redshift database name

How to use

Manage this setting using Meltano UI, meltano config, or an environment variable:

meltano config target-redshift set dbname <dbname>

export TARGET_REDSHIFT_DBNAME=<dbname>

User name (user)

Redshift user name

How to use

Manage this setting using Meltano UI, meltano config, or an environment variable:

meltano config target-redshift set user <user>

export TARGET_REDSHIFT_USER=<user>

password

Redshift password

How to use

Manage this setting using Meltano UI, meltano config, or an environment variable:

meltano config target-redshift set password <password>

export TARGET_REDSHIFT_PASSWORD=<password>

S3 Bucket name (s3_bucket)

AWS S3 bucket name

How to use

Manage this setting using Meltano UI, meltano config, or an environment variable:

meltano config target-redshift set s3_bucket <s3_bucket>

export TARGET_REDSHIFT_S3_BUCKET=<s3_bucket>

default_target_schema

  • Environment variable: TARGET_REDSHIFT_DEFAULT_TARGET_SCHEMA, alias: TARGET_REDSHIFT_SCHEMA
  • Default: $MELTANO_EXTRACT__LOAD_SCHEMA

Name of the schema where the tables will be created. If schema_mapping is not defined then every stream sent by the tap is loaded into this schema.

How to use

Manage this setting using Meltano UI, meltano config, or an environment variable:

meltano config target-redshift set default_target_schema <default_target_schema>

export TARGET_REDSHIFT_DEFAULT_TARGET_SCHEMA=<default_target_schema>

AWS profile name (aws_profile)

AWS profile name for profile based authentication. If not provided, AWS_PROFILE environment variable will be used.

How to use

Manage this setting using Meltano UI, meltano config, or an environment variable:

meltano config target-redshift set aws_profile <aws_profile>

export TARGET_REDSHIFT_AWS_PROFILE=<aws_profile>

AWS S3 Access Key ID (aws_access_key_id)

S3 Access Key Id. Used for S3 and Redshift copy operations. If not provided, AWS_ACCESS_KEY_ID environment variable will be used.

How to use

Manage this setting using Meltano UI, meltano config, or an environment variable:

meltano config target-redshift set aws_access_key_id <aws_access_key_id>

export TARGET_REDSHIFT_AWS_ACCESS_KEY_ID=<aws_access_key_id>

AWS S3 Secret Access Key (aws_secret_access_key)

S3 Secret Access Key. Used for S3 and Redshift copy operations. If not provided, AWS_SECRET_ACCESS_KEY environment variable will be used.

How to use

Manage this setting using Meltano UI, meltano config, or an environment variable:

meltano config target-redshift set aws_secret_access_key <aws_secret_access_key>

export TARGET_REDSHIFT_AWS_SECRET_ACCESS_KEY=<aws_secret_access_key>

AWS S3 Session Token (aws_session_token)

S3 AWS STS token for temporary credentials. If not provided, AWS_SESSION_TOKEN environment variable will be used.

How to use

Manage this setting using Meltano UI, meltano config, or an environment variable:

meltano config target-redshift set aws_session_token <aws_session_token>

export TARGET_REDSHIFT_AWS_SESSION_TOKEN=<aws_session_token>

AWS Redshift COPY role ARN (aws_redshift_copy_role_arn)

AWS Role ARN to be used for the Redshift COPY operation. Used instead of the given AWS keys for the COPY operation if provided - the keys are still used for other S3 operations

How to use

Manage this setting using Meltano UI, meltano config, or an environment variable:

meltano config target-redshift set aws_redshift_copy_role_arn <aws_redshift_copy_role_arn>

export TARGET_REDSHIFT_AWS_REDSHIFT_COPY_ROLE_ARN=<aws_redshift_copy_role_arn>

AWS S3 ACL (s3_acl)

S3 Object ACL

How to use

Manage this setting using Meltano UI, meltano config, or an environment variable:

meltano config target-redshift set s3_acl <s3_acl>

export TARGET_REDSHIFT_S3_ACL=<s3_acl>

S3 Key Prefix (s3_key_prefix)

