​

Components are broadly classified into:

• Readers

• Writers

• Transforms

• Consumers

• Producers

• Scripting

• ML

• Scheduler

Component Panel

1. Expand component group and select

2. Search using the search bar in the component panel

3. Drag and Drop the components to the workflow editor.

Any Pipeline System component can be easily dragged to the workflow it includes the following steps to use a System component in the Pipeline Workflow editor:

1. Drag and Drop the components to the workflow editor.

2. Search using the search bar in the component panel

3. Expand component group and select

The MongoDB writer component is designed to write the data in the MongoDB collection.

All component configurations are classified broadly into the following sections:

• ​​Basic Information​​

• Meta Information

• Resource Configuration​​

• ​Connection Validation​

Configuring the Meta Information tab of the MongoDB Writer

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

  • Standard

  • SRV

  • Connection String

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

• Port(*): Port number (It appears only with the Standard Connection Type).

• Username(*): Provide username.

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

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

• Collection Name(*): Provide the name of the collection.

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

• Additional Parameters: Provide the additional parameters to connect with MongoDB. This field is optional.

• Enable SSL: Check this box to enable SSL for this components. MongoDB connection credentials will be different if this option is enabled.

• Certificate Folder: This option will appear when the Enable SSL field is checked-in. The user has to select the certificate folder from drop down which contains the files which has been uploaded to the admin settings for connecting MongoDB with SSL. Please refer the below given images for the reference.

• Save Mode: Select the Save mode from the drop down.

  • Append: This operation adds the data to the collection.

  • Ignore: "Ignore" is an operation that skips the insertion of a record if a duplicate record already exists in the database. This means that the new record will not be added, and the database will remain unchanged. "Ignore" is useful when you want to prevent duplicate entries in a database.

  • Upsert: It is a combination of update and insert options. It is an operation that updates a record if it already exists in the database or inserts a new record if it does not exist. This means that "upsert" updates an existing record with new data or creates a new record if the record does not exist in the database.

This option is available for all the components to validate the connection before deploying the components to avoid connection-related errors. This will also work with environment variables.

A feature to scale your component to the max number of instances to reduce the data processing lag automatically. This feature detects the need to scale up the components in case of higher data traffic.

All components have option of Intelligent scaling which is ability of the system to dynamically adjust the scale or capacity of the reader component based on the current demand and available resources. It involves automatically optimizing the resources allocated to the component to ensure efficient and effective processing of tasks.

S3 Reader component typically authenticate with S3 using AWS credentials, such as an access key ID and secret access key, to gain access to the S3 bucket and its contents. S3 Reader is designed to read and access data stored in an S3 bucket in AWS.

All component configurations are classified broadly into the following sections:

• ​Basic Information​

• Meta Information

• ​Resource Configuration​

• ​Connection Validation

• Navigate to the Data Pipeline Editor.

• Expand the Reader section provided under the Component Pallet.

• Drag and drop the S3 Reader component to the Workflow Editor.

• Click on the dragged S3 Reader to get the component properties tabs.

Basic Information

It is the default tab to open for the component while configuring it.

• Invocation Type: Select an invocation mode out of ‘Real-Time’ or ‘Batch’ using the drop-down menu.

• Deployment Type: It displays the deployment type for the reader component. This field comes pre-selected.

• Container Image Version: It displays the image version for the docker container. This field comes pre-selected.

• Failover Event: Select a failover Event from the drop-down menu.

• Batch Size (min 10): Provide the maximum number of records to be processed in one execution cycle (Min limit for this field is 10).

Meta Information

Open the ‘Meta Information’ tab and fill in all the connection-specific details for the S3 Reader.·

Configuration fields when SNS Monitor is disabled

• Bucket Name (*): Enter AWS S3 Bucket Name.

• Zone (*): Enter S3 Zone. (For eg: us-west-2)

• Access Key (*): Provide Access Key ID shared by AWS.

• Secret Key (*): Provide Secret Access Key shared by AWS.

• Table (*): Mention the Table or file name from S3 location which is to be read.

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

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

• Query: Enter a Spark SQL query. Take inputDf as table name.

Configuration fields when SNS Monitor is enabled

• Access Key (*): Provide Access Key ID shared by AWS.

• Secret Key (*): Provide Secret Access Key shared by AWS.

• Table (*): Mention the Table or file name from S3 location which is to be read.

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

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

• Query: Enter a Spark SQL query. Take inputDf as table name.

Sample Spark SQL query for S3 Reader:

select * from inputDf where Gender = 'Male';

Selected Columns

There is also a section for the selected columns in the Meta Information tab if the user can select some specific columns from the table to read data instead of selecting a complete table so this can be achieved by using the ‘Selected Columns’ section. Select the columns which you want to read and if you want to change the name of the column, then put that name in the alias name section otherwise keep the alias name the same as of column name and then select a Column Type from the drop-down menu.

or

Use ‘Download Data’ and ‘Upload File’ options to select the desired columns.

• Upload File: The user can upload the existing system files (CSV, JSON) using the ‘Upload File’ icon (file size must be less than 2 MB).

• Download Data (Schema): Users can download the schema structure in JSON format by using the ‘Download Data’ icon.

Partition Columns

Provide a unique Key column name on which the partition has been done and has to be read.

Saving the Component Configuration

• Click the Save Component in Storage icon after doing all the configurations to save the reader component.​

• A notification message appears to inform about the component configuration success.

Invocation Type

The Invocation-Type config decides the type of deployment of the component. There are following two types of invocations:

1. Real-Time

2. Batch

Real-Time

When the Component has the real-time invocation, the component never goes down when the pipeline is active. This is for situations where you want to keep the component ready all the time to consume data.

Intelligent Scaling

When "Realtime" is selected as the invocation type, we have an additional option to scale up the component called "Intelligent Scaling."

Please refer to the following page to learn more about Intelligent Scaling.

Batch

When the component has the batch invocation type then the component needs a trigger to initiate the process from the previous event. Once the process of the component is finished and there are no new events to process the component goes down.

These are really helpful in Batch or scheduled operations where the data is not streaming or real-time.

Batch Size

The pipeline components process the data in micro-batches. This batch size is given to define the maximum number of records that you want to process in a single cycle of operation; This is really helpful if you want to control the number of records being processed by the component if the unit record size is huge. You can configure it in the base config of the components.

Failover Events

We can create a failover event and map it in the component base configuration, so that if the component fails it audits all the failure messages with the data (if available) and timestamp of the error.

