ETL — the process of extracting data from multiple sources, transforming it into a format for analysis and loading it into a data warehouse — is tedious and time-consuming, but the advent of ETL automation tools has made it more manageable for organizations big and small.

Understanding how ETL automation works, including ETL testing automation, is beneficial for selecting the right ETL tools and automation solutions for your use case, whether you’re in DataOps or another closely related function.

How ETL works

Automated ETL involves using technology to automate steps in the ETL process. These steps include extraction from different sources, transformation to meet business rules and loading into a target data warehouse.

Automation plays a significant role in streamlining data integration, maintaining data quality and making the entire data management process more efficient. With automation, teams avoid potential data transformation errors and can guarantee that deduplication takes place.

Automating the ETL process also optimizes data processing, making it possible to handle big data quickly and effectively. It streamlines workflows to better conform to the schema of the target data warehouse.

Optimizing the ETL process

Consider the strategies you can use in each stage that drive more efficient ETL processes.

Data extraction

There are several tested methods for optimizing the data extraction process. These include:

Data extraction tools: Data extraction tools or connectors can be used to optimize data extraction. Many of these tools have features to enable caching and connection pooling and optimize data retrieval algorithms. 

Data source considerations: It’s important to understand the characteristics and limitations of your data source systems. If data is extracted from a relational database, its indexes, statistics and database configurations should be optimized for query performance. If it’s extracted from APIs, the pagination, batch processing or rate-limiting mechanisms must be available to optimize data retrieval. 

Filtering and selection: You can apply filters and selection criteria during the extraction process to retrieve only the required data. This can be done by eliminating unnecessary columns or rows irrelevant to the target data model or reporting requirements. 

Incremental extraction: With an incremental extraction strategy, only modified or new data is extracted since the last extraction. This minimizes the amount of source data that needs to be processed. Timestamps, changing data capture (CDC) and other tools can be used to track and extract delta changes only.

Parallel processing: If the source system supports it, you can split the extraction workload across multiple threads or processes to extract data in parallel. This improves speed and efficiency, especially for large datasets. 

Query optimization: For data extraction, queries should be well-structured, use appropriate indexes and avoid joins, subqueries or complex calculations.

Data transformation

The best methodology for optimizing data transformation focuses on improving how the source data is converted from the existing format to the desired format while preserving data accuracy. Strategies include:  

Data profiling: Thorough data profiling helps teams understand the structure, quality and characteristics of source data. This helps identify inconsistencies, anomalies and data quality issues.

Efficient data structures: Data structures, like hash tables or dictionaries for lookups, can be used to create efficient data structures for storing and manipulating data during the transformation process.

Filtering and early data validation: Applying filters and data validation as early as possible will help filter out invalid or irrelevant data. This minimizes processing overhead and improves the speed of data transformation.

Selective transformation: This means applying transformative operations to necessary fields and columns and avoiding transferring any irrelevant data or unused raw data.

Set-based operations: Set-based operations, like SQL queries or bulk transformations, allow multiple records to be processed simultaneously. This is much more efficient than row-by-row processing.

Data loading

Optimizing the data load process involves strategies like:

Batch processing: Transformed data can be grouped into batches for loading into a data warehouse. This reduces the overhead of individual transactions and improves load performance. The optimal batch size can be determined based on data volume, system resources and network capabilities.

Data compression: Compressed data takes up less space and requires less I/O operating during the load process. Compression algorithms can be selected based on query patterns, distribution methodology and types of data.

Data staging: Storing data temporarily in a staging area or landing zone before loading into a data warehouse allows time to ensure only high-quality and relevant data is loaded.

Error handling and logging: Error handling techniques can be used to capture and handle errors that happen during the load process. This helps with troubleshooting and finding opportunities to further optimize the ETL system.

Indexing and partitioning: Data warehouse tables should be indexed and partitioned based on data usage patterns and query requirements. This creates a better data retrieval process by dividing the data into more manageable segments.

Top ETL automation tools

We’re giving you the information you need to start your search for the right ETL automation tool. Below, find our overview of 10 top choices.

1. RunMyJobs by Redwood
2. ActiveBatch by Redwood
3. Tidal by Redwood
4. Amazon Redshift
5. Apache Airflow
6. Apache Hadoop
7. AWS Data Pipeline
8. Azure Data Factory
9. Oracle Autonomous Data Warehouse
10. Qlik Compose

ETL automation tool comparison

RunMyJobs by Redwood

RunMyJobs by Redwood is an ETL automation solution designed for hybrid IT teams and enterprise companies to help scale data processes so DevOps teams can easily adapt to evolving business requirements.