A static prefix before the generated S3 key names. Using prefixes you can upload files into specific directories in the S3 bucket. Default(None)

How to use

Manage this setting using Meltano UI, meltano config, or an environment variable:

meltano config target-redshift set s3_key_prefix <s3_key_prefix>

export TARGET_REDSHIFT_S3_KEY_PREFIX=<s3_key_prefix>

COPY options (copy_options)

  • Environment variable: TARGET_REDSHIFT_COPY_OPTIONS
  • Default: EMPTYASNULL BLANKSASNULL TRIMBLANKS TRUNCATECOLUMNS TIMEFORMAT 'auto' COMPUPDATE OFF STATUPDATE OFF

Parameters to use in the COPY command when loading data to Redshift. Some basic file formatting parameters are fixed values and not recommended overriding them by custom values. They are like: CSV GZIP DELIMITER ',' REMOVEQUOTES ESCAPE.

How to use

Manage this setting using Meltano UI, meltano config, or an environment variable:

meltano config target-redshift set copy_options <copy_options>

export TARGET_REDSHIFT_COPY_OPTIONS=<copy_options>

batch_size_rows

Maximum number of rows in each batch. At the end of each batch, the rows in the batch are loaded into Redshift.

How to use

Manage this setting using Meltano UI, meltano config, or an environment variable:

meltano config target-redshift set batch_size_rows 100000

export TARGET_REDSHIFT_BATCH_SIZE_ROWS=100000

flush_all_streams

Flush and load every stream into Redshift when one batch is full. Warning - This may trigger the COPY command to use files with low number of records, and may cause performance problems.

How to use

Manage this setting using Meltano UI, meltano config, or an environment variable:

meltano config target-redshift set flush_all_streams true

export TARGET_REDSHIFT_FLUSH_ALL_STREAMS=true

parallelism

The number of threads used to flush tables. 0 will create a thread for each stream, up to parallelism_max. -1 will create a thread for each CPU core. Any other positive number will create that number of threads, up to parallelism_max.

How to use

Manage this setting using Meltano UI, meltano config, or an environment variable:

meltano config target-redshift set parallelism 0

export TARGET_REDSHIFT_PARALLELISM=0

max_parallelism

Max number of parallel threads to use when flushing tables.

How to use

Manage this setting using Meltano UI, meltano config, or an environment variable:

meltano config target-redshift set max_parallelism 16

export TARGET_REDSHIFT_MAX_PARALLELISM=16

default_target_schema_select_permissions

Grant USAGE privilege on newly created schemas and grant SELECT privilege on newly created tables to a specific list of users or groups. If schema_mapping is not defined then every stream sent by the tap is granted accordingly.

How to use

Manage this setting using Meltano UI, meltano config, or an environment variable:

meltano config target-redshift set default_target_schema_select_permissions <default_target_schema_select_permissions>

export TARGET_REDSHIFT_DEFAULT_TARGET_SCHEMA_SELECT_PERMISSIONS=<default_target_schema_select_permissions>

schema_mapping

Useful if you want to load multiple streams from one tap to multiple Redshift schemas. If the tap sends the stream_id in - format then this option overwrites the default_target_schema value. Note, that using schema_mapping you can overwrite the default_target_schema_select_permissions value to grant SELECT permissions to different groups per schemas or optionally you can create indices automatically for the replicated tables.

How to use

Manage this setting using Meltano UI, meltano config, or an environment variable:

meltano config target-redshift set schema_mapping '{...}'

export TARGET_REDSHIFT_SCHEMA_MAPPING='{...}'

disable_table_cache

By default the connector caches the available table structures in Redshift at startup. In this way it doesn’t need to run additional queries when ingesting data to check if altering the target tables is required. With disable_table_cache option you can turn off this caching. You will always see the most recent table structures but will cause an extra query runtime.