The DB reader is a spark-based reader which gives you capability to read data from multiple database sources. All the database sources are listed below:

All component configurations are classified broadly into the following sections:

• ​​Basic Information​​

• Meta Information

• ​Resource Configuration​​

• ​Connection Validation​

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

• 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). It supports query containing a join statement as well. Please refer the below image for making query on multiple tables.

• Enable SSL: Check this box to enable SSL for this components. Enable SSL feature in DB reader component will appear only for two(2) drivers: PostgreSQL and ClickHouse.

• Certificate Folder: This option will appear when the Enable SSL field is checked-in. The user has to select the certificate folder from drop down which contains the files which has been uploaded to the admin settings. Please refer the below given images for the reference.

Sample Spark SQL query for DB Reader:

select * from employee where Gender = 'Male';

//In the Spark SQL query above, the selected database contains a table named 'employee'. The query retrieves all records from the 'employee' table where the gender is specified as 'Male'.

• Certificate file (.pem format)]

• Key file (.key format)

SFTP stream reader is designed to read and access data from an SFTP server. SFTP stream readers typically authenticate with the SFTP server using username and password or SSH key-based authentication.

SFTP Stream Reader

All component configurations are classified broadly into the following sections:

• ​Basic Information​​

• Meta Information

• Resource Configuration​​

• ​Connection Validation

Steps to configure the meta information of SFTP Stream Reader Component

• Host: Enter the host.

• Username: Enter username for SFTP stream reader.

• Port: Provide the Port number.

• Add File Name: Enable this option to get the file name along with the data.

• Authentication: Select an authentication option using the drop-down list.

  • Password: Provide a password to authenticate the SFTP Stream reader component

  • PEM/PPK File: Choose a file to authenticate the SFTP Stream reader component. The user needs to upload a file if this authentication option has been selected.

• Reader Path: Enter the path from where the file has to be read.

• Channel: Select a channel option from the drop-down menu (the supported channel is SFTP).

• File type: Select the file type from the drop-down:

  • 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. Schema: If CSV is selected as file type, then paste spark schema of CSV file in this field.

  • 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.

  • 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.

• File Metadata Topic: Enter Kafka Event Name where the reading file metadata has to be sent.

• Column filter: Select the columns which you want to read and if you want to change the name of the column, then put that name in the alias name section otherwise keep the alias name the same as of column name and then select a Column Type from the drop-down menu.

• Use Download Data and Upload File options to select the desired columns.

  • Upload File: The user can upload the existing system files (CSV, JSON) using the Upload File icon.

  • Download Data (Schema): Users can download the schema structure in JSON format by using the Download Data icon.

The Data Pipeline contains an option to configure the resources i.e., Memory & CPU for each component that gets deployed.

There are two types of components-based deployment types:

• Docker

• Spark

Docker

After we save the component and pipeline, The component gets saved with the default configuration of the pipeline i.e. Low, Medium, and High. After the users save the pipeline, we can see the configuration tab in the component. There are multiple things:

There are Request and Limit configurations needed for the Docker components.

The users can see the CPU and Memory options to be configured.

CPU: This is the CPU config where we can specify the number of cores that we need to assign to the component.

Memory: This option is to specify how much memory you want to dedicate to that specific component.

Instances: The number of instances is used for parallel processing. If the users. give N no of instances those many pods will be deployed.

Spark

Spark Component has the option to give the partition factor in the Basic Information tab. This is critical for parallel spark jobs.

Please follow the given example to achieve it:

E.g., If the users need to run 10 parallel spark processes to write the data where the number of inputs Kafka topic partition is 5 then, they will have to set the partition count to 2[i.e., 5*2=10 jobs]. Also, to make it work the number of cores * number of instances should be equal to 10.2 cores * 5

instances =10 jobs.

The configuration of the Spark Components is slightly different from the Docker components. When the spark components are deployed, there are two pods that come up:

• Driver

• Executor

Provide the Driver and Executor configurations separately.

Instances: The number of instances used for parallel processing. If we give N as the number of instances in the Executor configuration N executor pods will get deployed.

​

SFTP Reader is designed to read and access files stored on an SFTP server. SFTP readers typically authenticate with the SFTP server using a username and password or SSH key pair, which grants access to the files stored on the server

All component configurations are classified broadly into the following sections:

• ​

• Meta Information

• ​

• ​

Please go through the below given steps to configure SFTP Reader component:

• Host: Enter the host.

• Username: Enter username for SFTP reader.

• Port: Provide the Port number.

• Dynamic Header: It can automatically detect the header row in a file and adjust the column names and number of columns as necessary.

• Authentication: Select an authentication option using the drop-down list.

  • Password: Provide a password to authenticate the SFTP component

  • PEM/PPK File: Choose a file to authenticate the SFTP component. The user must upload a file if this authentication option is selected.

• Reader Path: Enter the path from where the file has to be read.

• Channel: Select a channel option from the drop-down menu (the supported channel is SFTP).

• Column filter: Select the columns that you want to read and if you change the name of the column, then put that name in the alias name section otherwise keep the alias name the same as the column name and then select a Column Type from the drop-down menu.

• Use the Download Data and Upload File options to select the desired columns.

  • Upload File: The user can upload the existing system files (CSV, JSON) using the Upload File icon.

  • Download Data (Schema): Users can download the schema structure in JSON format by using the Download Data icon.

A MongoDB reader is designed to read and access data stored in a MongoDB database. Mongo readers typically authenticate with MongoDB using a username and password or other authentication mechanisms supported by MongoDB.

All component configurations are classified broadly into the following sections:

• ​

• Meta Information

• ​

• ​​

MongoDB Reader reads the data from the specified collection of Mongo Database. It has an option to filter data using spark SQL query.

• Drag & Drop the MongoDB Reader on the Workflow Editor.

• Click on the dragged reader component to open the component properties tabs below.

Basic Information

It is the default tab to open for the MongoDB reader while configuring the component.

• Select an Invocation type from the drop-down menu to confirm the running mode of the reader component. Select the Real-Time or Batch option from the drop-down menu.

• Deployment Type: It displays the deployment type for the component. This field comes pre-selected.

• Container Image Version: It displays the image version for the docker container. This field comes pre-selected.

• Failover Event: Select a failover Event from the drop-down menu.

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

Configuring the Meta Information tab of the MongoDB Reader

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

  • Standard

  • SRV

  • Connection String

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

• Port(*): Port number (It appears only with the Standard Connection Type).

• Username(*): Provide username.

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

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

• Collection Name(*): Provide the name of the collection.

• Partition Column: specify a unique column name, whose value is a number .

• Query: Enter a Spark SQL query. Take the mongo collection_name as the table name in Spark SQL query.