With RunMyJobs, you can:

• Automate repetitive tasks, including ETL testing, with no-code templates to execute workflows based on source data, files, events and more.
• Centralize control over resource provisioning across ERP, CRM and other systems through a single dashboard.
• Coordinate and integrate with your other essential data tools, including API adapters and cloud service providers such as Amazon Web Services and Google Cloud.
• Ensure consistent data security with TLS 1.3 encryption and agentless connectivity to SAP, Oracle, VMS and other applications.
• Establish comprehensive audit trails and enforce business rules across teams and departments.
• Extend your workflow orchestration beyond data to your business processes while maintaining one intuitive interface, with drag-and-drop components for easy automation design.
• Simplify your cloud data warehousing with low-code data integration and cloud-native data management.

Find out more by scheduling a demo of RunMyJobs.

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ActiveBatch by Redwood

ActiveBatch by Redwood is a powerful workload automation and job scheduling tools that enables seamless automation of ETL workflows with its pre-built integrations and advanced scheduling options.

With ActiveBatch, you can:

• Access a library of pre-built job steps and integrations for various applications, databases and platforms, reducing the need for custom scripting.
• Empower your business users to run, monitor and manage processes with a user-friendly self-service portal.
• Handle complete, large-scale workloads with high-availability features.
• Meet stringent compliance or regulatory requirements with comprehensive auditing and governance tools.
• Use advanced date/time and event-driven scheduling to create end-to-end process automations and increase job success rates.

Learn more about ActiveBatch.

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Tidal by Redwood

Tidal by Redwood provides enterprise-grade workload automation with features like predictive analytics and SLA management, making it ideal for complex ETL processes.

With Tidal, you can:

• Access 60+ pre-built integrations, including adapters for JD Edwards and Oracle databases.
• Automate intricate workflows with complex dependencies.

• Monitor and correct issues in critical business processes and workflows with proactive alerts and SLA remediation.
• Take advantage of developer-friendly features like a full API, CLI and adapters for SSH and web services to integrate the applications of your choice in your workflow automations.
• Utilize machine learning algorithms to predict workload patterns and resource utilization.

Learn more about Tidal Software.

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Amazon Redshift

Amazon Redshift is a fully managed, scalable data warehousing solution optimized for fast querying and analytics. It’s suitable for storing and processing large datasets.

Key features of Amazon Redshift include:

• Easy integration with AWS services and ETL tools
• Massively Parallel Processing (MPP) for faster data processing
• Petabyte-scale storage capacity

Learn more about Amazon Redshift.

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Apache Airflow

Apache Airflow is an open-source workflow orchestration tool that’s ideal for building, monitoring and scheduling dynamic ETL pipelines using Python. Its modular, extensible design supports a wide array of data sources.

Key features of Apache Airflow include:

• A web-based user interface to track the progress and status of workflows
• Dynamic pipeline generation
• Rich set of integrations and the ability to create custom plug-ins

Learn more about Apache Airflow.

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Apache Hadoop

Hadoop is a distributed computing framework designed to process and store massive datasets across clusters of machines, making it a popular choice for big data ETL. Hadoop’s fault tolerance and scalability make it reliable for high-volume operations.

Key features of Hadoop include:

• Automatic failure handling at the application layer
• Data locality to reduce network congestion
• Distributed data processing model for big data tasks

Learn more about Hadoop.

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AWS Data Pipeline

AWS Data Pipeline is a managed ETL service that automates the movement and transformation of data across AWS and on-premises sources. It features flexible scheduling and robust error handling.

Key features of AWS Data Pipeline include:

• Built-in error handling and automatic rerty mechanisms
• Fine-grained access controls
• Templates for the majority of AWS databases

Learn more about AWS Data Pipeline.

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Azure Data Factory

Azure Data Factory is a cloud-based ETL service that enables hybrid data integration with a visual, code-free interface, making it easy to design and manage complex data workflows. It offers the scalability to handle large data volumes.

Key features of Azure Data Factory include:

• A visual interface for designing ETL workflows without coding
• Built-in connectors for ingesting data from on-premises and SaaS sources
• Managed SQL Server Integration Services (SSIS)

Learn more about Azure Data Factory.

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Oracle Autonomous Data Warehouse

Oracle Data Warehouse is a high-performance data warehousing solution that integrates analytics and machine learning, enabling efficient ETL processes and complex data transformations.

Key features of Oracle Autonomous Data Warehouse include:

• Advanced in-database analytics
• Automated provisioning, configuration, scaling and more
• Self-service data management tools for loading, transforming and sharing

Learn more about Oracle Autonomous Data Warehouse.

