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DynamoDB Query

The DynamoDB Query component uses the DynamoDB API to retrieve data and load it into a table—this stages the data, so the table is reloaded each time. You can then use transformation components to enrich and manage the data in permanent tables.

Warning

This component is potentially destructive. If the target table undergoes a change in structure, it will be recreated. Otherwise, the target table is truncated. Setting the load option Recreate Target Table to Off will prevent both recreation and truncation. Do not modify the target table structure manually.

If the component requires access to a cloud provider, it will use the cloud credentials associated with your environment to access resources.

Properties

Reference material is provided below for the Connect, Configure, Destination, and Advanced Settings properties.

Connect

Authentication Method = drop-down

The method of authenticating the DynamoDB Query component:

  • Environment Credentials: Use the credentials set for the current environment.
  • Access/Secret Key: Complete the Access Key and Secret Key properties.

Access Key = string

(Access/Secret Key only)

Your DynamoDB access key. To learn how to generate an access key and secret key, read Setting Up DynamoDB (Web Service).

Secret Key = drop-down

(Access/Secret Key only)

Choose the secret definition that represents your credentials for this connector. Your credentials should be saved as a secret definition before using this component.

Click Manage to navigate to the Secret definitions tab to review your secret definitions and to add new secret definitions. Read Secret definitions to learn how to create a secret definition.

Region = drop-down

Select the AWS region where the DynamoDB service is located.

Connection Options = column editor

  • Parameter: A JDBC parameter supported by the database driver. The available parameters are explained in data model. Manual setup is not usually required, since sensible defaults are assumed.
  • Value: A value for the given parameter.

Click the Text Mode toggle at the bottom of the Connection Options dialog to open a multi-line editor that lets you add items in a single block. For more information, read Text mode.

To use grid variables, tick the Use Grid Variable checkbox at the bottom of the Connection Options dialog.

Configure

Mode = drop-down

  • Basic: This mode will build a query for you using settings from the Schema, Data Source, Data Selection, Data Source Filter, Combine Filters, and Limit parameters. In most cases, this mode will be sufficient.
  • Advanced: This mode will require you to write an SQL-like query to call data from the service you're connecting to. The available fields and their descriptions are documented in the data model.

There are some special pseudo columns that can form part of a query filter, but are not returned as data. This is fully described in the data model.

Note

While the query is exposed in an SQL-like language, the exact semantics can be surprising, for example, filtering on a column can return more data than not filtering on it. This is an impossible scenario with regular SQL.

SQL Query = code editor

This is an SQL-like SELECT query, written in the SQL accepted by your cloud data warehouse. Treat collections as table names, and fields as columns. Only available in Advanced mode.

For detailed information about tables and views for this connector, use the data model, found below.

Data Source = drop-down

Select a single data source to be extracted from the source system and loaded into a table in the destination. The source system defines the data sources available. Use multiple components to load multiple data sources.

For detailed information about tables and views for this connector, refer to the data model below, where applicable.

Data Selection = dual listbox

Choose one or more columns to return from the query. The columns available are dependent upon the data source selected. Move columns left-to-right to include in the query.

To use grid variables, tick the Use Grid Variable checkbox at the bottom of the Data Selection dialog.

Data Source Filter = column editor

Define one or more filter conditions that each row of data must meet to be included in the load.

  • Input Column: Select an input column. The available input columns vary depending upon the data source.
  • Qualifier:
    • Is: Compares the column to the value using the comparator.
    • Not: Reverses the effect of the comparison, so "Equals" becomes "Not equals", "Less than" becomes "Greater than or equal to", etc.
  • Comparator: Choose a method of comparing the column to the value. Possible comparators include: "Equal to", "Greater than", "Less than", "Greater than or equal to", "Less than or equal to", "Like", "Null". "Equal to" can match exact strings and numeric values, while other comparators, such as "Greater than" and "Less than", will work only with numerics. The "Like" operator allows the wildcard character % to be used at the start and end of a string value to match a column. The Null operator matches only null values, ignoring whatever the value is set to. Not all data sources support all comparators, meaning that it is likely that only a subset of the above comparators will be available to choose from.
  • Value: The value to be compared.

Click the Text Mode toggle at the bottom of the Connection Options dialog to open a multi-line editor that lets you add items in a single block. For more information, read Text mode.

