Readers are a group of tasks that can read data from different DB and cloud storages. In Jobs, all the tasks run in real-time.

There are eight(8) Readers tasks in Jobs. All the readers tasks contains the following tabs:

• Meta Information: Configure the meta information same as doing in pipeline components.

• Preview Data: Only ten(10) random data can be previewed in this tab only when the task is running in Development mode.

• Preview schema: Spark schema of the reading data will be shown in this tab.

• Logs: Logs of the tasks will display here.

HDFS stands for Hadoop Distributed File System. It is a distributed file system designed to store and manage large data sets in a reliable, fault-tolerant, and scalable way. HDFS is a core component of the Apache Hadoop ecosystem and is used by many big data applications.

This task reads the file located in HDFS (Hadoop Distributed File System).

Configuring the Meta Information tab fields

Drag the HDFS reader task to the Workspace and click on it to open the related configuration tabs for the same. The Meta Information tab opens by default.

• Host IP Address: Enter the host IP address for HDFS.

• Port: Enter the Port.

• Zone: Enter the Zone for HDFS. Zone is a special directory whose contents will be transparently encrypted upon write and transparently decrypted upon read.

• File Type: Select the File Type from the drop down. The supported file types are:

  • CSV: The Header and Infer Schema fields get displayed with CSV as the selected File Type. Enable Header option to get the Header of the reading file and enable Infer Schema option to get true schema of the column in the CSV file.

  • JSON: The Multiline and Charset fields get displayed with JSON as the selected File Type. Check-in the Multiline option if there is any multiline string in the file.

  • PARQUET: No extra field gets displayed with PARQUET as the selected File Type.

  • AVRO: This File Type provides two drop-down menus.

    • Compression: Select an option out of the Deflate and Snappy options.

    • Compression Level: This field appears for the Deflate compression option. It provides 0 to 9 levels via a drop-down menu.

  • XML: Select this option to read XML file. If this option is selected, the following fields will get displayed:

    • Infer schema: Enable this option to get true schema of the column.

    • Path: Provide the path of the file.

    • Root Tag: Provide the root tag from the XML files.

    • Row Tags: Provide the row tags from the XML files.

    • Join Row Tags: Enable this option to join multiple row tags.

  • Path: Provide the path of the file.

  • Partition Columns: Provide a unique Key column name to partition data in Spark.

This task is used to read data from MongoDB collection.

Configuring the Meta Information tab fields

Drag the MongoDB reader task to the Workspace and click on it to open the related configuration tabs for the same. The Meta Information tab opens by default.

• Connection Type: Select the connection type from the drop-down:

  • Standard

  • SRV

  • Connection String

• Port (*): Provide the Port number (It appears only with the Standard connection type).

• Host IP Address (*): The IP address of the host.

• Username (*): Provide a username.

• Password (*): Provide a valid password to access the MongoDB.

• Database Name (*): Provide the name of the database where you wish to write data.

• Additional Parameters: Provide details of the additional parameters.

• Cluster Shared: Enable this option to horizontally partition data across multiple servers.

• Schema File Name: Upload Spark Schema file in JSON format.

• Query: Please provide Mongo Aggregation query in this field.

This task is used to read the data from the following databases: MYSQL, MSSQL, Oracle, ClickHouse, Snowflake, PostgreSQL, Redshift.

Configuring the Meta Information tab fields

Drag the DB reader task to the Workspace and click on it to open the related configuration tabs for the same. The Meta Information tab opens by default.

• Host IP Address: Enter the Host IP Address for the selected driver.

• Port: Enter the port for the given IP Address.

• Database name: Enter the Database name.

• Table name: Provide a single or multiple table names. If multiple table name has be given, then enter the table names separated by comma(,).

• User name: Enter the user name for the provided database.

• Password: Enter the password for the provided database.

• Driver: Select the driver from the drop down. There are 7 drivers supported here: MYSQL, MSSQL, Oracle, ClickHouse, Snowflake, PostgreSQL, Redshift.

• Fetch Size: Provide the maximum number of records to be processed in one execution cycle.

• Create Partition: This is used for performance enhancement. It's going to create the sequence of indexing. Once this option is selected, the operation will not execute on server.

• Partition By: This option will appear once create partition option is enabled. There are two options under it:

  • Auto Increment: The number of partitions will be incremented automatically.

  • Index: The number of partitions will be incremented based on the specified Partition column.

• Query: Enter the spark SQL query in this field for the given table or table(s). Please refer the below image for making query on multiple tables.

This task is used to read data from Azure blob container.

Configuring the Meta Information tab fields

Drag the Azure Blob reader task to the Workspace and click on it to open the related configuration tabs for the same. The Meta Information tab opens by default.

• Read using: There are three(3) options available under this tab:

  1. Shared Access Signature

  2. Secret Key

  3. Principal Secret

Read using Shared Access Signature

Provide the following details:

1. Shared Access Signature: This is a URI that grants restricted access rights to Azure Storage resources.

2. Account Name: Provide the Azure account name.

3. Container: Provide the container name from where the file is located and which has to be read.

4. File type: There are four(5) types of file extensions are available under it:

   • CSV: The Header and Infer Schema fields get displayed with CSV as the selected File Type. Enable Header option to get the Header of the reading file and enable Infer Schema option to get true schema of the column in the CSV file.