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Qlik Compose

Qlik Compose is a data integration tool that automates ETL processes for data warehouse and analytics tasks and supports the acceleration of data integration and transformation.

Key features of Qlik Compose include:

• Ability to combine data warehouse and data mart tasks in a single workflow
• Automated data model design and source mapping
• Real-time data streaming

Learn more about Qlik Compose.

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The importance of ETL testing

ETL automation doesn’t end with automating the processes in each stage. You also need to building ETL testing — the process of verifying and validating an ETL system. When you test your ETL processes, you ensure that every step goes according to plan.

This is a critical activity for data validation, specifically accuracy and consistency. Testing also mitigates risks, optimizes system performance, aids in quality assurance and makes it easier to comply with regulatory requirements. By performing tests like data completeness checks, data transformation validations and data reconciliation, a data team can identify discrepancies, errors or data loss during extraction, transformation or loading.

ETL testing is part of the overall quality assurance process for data integration projects. It helps ensure data is correctly transformed and loaded to meet specific business rules and requirements. The ETL testing process also includes performance testing. This evaluates the efficiency and speed of each stage of ETL. By identifying bottlenecks, optimization opportunities and scalability issues, performance tests improve the overall responsiveness of your ETL processes.

Finally, it’s important not to overlook regression testing, which ensures new changes haven’t introduced unexpected issues or errors in previously validated ETL processes.

Because ETL systems handle significant volumes of valuable — and sometimes sensitive — data, risk mitigation is crucial. By conducting comprehensive testing, your organization can mitigate risks associated with data inaccuracies, incomplete transformations or data loss. This protects the reliability and trustworthiness of your data.

Many industries, including finance, healthcare and retail, have strict compliance and regulatory requirements regarding data integrity, privacy and security. ETL testing can validate data handling processes to make compliance with relevant regulations and standards much easier.

Top ETL testing tools

There are a number of ETL testing tools available for teams to choose from, each with unique features and functionality. Below are five of the most popular.

1. Apache Nifi: Apache Nifi is an open-source data integration and ETL tool with a visual interface for designing and executing data flows. It offers capabilities for transformation, routing and quality checks. Apache Nifi supports real-time data processing and integrates with various data sources and target systems.
2. Informatica Data Validation Option: Informatica is an ETL tool with comprehensive data validation and testing capabilities. It provides features for data profiling, data quality checks, metadata analysis and rule-based validation. Informatica supports automated and manual testing.
3. Jaspersoft ETL: Jaspersoft ETL is an open-source ETL tool with a graphical user interface for workflow design and execution. It offers features for data transformation, cleansing and validation. Jaspersoft ETL supports various databases, platforms and data stores.
4. Microsoft SQL Server Integration Services (SSIS): SSIS is a popular Microsoft ETL tool. Features include data integration, transformation, ETL testing and debugging. SSIS integrates well with Microsoft SQL Server and other Microsoft products.
5. Talend Data Integration: Talend is an open-source ETL tool with powerful testing and data integration features. It provides data mapping, transformation and validation. Talend allows users to design and execute test cases, perform data quality checks and facilitate test automation.

To perfect each stage of ETL, you need the support of a powerful platform. Discover the ways RunMyJobs could revolutionize your ETL processes: Book a demo today.

Python, a versatile and powerful programming language, complements ETL automation with numerous tools and libraries.

In this article, we explore why you might choose Python for building ETL automation and look at the pros and cons of popular ETL tools and a full stack workload automation solution.

What is ETL automation?

ETL automation is the process of automating the extraction, transformation and loading of raw data from multiple data sources into a data warehouse or other storage system. Using software tools and scripts, you can streamline these processes and reduce errors by eliminating manual intervention.

Before automation became widely available, ETL processes were performed manually and, therefore, quite time-consuming. Now, organizations of all sizes can leverage ETL tools and frameworks to automate repetitive tasks and manage complex datasets. These tools not only save time and improve resource allocation but also enhance data quality, consistency and integrity. 

What are ETL pipelines?

ETL pipelines are workflows that define steps and dependencies involved in ETL processes. These pipelines specify the order in which data is extracted, transformed and loaded to enable a seamless flow of information. ETL pipelines often involve directed acyclic graphs (DAGs) to represent dependencies between tasks.

Each task in the data pipeline performs a specific ETL operation. This could include data extraction from one or more data sources, data aggregation, transformations or loading the transformed data into a target system (data warehouse, data lake or similar).

By organizing tasks into an ETL pipeline, data engineers can automate the entire process while maintaining data consistency and integrity.

What is Python for ETL?