Combine Filters = drop-down

The data source filters you have defined can be combined using either And or Or logic. If And, then all filter conditions must be satisfied to load the data row. If Or, then only a single filter condition must be satisfied. The default is And.

If you have only one filter, or no filters, this parameter is essentially ignored.

Limit = integer

Set a numeric value to limit the number of rows that are loaded. The default is 100. To load all rows from your data source, delete the default 100 and leave the field empty (i.e. do not set a limit).

Destination

Select your cloud data warehouse.

Type = drop-down

  • Standard: The data will be staged in your storage location before being loaded into a table. This is the only setting currently available.

Warehouse = drop-down

The Snowflake warehouse used to run the queries. The special value [Environment Default] uses the warehouse defined in the environment. Read Overview of Warehouses to learn more.

Database = drop-down

The Snowflake database. The special value [Environment Default] uses the database defined in the environment. Read Databases, Tables and Views - Overview to learn more.

Schema = drop-down

The Snowflake schema. The special value [Environment Default] uses the schema defined in the environment. Read Database, Schema, and Share DDL to learn more.

Target Table = string

The name of the table to be created in your Snowflake database. This table will be recreated and will drop any existing table of the same name.

You can use a Table Input component in a transformation pipeline to access and transform this data after it has been loaded.

Primary Keys = dual listbox

Select one or more columns to be designated as the table's primary key.

Stage = drop-down

Select a managed stage. The special value, [Custom], will create a stage "on the fly" for use solely within this component.

Stage Platform = drop-down

Choose where the data is staged before being loaded into your Snowflake table using the drop-down menu.

  • Existing Amazon S3 Location: Activates the S3 Staging Area property, allowing users to specify a custom staging area on Amazon S3. The Stage Authentication property is also activated, letting users select a method of authenticating the data staging.
  • Existing Azure Blob Storage Location: Activates the Storage Account and Blob Container properties, allowing users to specify a custom staging location on Azure. The Stage Authentication property is also activated, letting users select a method of authenticating the data staging.
  • Existing Google Cloud Storage Location: Activates the Storage Integration and GCS Staging Area properties, allowing users to specify a custom staging area within Google Cloud Storage.
  • Snowflake Managed: Create and use a temporary internal stage on Snowflake for staging the data. This stage, along with the staged data, will cease to exist after loading is complete. This is the default setting.

Stage Authentication = drop-down

Select an authentication method for data staging. Only available when Stage Platform is set to either Existing Amazon S3 Location or Existing Azure Blob Storage Location.

  • Credentials: Uses the credentials configured in the environment. If no credentials have been configured, an error will occur.
  • Storage Integration: Use a Snowflake storage integration to authentication data staging. A storage integration is a Snowflake object that stores a generated identity and access management (IAM) entity for your external cloud storage, along with an optional set of allowed or blocked storage locations. To learn more, read Create Storage Integration.

Storage Integration = drop-down

Select a Snowflake storage integration from the drop-down list. Storage integrations are required to permit Snowflake to read data from and write to your cloud storage location (Amazon S3, Azure Blob Storage, Google Cloud Storage) and must be set up in advance of selection.

To use this property with an Amazon S3 or Azure Blob Storage location, set Stage Authentication to Storage Integration. For Google Cloud Storage, Storage Integration is the only stage authentication method.

S3 Staging Area = drop-down

Select an S3 bucket for temporary storage. Ensure your access credentials have S3 access and permission to write to the bucket. Read Secret definitions for details on setting up access. The temporary objects created in this bucket will be removed again after the load completes, they are not kept.

Use Accelerated Endpoint = boolean

When True, data will be loaded via the s3-accelerate endpoint. Consider the following information:

  • Enabling acceleration can enhance the speed at which data is transferred to the chosen S3 bucket. However, enhanced speed is not always guaranteed. Read Amazon S3 Transfer Acceleration Speed Comparison to compare S3 Direct versus S3 Accelerated Transfer speeds.
  • Users must manually set the acceleration configuration of an existing bucket. To learn more, read PutBucketAccelerateConfiguration in the AWS API Reference.
  • This property is only available if the selected S3 bucket has Amazon S3 Transfer Acceleration enabled. For more information, including how to enable this feature, read Getting started with Amazon S3 Transfer Acceleration.
  • Cases may arise where Data Productivity Cloud can't determine whether the chosen S3 bucket has Amazon S3 Transfer Acceleration enabled. In these cases, Designer will reveal this property for user input on a "just in case" basis. In these cases, Designer may return a validation message that reads "OK - Bucket could not be validated." You may also encounter cases where, if you do not have permission to get the status of the acceleration configuration (namely, the permission, GetAccelerateConfiguration), Designer will again show this property "just in case".
  • The default setting is False.