• Limit: Set a limit for the number of records to be read from MongoDB collection.

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

• Cluster Sharded: Enable this option if data has to be read from sharded clustered database. A sharded cluster in MongoDB is a distributed database architecture that allows for horizontal scaling and partitioning of data across multiple nodes or servers. The data is partitioned into smaller chunks, called shards, and distributed across multiple servers.

• Additional Parameters: Provide the additional parameters to connect with MongoDB. This field is optional.

• Enable SSL: Check this box to enable SSL for this components. MongoDB connection credentials will be different if this option is enabled.

• Certificate Folder: This option will appear when the Enable SSL field is checked-in. The user has to select the certificate folder from drop down which contains the files which has been uploaded to the admin settings for connecting MongoDB with SSL. Please refer the below given images for the reference.

Sample Spark SQL query for MongoDB Reader:

The following images display the various possibilities of the Meta Information for the MongoDB Reader:

i. Meta Information Tab with Standard as Connection Type.

ii. Meta Information Tab with SRV as Connection Type.

iii. Meta Information Tab with Connection String as Connection Type.

• Column Filter: The users can select some specific columns from the table to read data instead of selecting a complete table; this can be achieved via the Column Filter section. Select the columns which you want to read and if you want to change the name of the column, then put that name in the alias name section otherwise keep the alias name the same as of column name and then select a Column Type from the drop-down menu.

or

Use the Download Data and Upload File options to select the desired columns.

1. Upload File: The user can upload the existing system files (CSV, JSON) using the Upload File icon (file size must be less than 2 MB).

2. Download Data (Schema): Users can download the schema structure in JSON format by using the Download Data icon.

• After doing all the configurations click the Save Component in Storage icon provided in the reader configuration panel to save the component.

• A notification message appears to inform about the component configuration success.​

An Elasticsearch reader component is designed to read and access data stored in an Elasticsearch index. Elasticsearch readers typically authenticate with Elasticsearch using username and password credentials, which grant access to the Elasticsearch cluster and its indexes.

Elastic Search Reader

All component configurations are classified broadly into the following sections:

• ​

• Meta Information

• ​

• ​​

Configuring the Meta Information Tab

• 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 Elasticsearch. 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.

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

All component configurations are classified broadly into 3 section

• ​

• Meta Information

Configuring the Meta Information tab of the HDFS Reader

• 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.

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

    • Push Down: In ORC (Optimized Row Columnar) file format, "push down" typically refers to the ability to push down predicate filters to the storage layer for processing. There will be two options in it:

      • True: When push down is set to True, it indicates that predicate filters can be pushed down to the ORC storage layer for filtering rows at the storage level. This can improve query performance by reducing the amount of data that needs to be read into memory for processing.

      • False: When push down is set to False, predicate filters are not pushed down to the ORC storage layer. Instead, filtering is performed after the data has been read into memory by the processing engine. This may result in more data being read and potentially slower query performance compared to when push down is enabled.

  • Path: Provide the path of the file.

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

GCS Reader component is typically designed to read data from Google Cloud Storage (GCS), a cloud-based object storage service provided by Google Cloud Platform. A GCS Reader can be a part of an application or system that needs to access data stored in GCS buckets. It allows you to retrieve, read, and process data from GCS, making it accessible for various use cases, such as data analysis, data processing, backups, and more.

GCS Reader pulls data from the GCS Monitor, so the first step is to implement .

Note: The users can refer to the section of this document for the details.

All component configurations are classified broadly into the following sections:

• Meta Information

• ​

GCS Reader with Docker Deployment

• Navigate to the Pipeline Workflow Editor page for an existing pipeline workflow with GCS Monitor and Event component.

• Open the Reader section of the Component Pallet.

• Drag the GCS Reader to the Workflow Editor.

• Click on the dragged GCS Reader component to get the component properties tabs below.

Basic Information

It is the default tab to open for the component while configuring it.

• Invocation Type: Select an invocation mode from the ‘Real-Time’ or ‘Batch’ using the drop-down menu.

• Deployment Type: It displays the deployment type for the reader component. This field comes pre-selected.

• Container Image Version: It displays the image version for the docker container. This field comes pre-selected.

• Failover Event: Select a failover Event from the drop-down menu.

• Batch Size (min 10): Provide the maximum number of records to be processed in one execution cycle (the minimum limit for this field is 10).

Steps to Configure the Meta Information of GCS Reader (with Docker Deployment Type)

• Bucket Name: Enter the Bucket name for GCS Reader. A bucket is a top-level container for storing objects in GCS.

• Directory Path: Enter the path where the file is located, which needs to be read.

• File Name: Enter the file name.

PySpark GCS Reader

• Navigate to the Pipeline Workflow Editor page for an existing pipeline workflow with the PySpark GCS Reader and Event component.

OR

• You may create a new pipeline with the mentioned components.

• Open the Reader section of the Component Pallet.

• Drag the PySpark GCS Reader to the Workflow Editor.

• Click the dragged GCS Reader component to get the component properties tabs below.

Basic Information

• Invocation Type: Select an invocation mode from the ‘Real-Time’ or ‘Batch’ using the drop-down menu.

• Deployment Type: It displays the deployment type for the reader component. This field comes pre-selected.

• Container Image Version: It displays the image version for the docker container. This field comes pre-selected.

• Failover Event: Select a failover Event from the drop-down menu.

• Batch Size (min 10): Provide the maximum number of records to be processed in one execution cycle (the minimum limit for this field is 10).

Steps to configure the Meta Information of GCS Reader (with Spark Deployment Type)

• Secret File (*): Upload the JSON from the Google Cloud Storage.

• Bucket Name (*): Enter the Bucket name for GCS Reader. A bucket is a top-level container for storing objects in GCS.

• Path: Enter the path where the file is located, which needs to be read.

• Read Directory: Disable reading single files from the directory.

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

• File-Type: Select the File-Type from the drop-down.

  • File Type (*): Supported file formats are:

    • CSV: The Header, Multilibe, and Infer Schema fields will be displayed with CSV as the selected File Type. Enable the Header option to get the Header of the reading file and enable the Infer Schema option to get the true schema of the column in the CSV file. Check the Multiline option if there is any Multiline string in the file.

    • JSON: The Multiline and Charset fields are 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 the XML file. If this option is selected, the following fields will be displayed:

      • Infer schema: Enable this option to get the 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.

• Query: Enter the Spark SQL query.

Select the desired columns using the Download Data and Upload File options.

• Upload File: The user can upload the existing system files (CSV, JSON) using the ‘Upload File’ icon (file size must be less than 2 MB).