   • JSON: The Multiline and Charset fields get displayed with JSON as the selected File Type. Check-in the Multiline option if there is any multiline string in the file.

   • PARQUET: No extra field gets displayed with PARQUET as the selected File Type.

   • AVRO: This File Type provides two drop-down menus.

     • Compression: Select an option out of the Deflate and Snappy options.

     • Compression Level: This field appears for the Deflate compression option. It provides 0 to 9 levels via a drop-down menu.

   • XML: Select this option to read XML file. If this option is selected, the following fields will get displayed:

     • Infer schema: Enable this option to get true schema of the column.

     • Path: Provide the path of the file.

     • Root Tag: Provide the root tag from the XML files.

     • Row Tags: Provide the row tags from the XML files.

     • Join Row Tags: Enable this option to join multiple row tags.

5. Path: This option will appear once the file type is selected. Enter the path where the selected file type is located.

6. Read Directory: Check in this box to read the specified directory.

7. Query: Provide Spark SQL query in this field.

Read using Secret Key Option

Provide the following details:

• Account Key: Enter the Azure account key. In Azure, an account key is a security credential that is used to authenticate access to storage resources, such as blobs, files, queues, or tables, in an Azure storage account.

• Account Name: Provide the Azure account name.

• Container: Provide the container name from where the blob is located. A container is a logical unit of storage in Azure Blob Storage that can hold blobs. It is similar to a directory or folder in a file system, and it can be used to organize and manage blobs.

• File type: There are four(5) types of file extensions are available under it:

  • CSV: The Header and Infer Schema fields get displayed with CSV as the selected File Type. Enable Header option to get the Header of the reading file and enable Infer Schema option to get true schema of the column in the CSV file.

  • JSON: The Multiline and Charset fields get displayed with JSON as the selected File Type. Check-in the Multiline option if there is any multiline string in the file.

  • PARQUET: No extra field gets displayed with PARQUET as the selected File Type.

  • AVRO: This File Type provides two drop-down menus.

    • Compression: Select an option out of the Deflate and Snappy options.

    • Compression Level: This field appears for the Deflate compression option. It provides 0 to 9 levels via a drop-down menu.

  • XML: Select this option to read XML file. If this option is selected, the following fields will get displayed:

    • Infer schema: Enable this option to get true schema of the column.

    • Path: Provide the path of the file.

    • Root Tag: Provide the root tag from the XML files.

    • Row Tags: Provide the row tags from the XML files.

    • Join Row Tags: Enable this option to join multiple row tags.

• Path: This option will appear once the file type is selected. Enter the path where the selected file type is located.

• Read Directory: Check in this box to read the specified directory.

• Query: Provide Spark SQL query in this field.

Read using Principal Secret

Provide the following details:

• Client ID: Provide Azure Client ID. The client ID is the unique Application (client) ID assigned to your app by Azure AD when the app was registered.

• Tenant ID: Provide the Azure Tenant ID. Tenant ID (also known as Directory ID) is a unique identifier that is assigned to an Azure AD tenant, which represents an organization or a developer account. It is used to identify the organization or developer account that the application is associated with.

• Client Secret: Enter the Azure Client Secret. Client Secret (also known as Application Secret or App Secret) is a secure password or key that is used to authenticate an application to Azure AD.

• Account Name: Provide the Azure account name.

• Container: Provide the container name from where the blob is located. A container is a logical unit of storage in Azure Blob Storage that can hold blobs. It is similar to a directory or folder in a file system, and it can be used to organize and manage blobs.

• Query: Provide Spark SQL query in this field.

• File type: There are four(5) types of file extensions are available under it:

  • CSV: The Header and Infer Schema fields get displayed with CSV as the selected File Type. Enable Header option to get the Header of the reading file and enable Infer Schema option to get true schema of the column in the CSV file.

  • JSON: The Multiline and Charset fields get displayed with JSON as the selected File Type. Check-in the Multiline option if there is any multiline string in the file.

  • PARQUET: No extra field gets displayed with PARQUET as the selected File Type.

  • AVRO: This File Type provides two drop-down menus.

    • Compression: Select an option out of the Deflate and Snappy options.

    • Compression Level: This field appears for the Deflate compression option. It provides 0 to 9 levels via a drop-down menu.

  • XML: Select this option to read XML file. If this option is selected, the following fields will get displayed:

    • Infer schema: Enable this option to get true schema of the column.

    • Path: Provide the path of the file.

    • Root Tag: Provide the root tag from the XML files.

    • Row Tags: Provide the row tags from the XML files.

    • Join Row Tags: Enable this option to join multiple row tags.

This task reads the file from Amazon S3 bucket.

Please follow the below mentioned steps to configure meta information of S3 reader task:

Drag the S3 reader task to the Workspace and click on it to open the related configuration tabs for the same. The Meta Information tab opens by default.