How to use

Manage this setting using Meltano UI, meltano config, or an environment variable:

meltano config target-redshift set disable_table_cache true

export TARGET_REDSHIFT_DISABLE_TABLE_CACHE=true

add_metadata_columns

Metadata columns add extra row level information about data ingestions, (i.e. when was the row read in source, when was inserted or deleted in redshift etc.) Metadata columns are creating automatically by adding extra columns to the tables with a column prefix SDC. The metadata columns are documented at https://transferwise.github.io/pipelinewise/data_structure/sdc-columns.html. Enabling metadata columns will flag the deleted rows by setting the _SDC_DELETED_AT metadata column. Without the add_metadata_columns option the deleted rows from singer taps will not be recongisable in Redshift.

How to use

Manage this setting using Meltano UI, meltano config, or an environment variable:

meltano config target-redshift set add_metadata_columns true

export TARGET_REDSHIFT_ADD_METADATA_COLUMNS=true

hard_delete

When hard_delete option is true then DELETE SQL commands will be performed in Redshift to delete rows in tables. It’s achieved by continuously checking the _SDC_DELETED_AT metadata column sent by the singer tap. Due to deleting rows requires metadata columns, hard_delete option automatically enables the add_metadata_columns option as well.

How to use

Manage this setting using Meltano UI, meltano config, or an environment variable:

meltano config target-redshift set hard_delete true

export TARGET_REDSHIFT_HARD_DELETE=true

data_flattening_max_level

Object type RECORD items from taps can be loaded into VARIANT columns as JSON (default) or we can flatten the schema by creating columns automatically. When value is 0 (default) then flattening functionality is turned off.

How to use

Manage this setting using Meltano UI, meltano config, or an environment variable:

meltano config target-redshift set data_flattening_max_level 0

export TARGET_REDSHIFT_DATA_FLATTENING_MAX_LEVEL=0

primary_key_required

Log based and Incremental replications on tables with no Primary Key cause duplicates when merging UPDATE events. When set to true, stop loading data if no Primary Key is defined.

How to use

Manage this setting using Meltano UI, meltano config, or an environment variable:

meltano config target-redshift set primary_key_required false

export TARGET_REDSHIFT_PRIMARY_KEY_REQUIRED=false

validate_records

Validate every single record message to the corresponding JSON schema. This option is disabled by default and invalid RECORD messages will fail only at load time by Redshift. Enabling this option will detect invalid records earlier but could cause performance degradation.

How to use

Manage this setting using Meltano UI, meltano config, or an environment variable:

meltano config target-redshift set validate_records true

export TARGET_REDSHIFT_VALIDATE_RECORDS=true

skip_updates

Do not update existing records when Primary Key is defined. Useful to improve performance when records are immutable, e.g. events

How to use

Manage this setting using Meltano UI, meltano config, or an environment variable:

meltano config target-redshift set skip_updates true

export TARGET_REDSHIFT_SKIP_UPDATES=true

compression

The compression method to use when writing files to S3 and running Redshift COPY.

How to use

Manage this setting using Meltano UI, meltano config, or an environment variable:

meltano config target-redshift set compression 

export TARGET_REDSHIFT_COMPRESSION=

slices

The number of slices to split files into prior to running COPY on Redshift. This should be set to the number of Redshift slices. The number of slices per node depends on the node size of the cluster - run SELECT COUNT(DISTINCT slice) slices FROM stv_slices to calculate this. Defaults to 1.

How to use

Manage this setting using Meltano UI, meltano config, or an environment variable:

meltano config target-redshift set slices 1

export TARGET_REDSHIFT_SLICES=1

Temp directory (temp_dir)

(Default: platform-dependent) Directory of temporary CSV files with RECORD messages.

How to use

Manage this setting using Meltano UI, meltano config, or an environment variable:

meltano config target-redshift set temp_dir <temp_dir>

export TARGET_REDSHIFT_TEMP_DIR=<temp_dir>

Looking for help?

If you're having trouble getting the target-redshift loader to work, look for an existing issue in its repository, file a new issue, or join the Meltano Slack community and ask for help in the #plugins-general channel.

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