• Download Data (Schema): Users can download the schema structure in JSON format using the ‘Download Data’ icon.

Or

The user can also use the Column Filter section to select columns.

Saving the Component Configuration

• Click the Save Component in Storage icon after doing all the configurations to save the reader component.

• A notification message appears to inform about the component configuration success.

A MongoDB reader component is designed to read and access data stored in a MongoDB database. Mongo readers typically authenticate with MongoDB using a username and password or other authentication mechanisms supported by MongoDB.

This page covers the configuration steps for the Mongo DB Reader.All component configurations are classified broadly into the following sections:

• ​​

• Meta Information

• ​

• ​​

MongoDB Reader reads data from the specified database’s Collection. It also has an option to filter the data using Mongo Query Language(MQL), Which will run the MQL directly on the MongoDB Server, and push the data to the out event.

Accessing the Component Properties

• Drag & drop the Mongo Reader component to the Workflow Editor.

• Click on the dragged reader component.

• The component properties tabs open below.

Basic Information

It is the default tab to open for the Mongodb Reader Lite while configuring the component.

• Select an Invocation Type from the drop-down menu to confirm the running mode of the reader component. Select Real-Time or Batch from the drop-down menu.​

• Deployment Type: It displays the deployment type for the component (This field comes pre-selected).

• Container Image Version: It displays the image version for the docker container (This field comes pre-selected).

• Failover Event: Select a failover Event from the drop-down menu.

• Batch Size (min 10): Provide the maximum number of records to be processed in one execution cycle (Min limit for this field is 10).

Meta Information

Open the Meta Information tab and fill in all the connection-specific details of MongoDB Reader Lite. The Meta Information tab opens with the below given fields:

Configuring the over all Meta Information tab fields

• Connection Type: Select either of the options out of Standard, SRV, and Connection String as connection types.

• Port number (*): 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.

• Collection Name (*): Provide the name of the collection.

• Fetch size: Specifies the number of documents to return in each batch of the response from the MongoDB collection. For ex: If 1000 is given in the fetch size field. Then it will read the 1000 data at one execution and it will process it further.

• Additional Parameters: Provide details of the additional parameters.

• Enable SSL: Check this box to enable SSL for this components. MongoDB connection credentials will be different if this option is enabled.

  The user needs to upload the following files on the certificate upload page:

  • Certificate file (.pem format)]

  • Key file (.key format)

• Certificate Folder: This option will appear when the Enable SSL field is checked-in. The user has to select the certificate folder from drop down which contains the files which has been uploaded to the admin settings for connecting MongoDB with SSL. Please refer the below given images for the reference.

• Connection String (*): Provide a connection string (It appears only with the Connection String connection type).

• Query: Provide a relevant query service. We can write the Mongo queries in the following manner:

Variations in the Meta Information tab fields

• Meta Information Tab with Standard as the Connection Type

• Meta Information Tab with SRV as the Connection Type

• Meta Information Tab with Connection String as the Connection Type

Meta Information Tab with enabled the "Enable SSL" field:

Saving the Reader Component

After configuring the required configuration fields, click the Save Component in Storage icon provided in the reader configuration panel to save the component.

• A confirmation message appears to notify the component properties are saved.

Updating and Activating Pipeline

• Click on the Update Pipeline icon to update the pipeline.

• A confirmation message appears to inform the user.

• Click on the Activate Pipeline icon.​

• The Confirm dialog box appears to ask the user permission.

• Click the YES option.

• A confirmation message appears to inform that the pipeline has been activated.

Accessing the Log Panel

• Click on the Toggle Log Panel icon.

• The Log Panel opens displaying the Logs and Advance Logs tabs.

Tabs for a Configured Component

• A configured component will display some more tabs such as the Configuration, Logs, and Pod Logs tabs (as displayed below for the Mongodb Reader Lite component).

Configuration Tab

Logs Tab

Pod Logs Tab

Summary Tab

This tab will show all the description about the component.

The Big Query Reader Component is designed for efficient data access and retrieval from Google Big Query, a robust data warehousing solution on Google Cloud. It enables applications to execute complex SQL queries and process large datasets seamlessly. This component simplifies data retrieval and processing, making it ideal for data analysis, reporting, and ETL workflows.

All component configurations are classified broadly into the following sections:

• Basic Information

• Meta Information

Steps to Configure the Big Query Reader Component

• Navigate to the Data Pipeline Editor.

• Expand the Reader section provided under the Component Pallet.

• Drag and drop the Big Query Reader component to the Workflow Editor.

• Click on the dragged Big Query Reader to get the component properties tabs.

Basic Information

It is the default tab to open for the component while configuring it.

• Invocation Type: Select an invocation mode from the ‘Real-Time’ or ‘Batch’ using the drop-down menu.

• Deployment Type: It displays the deployment type for the reader component. This field comes pre-selected.

• Batch Size (min 1): Provide the maximum number of records to be processed in one execution cycle (Min limit for this field is 1).

• Failover Event: Select a failover Event from the drop-down menu.

• Container Image Version: It displays the image version for the docker container. This field comes pre-selected.

Meta Information

• Open the Meta Information tab and fill in all the connection-specific details for the Big Query Reader.

• Read using: The 'Service Account' option is available under this field, so select it.

• Dataset Id: Mention the Dataset ID from Big Query which is to be read.

• Table Id: Mention the Table ID from Big Query which is to be read.

• Location (*): Mention the location according to your Project.

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

• Query: Enter an SQL Query.

Sample Spark SQL query for Big Query Reader:

Saving the Component Configuration

• A notification message appears to inform about the component configuration success.

ClickHouse reader is designed to read and access data stored in a ClickHouse database. ClickHouse readers typically authenticate with ClickHouse using a username and password or other authentication mechanisms supported by ClickHouse.

Along with the Spark Driver in RDBMS reader we have Docker Reader that supports TCP port All component configurations are classified broadly into the following sections:

All component configurations are classified broadly into the following sections:

• ​​Basic Information​​

• Meta Information

• ​Resource Configuration​​

• ​Connection Validation

Configuring the Meta Information Tab fields

• Host IP Address: Enter the Host IP Address.

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

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

• Password: Enter the password for the provided database.

• 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(,). Settings: Option that allows you to customize various configuration settings for a specific query.

• Enable SSL: Enabling SSL with ClickHouse Reader involves configuring the reader to use the Secure Sockets Layer (SSL) protocol for secure communication between the reader and the ClickHouse server.

• Query: Write SQL query to filter out desired data from ClickHouse Database.

