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0 = 100h kms−1 Mpc−1 (2) where h is a dimensionless number parameterizing our ignorance. (Word on the street is that 0.6 0.9.) The inverse of the Hubble constant is the Hubble time t H t H ≡ 1 H 0 = 9.78×109 h−1 yr = 3.09× 1017 h−1 s (3) and the speed of light c times the Hubble time is the Hubble distance D H D H ≡ c H 0. Download Redshift + Crack Keygen PATCH. Home; Submit File/Crack; Contact; Redshift + crack serial keygen. January 12, 2020. Copy Download Link (paste this to your. VGAutoS 2.0.0; Movie Splitter 2.11; MPEG To Zune Converter/Splitter 1.70; TheSeeker 0.6.3.
pandas_redshiftThis package is designed to make it easier to get data from redshift into a pandas DataFrame and vice versa.The pandas_redshift package only supports python3.
Version 2.0.0 note: Version 2.0.0 introduces a change which may not be compatible with users current workflows/piplines. Previous to 2.0.0 the default when writing a DataFrame to redshift was to write all data types as VARCHAR. In the current version the redshift data types will be inferred from the DataFrame dtypes. Redshift GUI 0.2.1 add to watchlist send us an update. 4 screenshots: runs on: Windows All file size: 521 KB filename: RedshiftGUI-0.2.1-Windows-x86.exe main category: System.
InstallationExampleConnect to redshift. If port is not supplied it will be set to amazon default 5439.
As of release 1.1.2 you can exclude the password if you are using a .pgpass file.
Query redshift and return a pandas DataFrame.
Write a pandas DataFrame to redshift. Requires access to an S3 bucket and previously running pr.connect_to_redshift.
If the table currently exists IT WILL BE DROPPED and then the pandas DataFrame will be put in it's place.
If you set append = True the table will be appended to (if it exists).
Other options:
As of v1.1.2 you can specify the region (necessary if the S3 bucket is in a different location than Redshift).
Redshift data types: http://docs.aws.amazon.com/redshift/latest/dg/c_Supported_data_types.html
Finally close the cursor, commit and close the database connection, and remove variables from the environment.
As this package is largely a layer over psycopg2 a convenience function has been added to execute and commit sql queries that don't have anything to do with your local machine (for example creating a new table).
If you encounter the error:psycopg2.InternalError: current transaction is aborted, commands ignored until end of transaction block
you can access the pyscopg2 internals with the following:
Adjust logging levels:
Latest version Released: 
Singer.io target for loading data to Amazon Redshift - PipelineWise compatible
Project descriptionSinger target that loads data into Amazon Redshift following the Singer spec.
This is a PipelineWise compatible target connector.

0 = 100h kms−1 Mpc−1 (2) where h is a dimensionless number parameterizing our ignorance. (Word on the street is that 0.6 0.9.) The inverse of the Hubble constant is the Hubble time t H t H ≡ 1 H 0 = 9.78×109 h−1 yr = 3.09× 1017 h−1 s (3) and the speed of light c times the Hubble time is the Hubble distance D H D H ≡ c H 0. Download Redshift + Crack Keygen PATCH. Home; Submit File/Crack; Contact; Redshift + crack serial keygen. January 12, 2020. Copy Download Link (paste this to your. VGAutoS 2.0.0; Movie Splitter 2.11; MPEG To Zune Converter/Splitter 1.70; TheSeeker 0.6.3.
pandas_redshiftThis package is designed to make it easier to get data from redshift into a pandas DataFrame and vice versa.The pandas_redshift package only supports python3.
Version 2.0.0 note: Version 2.0.0 introduces a change which may not be compatible with users current workflows/piplines. Previous to 2.0.0 the default when writing a DataFrame to redshift was to write all data types as VARCHAR. In the current version the redshift data types will be inferred from the DataFrame dtypes. Redshift GUI 0.2.1 add to watchlist send us an update. 4 screenshots: runs on: Windows All file size: 521 KB filename: RedshiftGUI-0.2.1-Windows-x86.exe main category: System.
InstallationExampleConnect to redshift. If port is not supplied it will be set to amazon default 5439.
As of release 1.1.2 you can exclude the password if you are using a .pgpass file.
Query redshift and return a pandas DataFrame.