Configuration fields when SNS Monitor is disabled

• Bucket Name (*): Enter S3 bucket name.

• Region (*): Provide the S3 region.

• Access Key (*): Access key shared by AWS to login..

• Secret Key (*): Secret key shared by AWS to login

• Table (*): Mention the Table or object name which is to be read

• File Type (*): Select a file type from the drop-down menu (CSV, JSON, PARQUET, AVRO, XML are the supported file types)

• Limit: Set a limit for the number of records.

• Query: Insert an SQL query (it supports query containing a join statement as well)​.

Configuration fields when SNS Monitor is enabled

• Access Key (*): Access key shared by AWS to login

• Secret Key (*): Secret key shared by AWS to login

• Table (*): Mention the Table or object name which has to be read

• File Type (*): Select a file type from the drop-down menu (CSV, JSON, PARQUET, AVRO, XML are the supported file types)

• Limit: Set limit for the number of records

• Query: Insert an SQL query (it supports query containing a join statement as well)

Partition Columns

Provide a unique Key column name to partition data in Spark.

Amazon Athena is an interactive query service that easily analyzes data directly in Amazon Simple Storage Service (Amazon S3) using standard SQL. With a few actions in the AWS Management Console, you can point Athena at your data stored in Amazon S3 and begin using standard SQL to run ad-hoc queries and get results in seconds.

Athena Query Executer task enables users to read data directly from the external table created in AWS Athena.

Configuring the Meta Information tab fields

• Region: Enter the region name where the bucket is located.

• Access Key: Enter the AWS Access Key of the account that must be used.

• Secret Key: Enter the AWS Secret Key of the account that must be used.

• Table Name: Enter the name of the external table created in Athena.

• Database Name: Name of the database in Athena in which the table has been created.

• Limit: Enter the number of records to be read from the table.

• Data Source: Enter the Data Source name configured in Athena. Data Source in Athena refers to your data's location, typically an S3 bucket.

• Workgroup: Enter the Workgroup name configured in Athena. The Workgroup in Athena is a resource type to separate query execution and query history between Users, Teams, or Applications running under the same AWS account.

• Query location: Enter the path where the results of the queries done in the Athena query editor are saved in the CSV format. Users can find this path under the Settings tab in the Athena query editor as Query Result Location.

• Query: Enter the Spark SQL query.

Sample Spark SQL query that can be used in Athena Reader:

This task can read the data from the Network pool of Sandbox.

Configuring the Meta Information tab fields

Drag the Sandbox reader task to the Workspace and click on it to open the related configuration tabs for the same. The Meta Information tab opens by default.

• Storage Type: This field is pre-defined.

• Sandbox File: Select the file name from the drop-down.

• File Type: Select the file type from the drop down.

• There are four(5) types of file extensions are available under it:

  • CSV: The Header and Infer Schema fields get displayed with CSV as the selected File Type. Enable Header option to get the Header of the reading file and enable Infer Schema option to get true schema of the column in the CSV file.

  • JSON: The Multiline and Charset fields get displayed with JSON as the selected File Type. Check-in the Multiline option if there is any multiline string in the file.

  • PARQUET: No extra field gets displayed with PARQUET as the selected File Type.

  • AVRO: This File Type provides two drop-down menus.

    • Compression: Select an option out of the Deflate and Snappy options.

    • Compression Level: This field appears for the Deflate compression option. It provides 0 to 9 levels via a drop-down menu.

  • XML: Select this option to read XML file. If this option is selected, the following fields will get displayed:

• Query: Provide Spark SQL query in this field.

Elasticsearch is an open-source search and analytics engine built on top of the Apache Lucene library. It is designed to help users store, search, and analyze large volumes of data in real-time. Elasticsearch is a distributed, scalable system that can be used to index and search structured, semi-structured, and unstructured data.

This task is used to read the data located in Elastic Search engine.

Configuring the Meta Information tab fields

Drag the ES reader task to the Workspace and click on it to open the related configuration tabs for the same. The Meta Information tab opens by default.

• Host IP Address: Enter the host IP Address for Elastic Search.

• Port: Enter the port to connect with Elastic Search.

• Index ID: Enter the Index ID to read a document in elastic search. In Elasticsearch, an index is a collection of documents that share similar characteristics, and each document within an index has a unique identifier known as the index ID. The index ID is a unique string that is automatically generated by Elasticsearch and is used to identify and retrieve a specific document from the index.

• Resource Type: Provide the resource type. In Elasticsearch, a resource type is a way to group related documents together within an index. Resource types are defined at the time of index creation, and they provide a way to logically separate different types of documents that may be stored within the same index.

• Is Date Rich True: Enable this option if any fields in the reading file contain date or time information. The "date rich" feature in Elasticsearch allows for advanced querying and filtering of documents based on date or time ranges, as well as date arithmetic operations.

• Username: Enter the username for elastic search.

• Password: Enter the password for elastic search.

• Query: Provide a spark SQL query.