Amazon Athena is an interactive query service that makes it easy to analyze 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 component enables users to read data directly from the external table created in AWS Athena.

Configuring meta information of Athena Query Executer component:

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

• Access Key: Enter the AWS Access Key of the AWS account which has to be used.

• Secret Key: Enter the AWS Secret Key of the AWS account which has to 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 the location where your data resides, typically an S3 bucket.

• Workgroup: Enter the Workgroup name configured in Athena. The Workgroup in Athena is a resource type used 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 queries done in the Athena query editor are saved in CSV format. You can find this path under the "Settings" tab in the Athena query editor in the AWS console, labeled as "Query Result Location".

• Query: Enter the Spark SQL query.

Sample Spark SQL query that can be used in Athena Query Executer:

// Selecting data from employee table:
SELECT * FROM employee LIMIT 10;



// insert data into table_2 by selecting data from table_1 in Athena database:
INSERT INTO table_2
SELECT * FROM table_1;



//Creating a table in Athena database by selecting data from another table in a different database:
CREATE TABLE database_2.table_2
WITH (
    format = 'PARQUET',   --file format(Parquet, AVRO, CSV, JSON etc..)
    external_location = 's3_path' -- Where data will be stored
) AS
SELECT *
FROM database_1.table_1;



//Using CTE queries to get the results:
WITH age_data AS (
    SELECT department,
           CAST(ROUND(AVG(age), 2) AS INT) AS avg_age,
           MIN(age) AS min_age,
           MAX(age) AS max_age
    FROM pipeline.employee_avro1
    GROUP BY department
    ORDER BY department
),
salary_data AS (
    SELECT e.department,
           CAST(ROUND(AVG(e.salary), 2) AS INT) AS average_salary,
           a.avg_age
    FROM pipeline.employee_avro1 e
    JOIN age_data a ON e.department = a.department
    GROUP BY e.department, a.avg_age
),
result_data AS (
    SELECT s.department,
           s.average_salary,
           s.avg_age,
           COUNT(emp_id) AS Total_employees
    FROM pipeline.employee_avro1 e
    JOIN salary_data s ON e.department = s.department
    GROUP BY s.department, s.average_salary, s.avg_age
)
SELECT *
FROM result_data;

Azure Metadata Reader is designed to read and access metadata associated with Azure resources. Azure Metadata Readers typically authenticate with Azure using Azure Active Directory credentials or other authentication mechanisms supported by Azure.

All component configurations are classified broadly into the following sections:

• ​​Basic Information​​

• Meta Information

• Resource Configuration​​

• ​Connection Validation

Configuring the Meta Information of Azure Blob Reader

Read Using: There are three authentication methods available to connect with Azure in the Azure Blob Reader Component:

• Shared Access Signature

• Secret Key

• 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. Path type: There are options available under it:

   • Null: If Null is selected as the Path Type, the component will read the metadata of all the blobs from the given container. The user does not need to fill the Blob Name field in this option.

   • Directory Path: Enter the directory path to read the metadata of files located in the specified directory. For example: employee/joining_year=2010/department=BI/designation=Analyst/.

   • Blob Name: Specify the blob name to read the metadata from that particular blob.

Read using Secret Key

Provide the following details:

1. Account Key: It is be used to authorize access to data in your storage account via Shared Key authorization.

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. Path type: There are options available under it:

   • Null: If Null is selected as the Path Type, the component will read the metadata of all the blobs from the given container. The user does not need to fill the Blob Name field in this option.

   • Directory Path: Enter the directory to read the metadata of files located in the specified directory. For example: employee/joining_year=2010/department=BI/designation=Analyst/.

   • Blob Name: Specify the blob name to read the metadata from that particular blob.

Read using Principal Secret

Provide the following details:

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

2. Tenant ID: It is a globally unique identifier (GUID) that is different than your organization name or domain.

3. Client Secret: The client secret is the password of the service principal.

4. Account Name: Provide the Azure account name.

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

6. Path type: There are options available under it:

   • Null: If Null is selected as the Path Type, the component will read the metadata of all the blobs from the given container. The user does not need to fill the Blob Name field in this option.

   • Directory Path: Enter the directory to read the metadata of files located in the specified directory. For example: employee/joining_year=2010/department=BI/designation=Analyst/.

   • Blob Name: Specify the blob name to read the metadata from that particular blob.

Output Metadata

Once the component runs successfully, it will send the following metadata to the output event:

• Container: Name of the container where the blob is present.

• Blob: Name of the blob present in the specified path.

• blobLastModifiedDateAndTime: Date and time when the blob was last modified.

• blobLength: Size of the blob.

All component configurations are classified broadly into the following sections:

• ​​Basic Information​​

• Meta Information

• ​Resource Configuration​​

• ​Connection Validation

Before using the Sandbox Reader component for reading a file, the user needs to upload a file in Data Sandbox under the Data Center module.

Steps to configure Sandbox Reader

• Navigate to the Data Pipeline Editor.

• Expand the Readers section provided under the Component Pallet.

• Drag and drop the Sandbox Reader component to the Workflow Editor.

• Click on the dragged Sandbox Reader to get the component properties tabs.

Basic Information

It is the default tab to open for the component while configuring it.

• Invocation Type: Select an invocation mode from the Real-Time or Batch options by using the drop-down menu.

• Deployment Type: It displays the deployment type for the reader component. This field comes pre-selected.

• Container Image Version: It displays the image version for the docker container. This field comes pre-selected.

• Failover Event: Select a failover Event from the drop-down menu.

• Batch Size (min 10): Provide the maximum number of records to be processed in one execution cycle (Minimum limit for this field is 10).

Configuring meta information of Sandbox Reader component:

• Storage Type: The user will find two options here:

  • Network: This option will be selected by default. In this option, the following fields will be displayed:

    • File Type: Select the type of the file to be read. Supported file types include CSV, JSON, PARQUET, AVRO, XML, and ORC.

    • Schema: Enter the Spark schema of the file in JSON format.

    • Sandbox Folder Path: Enter the Sandbox folder name where the data is stored in part files.

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

  • Platform: In this option, the following fields will be displayed:

    • File Type: Select the type of the file to be read. The supported file types are CSV, JSON, PARQUET, AVRO, XML, and ORC.

    • Sandbox Name: This field will display once the user selects the file type. It will show all the Sandbox names for the selected file type, and the user has to select the Sandbox name from the drop-down.

    • Sandbox File: This field displays the name of the sandbox file to be read. It will automatically fill when the user selects the sandbox name.

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

• Query: Enter a spark SQL query. Take inputDf as a table name.

