July 30, 2023

tDeltaLakeOutput – Docs for ESB 7.x

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tDeltaLakeOutput

Writes records in the Delta Lake layer of your Data Lake system in the Parquet format.

Delta Lake is an open source storage layer that brings ACID (Atomicity,
Consistency, Isolation, Durability) transactions, scalable metadata handling, and
unifies streaming and batch data processing to Data Lakes. To put the concept visual,
data stored in Delta Lake takes the shape of versioned Parquet files with their
transaction logs.

For further information, see the Delta Lake documentation on https://docs.delta.io/latest/index.html.

tDeltaLakeOutput properties for Apache Spark Batch

These properties are used to configure tDeltaLakeOutput running in the Spark Batch Job framework.

The Spark Batch
tDeltaLakeOutput component belongs to the Technical family.

The component in this framework is available in all subscription-based Talend products with Big Data
and Talend Data Fabric.

Basic settings

Define a storage configuration component

Select the configuration component to be used to provide the configuration
information for the connection to the target file system such as HDFS.

If you leave this check box clear, the target file system is the local
system.

The configuration component to be used must be present in the same Job.
For example, if you have dropped a tHDFSConfiguration component in the Job, you can select it to write
the result in a given HDFS system.

Property type

Either Built-In or Repository.

 

Built-In: No property data stored centrally.
 

Repository: Select the repository file where the
properties are stored.

The properties are stored centrally under the Hadoop
Cluster node of the Repository
tree.

The fields that come after are pre-filled in using the fetched data.

For further information about the Hadoop
Cluster node, see the Getting Started Guide.

Schema and Edit Schema

A schema is a row description. It defines the number of fields
(columns) to be processed and passed on to the next component. When you create a Spark
Job, avoid the reserved word line when naming the
fields.

Click Edit
schema to make changes to the schema. If the current schema is of the Repository type, three options are available:

  • View schema: choose this
    option to view the schema only.

  • Change to built-in property:
    choose this option to change the schema to Built-in for local changes.

  • Update repository connection:
    choose this option to change the schema stored in the repository and decide whether
    to propagate the changes to all the Jobs upon completion. If you just want to
    propagate the changes to the current Job, you can select No upon completion and choose this schema metadata
    again in the Repository Content
    window.

Spark automatically infers
data types for the columns in a PARQUET schema. In a Talend Job for Apache Spark, the Date type is inferred
and stored as int96.
 

Built-In: You create and store the schema locally for this component
only.
 

Repository: You have already created the schema and stored it in the
Repository. You can reuse it in various projects and Job designs.

Folder/File

Browse to, or enter the path pointing to the data to be used in the file system.

The button for browsing does not work with the Spark
Local mode; if you are
using the other Spark Yarn
modes that the Studio supports with your distribution, ensure that you have properly
configured the connection in a configuration component in the same Job, such as

tHDFSConfiguration
. Use the
configuration component depending on the filesystem to be used.

Action

Select an operation for writing data to the filesystem to which the
configuration component in your Job provides the connection information:

  • Create:
    Creates the directory specified in the Folder/File field and write data in it.
  • Overwrite:
    Overwrites directory specified in the Folder/File field using the incoming data.
  • Append: Adds
    incoming records to the existing in the directory specified in the
    Folder/File field.

Delta Lake systematically creates slight differences between the upload time of a file and the metadata timestamp of this file. Bear in mind these differences when you need to filter data.

Advanced settings

Define column partitions Select this check box and complete the table that is displayed using columns from the schema of the incoming data. The records of the selected columns are used as keys to partition your data.
Sort columns alphabetically Select this check box to sort the schema columns in the alphabetical order. If you leave this check box clear, these columns stick to the order defined in the schema editor.
Use Timestamp format for Date type

Select the check box to output dates, hours, minutes and seconds contained in your
Date-type data. If you clear this check box, only years, months and days are
outputted.

The format used by Deltalake is yyyy-MM-dd HH:mm:ss.
Merge Schema The schema of your datasets often evolves through time. Select this
check box to merge the schemas of the incoming data and the existing data
when their schemas are different.

If you leave this check
box and the Overwrite Schema check box clear,
only the columns of the existing data are used.
Overwrite Schema

The schema of your datasets often evolves through time. Select this check
box to use the schemas of the incoming data to overwrite the schemas of
the existing data.

If you leave this check box and the Merge
Schema check box clear, only the columns of the existing
data are used.

Usage

Usage rule

This component is used as an end component and requires an input link.

Delta Lake systematically creates slight differences between the upload time of a file and
the metadata timestamp of this file. Bear in mind these differences when
you need to filter data.

Spark Connection

In the Spark
Configuration tab in the Run
view, define the connection to a given Spark cluster for the whole Job. In
addition, since the Job expects its dependent jar files for execution, you must
specify the directory in the file system to which these jar files are
transferred so that Spark can access these files:

  • Yarn mode (Yarn client or Yarn cluster):

    • When using Google Dataproc, specify a bucket in the
      Google Storage staging bucket
      field in the Spark configuration
      tab.

    • When using HDInsight, specify the blob to be used for Job
      deployment in the Windows Azure Storage
      configuration       area in the Spark
      configuration       tab.

    • When using Altus, specify the S3 bucket or the Azure
      Data Lake Storage for Job deployment in the Spark
      configuration       tab.
    • When using Qubole, add a
      tS3Configuration to your Job to write
      your actual business data in the S3 system with Qubole. Without
      tS3Configuration, this business data is
      written in the Qubole HDFS system and destroyed once you shut
      down your cluster.

    • When using on-premise
      distributions, use the configuration component corresponding
      to the file system your cluster is using. Typically, this
      system is HDFS and so use tHDFSConfiguration.

  • Standalone mode: use the
    configuration component corresponding to the file system your cluster is
    using, such as tHDFSConfiguration or
    tS3Configuration.

    If you are using Databricks without any configuration component present
    in your Job, your business data is written directly in DBFS (Databricks
    Filesystem).

This connection is effective on a per-Job basis.

Document get from Talend https://help.talend.com
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Docs 7.x
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