Storage Account = drop-down

Select a storage account with your desired blob container to be used for staging the data. For more information, read Storage account overview.

Blob Container = drop-down

Select a Blob container to be used for staging the data. For more information, read Introduction to Azure Blob storage.

GCS Staging Area = drop-down

The URL and path of the target Google Cloud Storage bucket to be used for staging the queried data. For more information, read Creating storage buckets.

Catalog = drop-down

Select a Databricks Unity Catalog. The special value [Environment Default] uses the catalog defined in the environment. Selecting a catalog will determine which databases are available in the next parameter.

Schema (Database) = drop-down

The Databricks schema. The special value [Environment Default] uses the schema defined in the environment. Read Create and manage schemasto learn more.

Table = string

The name of the table to be created in your Snowflake database. This table will be recreated and will drop any existing table of the same name.

You can use a Table Input component in a transformation pipeline to access and transform this data after it has been loaded.

Stage Platform = drop-down

Choose where the data is staged before being loaded into your Databricks table using the drop-down menu.

  • AWS S3: Lets users specify a custom staging area on Amazon S3.
  • Azure Blob: Lets users specify a custom staging area on Azure Blob storage.
  • Personal Staging (deprecated): Uses a Databricks personal staging location. This option has been deprecated by Databricks.
  • Volume: Use a pre-existing Databricks volume to stage your data. Only external volumes are available.

S3 Staging Area = drop-down

Select an S3 bucket for temporary storage. Ensure your access credentials have S3 access and permission to write to the bucket. Read Secret definitions for details on setting up access. The temporary objects created in this bucket will be removed again after the load completes, they are not kept.

Storage Account = drop-down

Select a storage account with your desired Blob container to be used for staging the data. For more information, read Storage account overview.

Blob Container = drop-down

Select a Blob container to be used for staging the data. For more information, read Introduction to Azure Blob storage.

Volume = drop-down

Select a Databricks volume.

Type = drop-down

  • Standard: The data will be staged in your storage location before being loaded into a table. This is the only setting currently available.

Use Accelerated Endpoint = boolean

When True, data will be loaded via the s3-accelerate endpoint.

  • Enabling acceleration can enhance the speed at which data is transferred to the chosen S3 bucket. However, enhanced speed is not always guaranteed. Read Amazon S3 Transfer Acceleration Speed Comparison to compare S3 Direct versus S3 Accelerated Transfer speeds.
  • Users must manually set the acceleration configuration of an existing bucket. To learn more, see PutBucketAccelerateConfiguration in the API Reference, available at the AWS documentation.
  • This property is only available if the selected S3 bucket has Amazon S3 Transfer Acceleration enabled. For more information, including how to enable this feature, read Getting started with Amazon S3 Transfer Acceleration.
  • Cases may arise where Data Productivity Cloud cannot determine whether the chosen S3 bucket has Amazon S3 Transfer Acceleration enabled. In these cases, Designer will reveal this property for user input on a "just in case" basis. In these cases, Designer may return a validation message that reads "OK - Bucket could not be validated." You may also encounter cases where, if you do not have permission to get the status of the acceleration configuration (namely, the permission, GetAccelerateConfiguration) Designer will again show this property "just in case".
  • The default setting is False.

Schema = drop-down

Select the table schema. The special value [Environment Default] uses the schema defined in the environment. For more information on using multiple schemas, read Schemas.

Target Table = string

The name of the table to be created in your Amazon Redshift schema. This table will be recreated and will drop any existing table of the same name.

You can use a Table Input component in a transformation pipeline to access and transform this data after it has been loaded.