• Column Filter: There is also a section for the selected columns in the Meta Information tab if the user can select some specific columns from the table to read data instead of selecting a complete table so this can be achieved by using the Column Filter section. Select the columns that you want to read and if you want to change the name of the column, then put that name in the alias name section otherwise keep the alias name the same as of column name and then select a Column Type from the drop-down menu.

• Use the Download Data and Upload File options to select the desired columns.

  • Upload File: The user can upload the existing system files (CSV, JSON) using the Upload File icon (file size must be less than 2 MB).

  • Download Data (Schema): Users can download the schema structure in JSON format by using the Download Data icon.

or

Use the Download Data and Upload File options to select the desired columns.

• Partition Columns: To read a specific partition, enter the name of the partitioned column.

Sample Query for Sandbox Reader:

select team as Team, Avg(monthly_salary) as Average_Salary,
Avg(experience) as Average_Experience from inputDf group by team;

Fields in the Meta Information tab may vary based on the selected File Type. All the possibilities are mentioned below:

• CSV: The following fields will display when CSV is selected as File Type:

  • Header: Enable the Header option to retrieve the header of the reading file.

  • Infer Schema: Enable the Infer Schema option to obtain the true schema of the columns in the CSV file.

  • Multiline: Enable the Multiline option to read multiline strings in the data.

  • Schema: This field will be visible only when the Header option is enabled. Enter the Spark schema in JSON format in the schema field to filter out the bad records. To filter the bad records, the user needs to map the failover Kafka event in the Failover Event field in the Basic Information tab.

• JSON: The Multiline and Charset fields are displayed with JSON as the selected File Type. Check in the Multiline option to see, 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 the XML file. If this option is selected, the following fields will be displayed:

  • Infer schema: Enable this option to get the 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.

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

  • Push Down: In ORC (Optimized Row Columnar) file format, "push down" typically refers to the ability to push down predicate filters to the storage layer for processing. There will be two options in it:

    • True: When push-down is set to True, it indicates predicate filters can be pushed down to the ORC storage layer for filtering rows at the storage level. This can improve query performance by reducing the amount of data that needs to be read into memory for processing.

    • False: When push down is set to False, predicate filters are not pushed down to the ORC storage layer. Instead, filtering is performed after the data has been read into memory by the processing engine. This may result in more data being read and potentially slower query performance compared to when push-down is enabled.

Azure Blob Reader is designed to read and access data stored in Azure Blob Storage. Azure Blob Readers typically authenticate with Azure Blob Storage using Azure Active Directory credentials or other authentication mechanisms supported by Azure.

This is a docker based component.

All component configurations are classified broadly into the following sections:

• ​​Basic Information​​

• Meta Information

• ​Resource Configuration​​

• ​Connection Validation

Configuring the Meta Information of Azure Blob Reader

Read Using: There are three authentication methods available to connect with Azure in the Azure Blob Reader Component:

• Shared Access Signature

• Secret Key

• Principal Secret

Read using Shared Access Signature

Provide the following details:

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

• Account Name: Provide the Azure account name.

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

• File Type: There are five (5) types of file extensions available:

  • CSV

  • JSON

  • PARQUET

  • AVRO

  • XML

• Read Directory: This field will be checked by default. If this option is enabled, the component will read data from all the blobs present in the container.

• Blob Name: This field will display only if the Read Directory field is disabled. Enter the specific name of the blob whose data has to be read.

• Column Filter: Enter the column names here. Only the specified columns will be fetched from Azure Blob. In this field, the user needs to fill in the following information:

  • Source Field: Enter the name of the column from the blob. The user can add multiple columns by clicking on the "Add New Column" option.

  • Destination Field: Enter the alias name for the source field.

  • Column Type: Enter the data type of the column.

  • Upload: This option allows the user to upload a data file in CSV, JSON, or EXCEL format. The column names will be automatically fetched from the uploaded data file and filled out in the Name, Alias, and Column Type fields.

  • Download Data: This option will download the data filled in the Column Filter field in JSON format.

  • Delete Data: This option will clear all the information filled in the Column Filter field.

Read Using Secret Key Option

Provide the following details:

• Account Key: Used to authorize access to data in your storage account via Shared Key authorization.

• Account Name: Provide the Azure account name.

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

• File Type: There are five (5) types of file extensions available:

  • CSV

  • JSON

  • PARQUET

  • AVRO

• Read Directory: This field will be checked by default. If this option is enabled, the component will read data from all the blobs present in the container.

• Blob Name: This field will display only if the Read Directory field is disabled. Enter the specific name of the blob whose data has to be read.

• Column Filter: Enter the column names here. Only the specified columns will be fetched from Azure Blob. In this field, the user needs to fill in the following information:

  • Source Field: Enter the name of the column from the blob. The user can add multiple columns by clicking on the "Add New Column" option.

  • Destination Field: Enter the alias name for the source field.

  • Column Type: Enter the data type of the column.

  • Upload: This option allows the user to upload a data file in CSV, JSON, or EXCEL format. The column names will be automatically fetched from the uploaded data file and filled out in the Name, Alias, and Column Type fields.

  • Download Data: This option will download the data filled in the Column Filter field in JSON format.

  • Delete Data: This option will clear all the information filled in the Column Filter field.

Read Using Principal Secret

Provide the following details:

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

• Tenant ID: A globally unique identifier (GUID) that is different from your organization name or domain.

• Client Secret: The password of the service principal.

• Account Name: Provide the Azure account name.

• File Type: There are five (5) types of file extensions available:

  • CSV

  • JSON

  • PARQUET

  • AVRO

• Read Directory: This field will be checked by default. If this option is enabled, the component will read data from all the blobs present in the container.

• Blob Name: This field will display only if the Read Directory field is disabled. Enter the specific name of the blob whose data has to be read.

• Column Filter: Enter the column names here. Only the specified columns will be fetched from Azure Blob. In this field, the user needs to fill in the following information:

  • Source Field: Enter the name of the column from the blob. The user can add multiple columns by clicking on the "Add New Column" option.

  • Destination Field: Enter the alias name for the source field.

  • Column Type: Enter the data type of the column.

  • Upload: This option allows the user to upload a data file in CSV, JSON, or EXCEL format. The column names will be automatically fetched from the uploaded data file and filled out in the Name, Alias, and Column Type fields.

  • Download Data: This option will download the data filled in the Column Filter field in JSON format.

  • Delete Data: This option will clear all the information filled in the Column Filter field.

Azure Blob Reader is designed to read and access data stored in Azure Blob Storage. Azure Blob Readers typically authenticate with Azure Blob Storage using Azure Active Directory credentials or other authentication mechanisms supported by Azure. This is a spark based component.

