ETL Scheduling and Automation - Top Tools

CEO, Portable

Bigger datasets do not mean better. Businesses who know how to use their data have a significant competitive advantage in driving business outcomes and pleasing customers.

Streamlining the data management processes is essential for any data-driven company, because data automation ensures data accuracy and reliability, saves time and resources, and improves overall data-driven decision-making. 

For companies to ensure that they are making the most effective and efficient use of data, they should schedule and automate their ETL processes. 

The article describes ETL scheduling and explains why automation is helpful. The article provides an example of automation and reviews some of the top ETL scheduling and automation tools that simplify this complex process.

What is ETL Scheduling?

ETL (extract, transform, load) refers to extracting data from relevant sources, transforming it to a desired format, and then loading it to a statistical or analytical tool for further analysis. Scheduling refers to creating an event that takes place at a particular time. This could be as simple as a daily dashboard refresh that loads new data.

ETL scheduling means automating and scheduling the tasks involved in extracting data from various sources, transforming it into a format suitable for analysis, and loading it into a data storage system. 

Why is ETL Scheduling Important?

ETL scheduling is crucial because it allows you to:

  1. Set up a schedule or a timeline for ETL jobs, such as when the data integration process should occur

  2. Set up a frequency for the ETL process

  3. Set up alerts or conditions that inform what should happen if there are any issues or errors along the way.

For instance, a SaaS company wants to set KPIs crucial for analyzing its state. The data required for analysis could come from multiple sources (e.g., CRM, SQL server, or PostgreSQL that stores data, marketing and sales channels, etc.). Therefore, the first step is to combine all the data into a single source. Once combined, analysts can track the defined metrics. 

Imagine writing the same algorithms for data processing daily. The process is time-consuming, less efficient, and resource intensive, with a high dependency on a technical resource.

Therefore, it is crucial to automate this process to ensure efficient and timely reporting. The company could set up an ETL schedule or cronjob to run every night at midnight. The ETL process would extract data from various sources, transform it into a consistent format, and load it into a data warehousing service. By scheduling the ETL process to run regularly, the company can ensure that the data is always up-to-date and available for analysis.

What is the role of a job scheduler?

A job scheduler is a service or a tool that schedules and automates the execution of different processes.

An example of a job scheduler is Task Scheduler in Microsoft Windows. With task scheduler, you can automate the execution of different processes.

Business intelligence tools like Power BI and Tableau, and cloud platforms like AWS and Azure, come with built-in job schedulers for automation.

A job scheduler helps organizations and individuals save time and reduce mistakes by making workflows more efficient and automating repetitive tasks. Some of the key roles of a job scheduler include:

  • Defining Jobs: With a job scheduler, a user can define the tasks that need to be executed, along with various inputs and outputs.

  • Scheduling Jobs: Scheduling allows the user to specify when a job should run. The specifics can vary depending on the type of job. Some jobs are scheduled to respond to specific triggers or events, while others run at a particular time.

  • Executing Jobs: When a job is defined and scheduled, the job scheduler ensures that the jobs execute successfully. The job scheduler also allows the user to set conditions or alerts to handle anomalies in the job run. This could be as simple as sending a notification to relevant stakeholders or by setting permissions accordingly.

Automation with Cron and Task Scheduler

Operating systems such as Linux and Microsoft Windows have their own task schedulers that allow you to automate tasks.


Cron is an automation tool for Unix-based systems. It can run specified tasks at a set time, date, or interval. Cron is commonly used for tasks like running backups, sending emails, updating databases, or running a python script.

To use cron, you must create a cron job. A cron job is a set of commands or scripts that run at a specified time. Cron requires using the command line, therefore, one must be familiar with cron's syntax before creating a cron job.

Task Scheduler

Task Scheduler is a built-in tool in Windows that allows you to schedule and automate various tasks. With Task Scheduler, you can schedule tasks to run at a specific time or when a certain event occurs.

You can create a basic task in Task Scheduler by following the steps below:

  1. Open Task Scheduler in Windows

  2. Click on Create Basic Task to create a new task

  3. Give your task a name and description for identification.

  4. In the triggers tab, select a relevant trigger according to your specific needs. Click Next when done.

  5. Select a frequency and schedule for the task

  6. Define the type of action you want to perform. 

  7. Once you've defined the task, triggers, and actions, click the Finish button to save and activate the job

With the steps above, you've automated a very basic task. This is a range of possibilities that you can achieve with this simple application.

What are the top tools for ETL automation and scheduling?

The explosion of new companies, products, and features has led to increased complexity, driving the need for modern data stack and data integration tools that centralize data from different applications. Evaluating ELT and ETL tools can be confusing. Below you'll find an overview of the top 5 ELT and ETL tools to consider for your business applications.

1. Portable

Portable is the best data integration tool for teams with long-tail data sources.

Portable is an ETL/ELT platform that features connectors for 300+ hard-to-find data sources.

The Portable team will develop and maintain custom connectors on request, with turnaround times as fast as a few hours.

Key features

  • 300+ built-in data connectors.

  • Fast turnaround time for custom connectors.

  • Ongoing maintenance of long-tail connectors at no additional cost.

Best suited for

Portable is best for teams that need to connect several data sources and want to focus on gleaning insights from data instead of developing and maintaining data pipelines.

2. AutoSys

Autosys is a job scheduling and management tool used by organizations to automate their business processes.

Key features:

  • Automated job scheduling and management

  • Centralized management of business processes

  • Real-time monitoring and reporting on job status and results

Best suited for:

Autosys is best for organizations with complex and time-sensitive business processes. It is also suitable for organizations that require centralized and automated management.

3. Informatica

Informatica is a portfolio of high-performance data virtualization tools. It includes features for data governance, integration services, API integration, analytics, ETL tools, and more.

Key features

  • Comprehensive suite of tools, including Informatica PowerCenter, Informatica B2B Data Transformation, and more.

  • Cloud-based and on-premises deployments available.

  • Advanced data transformation functionality.

Best suited for

Informatica is best for enterprise businesses looking for a robust, comprehensive solution for all kinds of data needs.

4. Oracle

Oracle has a suite of tools for data integration, including Oracle Data Integrator and Oracle GoldenGate. The platform comes with data governance, a data warehouse, and profiling features.

Key features

  • Fully integrated into the Oracle ecosystem of tools.

  • Auto-detection of corrupted data and built-in corrective transformations.

  • Machine learning and AI model deploying capabilities.

  • Metadata extraction.

Best suited for

Oracle is one of the most cost-effective solutions for enterprises that need a data integration solution for massive amounts of data. It's also the easiest solution for businesses fully integrated into the Oracle ecosystem.

5. IBM

IBM has several data tools, including InfoSphere DataStage and App Connect.

Key features

  • Massively parallel processing capabilities.

  • Robust data quality features, including profiling, matching, enrichment, and standardization.

  • Support for cloud-based and on-premise data sources.

Best suited for

IBM's suite of data integration tools is best for teams already using IBM tools in other areas.