Creating Jobs in Data Warehousing

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DataStage offers the following types of jobs:

Building a DataStage Job

  1. Define optional project-level environment variables in DataStage Administrator.
  2. Define optional environment parameters.
  3. Import or create table definitions, if they are not already available.
  4. Add stages and links to the job to represent data flow.
  5. Edit source and target stages to determine data sources, table definitions, file names, and so on.
  6. Modify transformer and processing stages to perform various tasks such as filters, lookups, and expressions.
  7. Save, compile, troubleshoot, and run the job.

Creating Jobs in Data Warehousing

Data warehousing is a crucial aspect of any data-driven organization. One of the essential tasks in data warehousing is creating jobs that define how data is processed and transformed. In this article, we'll discuss creating jobs using popular ETL (Extract, Transform, Load) tools.

Creating Jobs with Apache NiFi

Apache NiFi is an open-source tool for data integration and processing. Here's how to create a simple job:

```sql - Connect the following processors (in this order): File, ConvertRecord, ExecuteSQL, PutSQL - Configure each processor as follows: - File: Set the input file path and any necessary parameters like delimiter or enclosure. - ConvertRecord: Map attributes based on input record structure. - ExecuteSQL: Write a SQL query to transform data. - PutSQL: Specify the target database connection details and table name. ```

Creating Jobs with Talend

Talend is another widely-used ETL tool. Here's how to create a job:

```sql - Drag and drop the necessary components from the palette (tFileInputDelimited, tMap, tDBOutput, etc.) onto the job design canvas. - Connect each component using data lines or tConnect. - Configure each component with appropriate parameters like input file path, output table name, and transformation rules. ```

Creating Jobs with Apache Airflow

Apache Airflow is a platform for managing workflows and scheduling ETL jobs. Here's how to create a DAG:

```python from airflow import DAG from airflow.operators.bash_operator import BashOperator from datetime import datetime, timedelta def my_function(): # Your custom function for transforming data goes here. pass default_args = { 'owner': 'airflow', 'start_date': datetime(2021, 1, 1), } dag = DAG('my_dag', default_args=default_args, schedule_interval=timedelta(days=1)) t1 = BashOperator(task_id='task1', bash_command='echo "Hello World"', dag=dag) t2 = MyCustomOperator(task_id='my_custom_task', my_function, dag=dag) t3 = BashOperator(task_id='task3', bash_command='echo "End of the pipeline"', dag=dag) t1 >> t2 >> t3 ```

Summary

In this article, we explored creating jobs in data warehousing using popular ETL tools like Apache NiFi, Talend, and Apache Airflow. By understanding the process of creating jobs, you can streamline your data processing tasks and ensure efficient data transformation within your organization's data warehousing environment.