All component configurations are classified broadly into the following sections:

• ​​Basic Information​​

• Meta Information

• ​Resource Configuration​​

• ​Connection Validation

Configuring the Meta Information of Azure Blob Reader

Read Using: There are three authentication methods available to connect with Azure in the Azure Blob Reader Component:

• Shared Access Signature

• Secret Key

• Principal Secret

Read using Shared Access Signature

Provide the following details:

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

• Account Name: Provide the Azure account name.

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

• File Type: There are five (5) types of file extensions available:

  • CSV

  • JSON

  • PARQUET

  • AVRO

  • XML

• Read Directory: This field will be checked by default. If this option is enabled, the component will read data from all the blobs present in the container.

• Blob Name: This field will display only if the Read Directory field is disabled. Enter the specific name of the blob whose data has to be read.

• Limit: Enter a number to limit the number of records that has to be read by the component.

• Column Filter: Enter the column names here. Only the specified columns will be fetched from Azure Blob. In this field, the user needs to fill in the following information:

  • Source Field: Enter the name of the column from the blob. The user can add multiple columns by clicking on the "Add New Column" option.

  • Destination Field: Enter the alias name for the source field.

  • Column Type: Enter the data type of the column.

  • Upload: This option allows the user to upload a data file in CSV, JSON, or EXCEL format. The column names will be automatically fetched from the uploaded data file and filled out in the Name, Alias, and Column Type fields.

  • Download Data: This option will download the data filled in the Column Filter field in JSON format.

  • Delete Data: This option will clear all the information filled in the Column Filter field.

• Query: Enter a Spark SQL query in this field. Use inputDf as the table name.

//Sample Spark SQL Query:

Select * from inputDf where department='sales';

Read Using Secret Key Option

Provide the following details:

• Account Key: Used to authorize access to data in your storage account via Shared Key authorization.

• Account Name: Provide the Azure account name.

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

• File Type: There are five (5) types of file extensions available:

  • CSV

  • JSON

  • PARQUET

  • AVRO

  • XML

• Read Directory: This field will be checked by default. If this option is enabled, the component will read data from all the blobs present in the container.

• Blob Name: This field will display only if the Read Directory field is disabled. Enter the specific name of the blob whose data has to be read.

• Limit: Enter a number to limit the number of records that has to be read by the component.

• Column Filter: Enter the column names here. Only the specified columns will be fetched from Azure Blob. In this field, the user needs to fill in the following information:

  • Source Field: Enter the name of the column from the blob. The user can add multiple columns by clicking on the "Add New Column" option.

  • Destination Field: Enter the alias name for the source field.

  • Column Type: Enter the data type of the column.

  • Upload: This option allows the user to upload a data file in CSV, JSON, or EXCEL format. The column names will be automatically fetched from the uploaded data file and filled out in the Name, Alias, and Column Type fields.

  • Download Data: This option will download the data filled in the Column Filter field in JSON format.

  • Delete Data: This option will clear all the information filled in the Column Filter field.

• Query: Enter a Spark SQL query in this field. Use inputDf as the table name.

Read Using Principal Secret

Provide the following details:

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

• Tenant ID: A globally unique identifier (GUID) that is different from your organization name or domain.

• Client Secret: The password of the service principal.

• Account Name: Provide the Azure account name.

• File Type: There are five (5) types of file extensions available:

  • CSV

  • JSON

  • PARQUET

  • AVRO

  • XML

• Read Directory: This field will be checked by default. If this option is enabled, the component will read data from all the blobs present in the container.

• Blob Name: This field will display only if the Read Directory field is disabled. Enter the specific name of the blob whose data has to be read.

• Limit: Enter a number to limit the number of records that has to be read by the component.

• Column Filter: Enter the column names here. Only the specified columns will be fetched from Azure Blob. In this field, the user needs to fill in the following information:

  • Source Field: Enter the name of the column from the blob. The user can add multiple columns by clicking on the "Add New Column" option.

  • Destination Field: Enter the alias name for the source field.

  • Column Type: Enter the data type of the column.

  • Upload: This option allows the user to upload a data file in CSV, JSON, or EXCEL format. The column names will be automatically fetched from the uploaded data file and filled out in the Name, Alias, and Column Type fields.

  • Download Data: This option will download the data filled in the Column Filter field in JSON format.

  • Delete Data: This option will clear all the information filled in the Column Filter field.

• Query: Enter a Spark SQL query in this field. Use inputDf as the table name.

An S3 Writer is designed to write data to an S3 bucket in AWS. S3 Writer typically authenticate with S3 using AWS credentials, such as an access key ID and secret access key, to gain access to the S3 bucket and its contents.

All component configurations are classified broadly into the following sections:

• ​​

• Meta Information

• ​

• ​​

Configuring the Meta Information tab of the S3 Writer

• Bucket Name: Enter the S3 Bucket name.

• 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 where the data has to be written in the S3 location.

• Region: Provide the S3 region where the Bucket is created.

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

• Save Mode: Select the save mode from the drop-down menu:

  • Append: It will append the data in the blob.

  • Overwrite: It will overwrite the data in the blob.

• Schema File Name: Upload a Spark schema file of the data which has to be written in JSON format.

• Column Filter: Enter the column names here. Only the specified columns will be fetched from the data from the previous connected event to the S3 Writer. In this field, the user needs to fill in the following information:

  • Name: Enter the name of the column which has to be written from the previous event. The user can add multiple columns by clicking on the "Add New Column" option.

  • Alias: Enter the alias name for the selected column name.

  • Column Type: Enter the data type of the column.

• Upload: This option allows the user to upload a data file in CSV, JSON, or EXCEL format. The column names will be automatically fetched from the uploaded data file and filled out in the Name, Alias, and Column Type fields.

• Download Data: This option will download the data filled in the Column Filter field in JSON format.

• Delete Data: This option will clear all the information filled in the Column Filter field.

• Partition Columns: This feature enables users to partition the data when writing to Azure Blob. Users can specify multiple columns for partitioning by clicking the "Add Column Name" option. For example, If data is partitioned by a date column, a separate folder will be created for each unique date in an Amazon S3 bucket. The data storage might look like this:

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 component writes the data in HDFS(Hadoop Distributed File System).

All component configurations are classified broadly into 3 sections

• ​

• Meta Information

• ​​

Configuring the Meta Information tab of the HDFS Writer

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

• Port: Enter the Port.

• Table: Enter the table name where the data has to be written.

• Zone: Enter the Zone for HDFS in which the data has to be written. Zone is a special directory whose contents will be transparently encrypted upon write and transparently decrypted upon read.