Write a pandas DataFrame to redshift. Requires access to an S3 bucket and previously running pr.connect_to_redshift.
If the table currently exists IT WILL BE DROPPED and then the pandas DataFrame will be put in it's place.
If you set append = True the table will be appended to (if it exists).
Other options:
As of v1.1.2 you can specify the region (necessary if the S3 bucket is in a different location than Redshift).
Redshift data types: http://docs.aws.amazon.com/redshift/latest/dg/c_Supported_data_types.html
Finally close the cursor, commit and close the database connection, and remove variables from the environment.
As this package is largely a layer over psycopg2 a convenience function has been added to execute and commit sql queries that don't have anything to do with your local machine (for example creating a new table).
If you encounter the error:psycopg2.InternalError: current transaction is aborted, commands ignored until end of transaction block
you can access the pyscopg2 internals with the following:
Adjust logging levels:
Latest version Released: 
Singer.io target for loading data to Amazon Redshift - PipelineWise compatible
Project descriptionSinger target that loads data into Amazon Redshift following the Singer spec.
This is a PipelineWise compatible target connector.
How to use itThe recommended method of running this target is to use it from PipelineWise. When running it from PipelineWise you don't need to configure this tap with JSON files and most of things are automated. Please check the related documentation at Target Redshift
If you want to run this Singer Target independently please read further.
InstallFirst, make sure Python 3 is installed on your system or follow theseinstallation instructions for Mac orUbuntu.
It's recommended to use a virtualenv:
or
To runLike any other target that's following the singer specificiation:
some-singer-tap | target-redshift --config [config.json] 1blocker mac.
Redshift 2 0 1 =It's reading incoming messages from STDIN and using the properites in config.json to upload data into Amazon Redshift.
Note: To avoid version conflicts run tap and targets in separate virtual environments.
Configuration settingsRunning the the target connector requires a config.json file. Example with the minimal settings:
Full list of options in config.json: https://supplessrolte1980.mystrikingly.com/blog/mac-ssd-and-hdd.
PropertyTypeRequired?Description
hostStringYesRedshift Host
portIntegerYesRedshift Port
userStringYesRedshift User
passwordStringYesRedshift Password
dbnameStringYesRedshift Database name
aws_profileStringNoAWS profile name for profile based authentication. If not provided, AWS_PROFILE environment variable will be used.
aws_access_key_idStringNoS3 Access Key Id. Used for S3 and Redshfit copy operations. If not provided, AWS_ACCESS_KEY_ID environment variable will be used.
aws_secret_access_keyStringNoS3 Secret Access Key. Used for S3 and Redshfit copy operations. If not provided, AWS_SECRET_ACCESS_KEY environment variable will be used.
aws_session_tokenStringNoS3 AWS STS token for temporary credentials. If not provided, AWS_SESSION_TOKEN environment variable will be used.
aws_redshift_copy_role_arnStringNoAWS 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
s3_aclStringNoS3 Object ACL
s3_bucketStringYesS3 Bucket name
s3_key_prefixString(Default: None) A static prefix before the generated S3 key names. Using prefixes you can upload files into specific directories in the S3 bucket.
copy_optionsString(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
batch_size_rowsInteger(Default: 100000) Maximum number of rows in each batch. At the end of each batch, the rows in the batch are loaded into Redshift.
flush_all_streamsBoolean(Default: False) 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.
parallelismInteger(Default: 0) 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.
max_parallelismInteger(Default: 16) Max number of parallel threads to use when flushing tables.
default_target_schemaStringName 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.
default_target_schema_select_permissionsStringGrant USAGE privilege on newly created schemas and grant SELECT privilege on newly created tables to a specific list of users or groups. Example: {'users': ['user_1','user_2'], 'groups': ['group_1', 'group_2']} If schema_mapping is not defined then every stream sent by the tap is granted accordingly.
schema_mappingObjectUseful 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.