S3 Staging Area = S3 bucket

Select an S3 bucket for temporary storage. Ensure your access credentials have S3 access and permission to write to the bucket. Read Secret definitions for details on setting up access. The temporary objects created in this bucket will be removed again after the load completes, they are not kept.

Distribution Style = drop-down

Table distribution is critical to good performance. Read the Distribution styles documentation for more information.

  • All: Copy rows to all nodes in the Redshift cluster.
  • Auto: (Default) Allow Redshift to manage your distribution style.
  • Even: Distribute rows around the Redshift cluster evenly.
  • Key: Distribute rows around the Redshift cluster according to the value of a key column.

Sort Key = dual listbox

Sort keys are critical to good performance. Read Working with sort keys for more information.

This property is optional, and lets you specify one or more columns from the input that should be set as the table's sort key.

Sort Key Options = drop-down

Decide whether the sort key is of a compound or interleaved variety.

Primary Key = dual listbox

Select one or more columns to be designated as the table's primary key.

Advanced Settings

Load Options = multiple drop-downs

  • Clean Staged Files: Destroy staged files after loading data. Default is On.
  • String Null is Null: Converts any strings equal to null into a null value. This is case-sensitive and only works with entirely lower-case strings. Default is Off.
  • Recreate Target Table: Choose whether the component recreates its target table before the data load. If Off, the existing table will be used. Default is On.
  • File Prefix: Give staged file names a prefix of your choice. Default is empty (no prefix).
  • Trim String Columns: Remove leading and trailing characters from a string column. Default is On.
  • Compression Type: Set the compression type to either gzip (default) or None.

Load Options = multiple drop-downs

  • Clean Staged Files: Destroy staged files after loading data. Default is On.
  • String Null is Null: Converts any strings equal to null into a null value. This is case-sensitive and only works with entirely lower-case strings. Default is Off.
  • Recreate Target Table: Choose whether the component recreates its target table before the data load. If Off, the existing table will be used. Default is On.
  • File Prefix: Give staged file names a prefix of your choice. Default is empty (no prefix).
  • Compression Type: Set the compression type to either gzip (default) or None.

Load Options = multiple drop-downs

  • Comp Update: Apply automatic compression to the target table. Default is On.
  • Stat Update: Automatically update statistics when filling a table. Default is On. In this case, it is updating the statistics of the target table.
  • Clean S3 Objects: Automatically remove UUID-based objects on the S3 bucket. Default is On. Effectively, users decide here whether to keep the staged data in the S3 bucket or not.
  • String Null is Null: Converts any strings equal to "null" into a null value. This is case-sensitive and only works with entirely lower-case strings. Default is On.
  • Recreate Target Table: Choose whether the component recreates its target table before the data load. If Off, the existing table will be used. Default is On.
  • File Prefix: Give staged file names a prefix of your choice. When this Load Option is selected, users should set their preferred prefix in the text field.
  • Compression Type: Set the compression type to either gzip (default) or None.

Encryption = drop-down

Decide how the files are encrypted inside the S3 bucket. This property is available when using an existing Amazon S3 location for staging.

KMS Key ID = drop-down

The ID of the KMS encryption key you have chosen to use in the Encryption property.

Auto Debug = drop-down

Choose whether to automatically log debug information about your load. These logs can be found in the task history and should be included in support requests concerning the component. Turning this on will override any debugging connection options.

Debug Level = drop-down

The level of verbosity with which your debug information is logged. Levels above 1 can log huge amounts of data and result in slower execution. These logs can be found in the Message field of the task details after pipeline execution and should be included in support requests concerning the component.

  1. Will log the query, the number of rows returned by it, the start of execution and the time taken, and any errors.
  2. Will log everything included in Level 1, plus cache queries and additional information about the request, if applicable.
  3. Will additionally log the body of the request and the response.
  4. Will additionally log transport-level communication with the data source. This includes SSL negotiation.
  5. Will additionally log communication with the data source, as well as additional details that may be helpful in troubleshooting problems. This includes interface commands.

Data model

The JDBC driver for this component models DynamoDB APIs as relational tables and stored procedures, which are documented in the data model. You'll also find API limitations and requirements. The connection option SupportEnhancedSQL is set to true by default and typically circumvents most API limitations.

Snowflake Databricks Amazon Redshift
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