• File Format: Select a file format in which the data has to be written.

  • CSV

  • JSON

  • PARQUET

  • AVRO

  • ORC

• Save Mode: Select a Save Mode.

• Schema file name: Upload Spark schema file in JSON format.

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

An Elasticsearch writer component is designed to write the data stored in an Elasticsearch index. Elasticsearch writers typically authenticate with Elasticsearch using username and password credentials, which grant access to the Elasticsearch cluster and its indexes.

All component configurations are classified broadly into the following sections:

• ​​

• Meta Information

• ​​

• ​​

Please follow the below mentioned steps to configure the meta information of ES writer:

• 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.

• Mapping ID: Provide the Mapping ID. In Elasticsearch, a mapping ID is a unique identifier for a mapping definition that defines the schema of the documents in an index. It is used to differentiate between different types of data within an index and to control how Elasticsearch indexes and searches data.

• 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.

• Username: Enter the username for elastic search.

• Password: Enter the password for elastic search.

• Schema File Name: Upload spark schema file in JSON format.

• Save Mode: Select the Save mode from the drop down.

  • Append

• Selected columns: The user can select the specific column, provide some alias name and select the desired data type of that column.

The DB writer is a spark-based writer component which gives you capability to write data to multiple database sources.

All component configurations are classified broadly into the following sections:

• ​​

• Meta Information

• ​​

• ​​

Drivers Available

Configuring the Meta Information of Azure Writer

• Database name: Enter the Database name.

• Table name: Provide a table name where the data has to be written.

• Enable SSL: Check this box to enable SSL for this components. Enable SSL feature in DB reader component will appear only for three(3) drivers: MongoDB, PostgreSQL and ClickHouse.

• Certificate Folder: This option will appear when the Enable SSL field is checked-in. The user has to select the certificate folder from drop down which contains the files which has been uploaded to the admin settings. Please refer the below given images for the reference.

• Schema File Name: Upload a Spark schema file of the data which has to be written in JSON format.

• Save Mode: Select the save mode from the drop-down menu:

  • Append: It will append the data in the table.

  • Overwrite: It will overwrite the data in the table.

  • Upsert: This operation allows the users to insert a new record or update existing data into a table. For configuring this, we need to provide the Composite Key.

    • Sort Column: This field will appear only when Upsert is selected as Save mode. If there are multiple records with the same composite key but different values in the batch, the system identifies the record with the latest value based on the Sort column. The Sort column defines the ordering of records, and the record with the highest value in the sort column is considered the latest.

  • Column Filter: Enter the column names here. Only the specified columns will be fetched from the data from the previous connected event to the DB Writer. In this field, the user needs to fill in the following information:

    • Name: Enter the name of the column which has to be written from the previous event. The user can add multiple columns by clicking on the "Add New Column" option.

    • Alias: Enter the alias name for the selected column name.

    • Column Type: Enter the data type of the column.

    • Upload: This option allows the user to upload a data file in CSV, JSON, or EXCEL format. The column names will be automatically fetched from the uploaded data file and filled out in the Name, Alias, and Column Type fields.

    • Download Data: This option will download the data filled in the Column Filter field in JSON format.

    • Delete Data: This option will clear all the information filled in the Column Filter field.

  • Query: In this field, we can write a DDL for creating the table in database where the in-event data has to be written. For example, please refer the below image:

All component configurations are classified broadly into the following sections:

• ​​Basic Information​​

• Meta Information

• Resource Configuration​​

• ​Connection Validation

Configuring the Meta Information Tab

Please follow the below mentioned steps to configure the Meta Information Tab of Sandbox Writer:

• Storage Type: The user will find two options here:

  • Network: This option will be selected by default. In this mode, a folder corresponding to the Sandbox file name provided by the user will be created at the Sandbox location. Data will be written into part files within this folder, with each part file containing data based on the specified batch size.

  • Platform: If the user selects the "Platform" option, a single file containing the entire dataset will be created at the Sandbox location, using the Sandbox file name provided by the user.

• Sandbox File: Enter the file name.

• File Type: Select the file type in which the data has to be written. There are 4 files types supported here:

  • CSV

  • JSON

  • Text

  • ORC

• Save Mode: Select the save mode from the drop-down menu:

  • Append: It will append the data in the blob.

  • Overwrite: It will overwrite the data in the blob.

• Schema File Name: Upload a Spark schema file of the data which has to be written in JSON format.

• Column Filter: Enter the column names here. Only the specified columns will be fetched from the data from the previous connected event to the Sandbox Writer. In this field, the user needs to fill in the following information:

  • Name: Enter the name of the column which has to be written from the previous event. The user can add multiple columns by clicking on the "Add New Column" option.

  • Alias: Enter the alias name for the selected column name. The column name given here will be written in the Sandbox file.

  • Column Type: Enter the data type of the column.

  • Upload: This option allows the user to upload a data file in CSV, JSON, or EXCEL format. The column names will be automatically fetched from the uploaded data file and filled out in the Name, Alias, and Column Type fields.

  • Download Data: This option will download the data filled in the Column Filter field in JSON format.

  • Delete Data: This option will clear all the information filled in the Column Filter field.

ClickHouse writer component is designed to write or store data in a ClickHouse database. ClickHouse writers typically authenticate with ClickHouse using a username and password or other authentication mechanisms supported by ClickHouse.

All component configurations are classified broadly into the following sections:

• ​​Basic Information​​

• Meta Information

• ​Resource Configuration​​

• ​Connection Validation

Configuring the Meta information tab of ClickHouse writer

• Host IP Address: Enter the Host IP Address.

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

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

• Password: Enter the password for the provided database.

• 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(,). Settings: Option that allows you to customize various configuration settings for a specific query.

• Enable SSL: Enabling SSL with ClickHouse writer involves configuring the writer to use the Secure Sockets Layer (SSL) protocol for secure communication between the writer and the ClickHouse server.

• Save Mode: Select the Save mode from the drop down.

• Column Filter: There is also a section for the selected columns in the Meta Information tab if the user can select some specific columns from the table to read data instead of selecting a complete table so this can be achieved by using the Column Filter section. Select the columns which you want to read and if you want to change the name of the column, then put that name in the alias name section otherwise keep the alias name the same as of column name and then select a Column Type from the drop-down menu.

  • Use Download Data and Upload File options to select the desired columns.

    • Upload File: The user can upload the existing system files (CSV, JSON) using the Upload File icon (file size must be less than 2 MB).

    • Download Data: Users can download the schema structure in JSON format by using the Download Data icon.