Note: This is an experimental feature and recommended to use via PipelineWise YAML files that will generate the object mapping in the right JSON format. For further info check a [PipelineWise YAML Example]
disable_table_cacheBoolean(Default: False) 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.
add_metadata_columnsBoolean(Default: False) 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.
hard_deleteBoolean(Default: False) 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.
data_flattening_max_levelInteger(Default: 0) 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.
primary_key_requiredBoolean(Default: True) 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.
validate_recordsBoolean(Default: False) 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 Snowflake. Enabling this option will detect invalid records earlier but could cause performance degradation.
skip_updatesBooleanNo(Default: False) Do not update existing records when Primary Key is defined. Useful to improve performance when records are immutable, e.g. events
compressionStringNoThe compression method to use when writing files to S3 and running Redshift COPY. The currently supported methods are gzip or bzip2. Defaults to none (').
slicesIntegerNoThe 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.
temp_dirString(Default: platform-dependent) Directory of temporary CSV files with RECORD messages.
To run tests:Install python dependencies in a virtual env:
To run unit tests:
To run integration tests define environment variables first:
To run pylint:Install python dependencies and run python linter
LicenseApache License Version 2.0
See LICENSE to see the full text.
Release historyRelease notifications | RSS feed Redshift 2019 1.6.0 
 1.5.0 
 1.4.1 
 1.4.0 
 1.3.0 
 1.2.1 Apple system 5.
Anymp4 android data recovery 2 0 12. 1.2.0 
 1.1.0 
 1.0.8 
 1.0.7 
 1.0.6 
 1.0.5 
 1.0.4 
 1.0.3 
 1.0.2 
Redshift 2 0 15 1.0.1 
 1.0.0 
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Property	Type	Required?	Description
host	String	Yes	Redshift Host
port	Integer	Yes	Redshift Port
user	String	Yes	Redshift User
password	String	Yes	Redshift Password
dbname	String	Yes	Redshift Database name
aws_profile	String	No	AWS profile name for profile based authentication. If not provided, `AWS_PROFILE` environment variable will be used.
aws_access_key_id	String	No	S3 Access Key Id. Used for S3 and Redshfit copy operations. If not provided, `AWS_ACCESS_KEY_ID` environment variable will be used.
aws_secret_access_key	String	No	S3 Secret Access Key. Used for S3 and Redshfit copy operations. If not provided, `AWS_SECRET_ACCESS_KEY` environment variable will be used.
aws_session_token	String	No	S3 AWS STS token for temporary credentials. If not provided, `AWS_SESSION_TOKEN` environment variable will be used.
aws_redshift_copy_role_arn	String	No	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
s3_acl	String	No	S3 Object ACL
s3_bucket	String	Yes	S3 Bucket name
s3_key_prefix	String	(Default: None) A static prefix before the generated S3 key names. Using prefixes you can upload files into specific directories in the S3 bucket.
copy_options	String	(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`
batch_size_rows	Integer	(Default: 100000) Maximum number of rows in each batch. At the end of each batch, the rows in the batch are loaded into Redshift.
flush_all_streams	Boolean	(Default: False) 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.
parallelism	Integer	(Default: 0) 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.
max_parallelism	Integer	(Default: 16) Max number of parallel threads to use when flushing tables.
default_target_schema	String	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.
default_target_schema_select_permissions	String	Grant USAGE privilege on newly created schemas and grant SELECT privilege on newly created tables to a specific list of users or groups. Example: `{'users': ['user_1','user_2'], 'groups': ['group_1', 'group_2']}` If `schema_mapping` is not defined then every stream sent by the tap is granted accordingly.
schema_mapping	Object	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. Note: This is an experimental feature and recommended to use via PipelineWise YAML files that will generate the object mapping in the right JSON format. For further info check a [PipelineWise YAML Example]
disable_table_cache	Boolean	(Default: False) 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.
add_metadata_columns	Boolean	(Default: False) 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.
hard_delete	Boolean	(Default: False) 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.
data_flattening_max_level	Integer	(Default: 0) 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.
primary_key_required	Boolean	(Default: True) 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.
validate_records	Boolean	(Default: False) 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 Snowflake. Enabling this option will detect invalid records earlier but could cause performance degradation.
skip_updates	Boolean	No	(Default: False) Do not update existing records when Primary Key is defined. Useful to improve performance when records are immutable, e.g. events
compression	String	No	The compression method to use when writing files to S3 and running Redshift `COPY`. The currently supported methods are `gzip` or `bzip2`. Defaults to none (`'`).
slices	Integer	No	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`.
temp_dir	String	(Default: platform-dependent) Directory of temporary CSV files with RECORD messages.

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