Python is a programming language widely adopted for building ETL workflows due to its flexibility, simplicity and extensive library ecosystem. It allows data engineers and analysts to create custom ETL processes tailored to specific business needs, ensuring that data is processed efficiently and accurately.

Python offers a range of built-in and third-party libraries like Pandas, NumPy and PySpark, which are specifically designed for handling and transforming data. These tools enable users to extract data from various sources, apply complex transformations and load it into databases or data warehouses seamlessly. Additionally, Python’s robust integration capabilities allow it to interact with APIs, cloud platforms and other systems, making it a versatile choice for modern ETL workflows.

By leveraging Python, organizations can build scalable, reusable ETL solutions that align with their evolving data management requirements.

Why is Python used for ETL?

Python is favored for ETL because of its powerful data manipulation capabilities, extensive library support and community-driven ecosystem. Combined with the right automation tools, Python enables sophisticated scheduling and orchestration of ETL pipelines, ensuring timely data delivery.

Python is also open-source, making it a budget-friendly option for organizations seeking to implement powerful ETL solutions without high licensing fees.

Benefits of Python for ETL automation

When it comes to ETL automation specifically, Python offers several advantages:

• Clean and intuitive syntax: Python syntax is easy to learn and read, making it accessible for beginners and well-liked by experienced programmers. The syntax allows developers to write concise and readable code in less time and maintain it with ease.
• Data integration capabilities: Python integrates easily with multiple data sources, data streams and formats, including CSV files, JSON, XML, SQL databases and more. Python also offers connectors and APIs to interact with popular big data tools like Hadoop and data storage systems like PostgreSQL and Microsoft SQL Server.
• Ecosystem of Python libraries: Python has a vast collection of open-source libraries for data manipulation. These include Pandas, NumPy and petl. Python libraries provide powerful tools for analysis and transformation.
• Scalability and performance: Python’s scalability is enhanced by libraries like PySpark that enable distributed data processing for big data analytics. Python also supports parallel processing for more efficient resource utilization.

Other programming languages that can be used for ETL processes include Java, SQL, HTML and Scala. 

Best Python ETL tools: Pros and cons

There are a number of Python ETL tools and frameworks available to simplify and automate ETL processes. Here, we’ll cover the pros and cons of the most popular tools. 

Apache Airflow

Apache Airflow is a Python-based workflow orchestration tool designed to manage and automate complex ETL pipelines through the use of DAGs.

Key features

• Airflow operators: Templates that can handle tasks such as data orchestration, transfer, cloud operations and even SQL script execution
• ‍Scheduling for data pipeline workflows tailored to your needs using cron expressions, custom triggers or intervals
• ‍Visualization for complex data pipeline workflows to make data more accessible to technical and non-technical users

Pros

• Open-source tool
• Support for plugins and custom operators
• Works well with major cloud platforms, APIs and databases

Cons

• Can be overly complex for small projects
• May be resource-intensive

Great Expectations

Great Expectations is a Python-based data validation framework that ensures data quality by enabling automated testing and profiling within ETL pipelines.

Key features

• A huge library of predefined expectations for various data types, such as textual, numerical and date/time data
• Customizable expectation suites that allow you to create your own expectations for applying with specific data sets
• Support for various data sources and formats like Databricks and relational databases
• The ability to integrate with an existing data pipeline to add data validation and quality checks to existing workflows

Pros

• Clear, human-readable documentation
• Specializes in validating and profiling data
• Built-in integrations for popular ETL tools

Cons

• Not a full ETL solution
• Works best for batch validations instead of real-time data checks

Pandas

Pandas is a popular Python library for data manipulation and data analysis. It provides data structures like DataFrames, which are highly efficient for handling structured data.

Key features

• Built-in functions for analyzing, cleaning, exploring and manipulating data
• Suited for different data types: tabular data, time series, arbitrary matrix data or any other form of statistical or observational data sets
• Easy implementation of all kinds of ETL practices, including data extraction, transformation, handling, cleaning, validating, data type conversion and exporting.

Pros

• Rich functionality
• Extensive documentation
• Wide adoption within the data science community

Cons

• Not ideal for processing extremely large datasets because of in-memory nature
• May require tools like PySpark for big data processing

pETL

pETL is a lightweight Python library for ETL tasks and automation. It provides simple and intuitive functions for working with tabular data for fast data manipulations. 

Key features

• Extract functions
  • fromcsv(): Extracts data from a CSV file and returns a table
  • fromjson(): Extracts data from a JSON file and returns a table
  • fromxml(): Extracts data from an XML file and returns a table
  • fromdb(): Does the same task of returning a table but from an SQL database
• Transform functions
  • select(): Filters rows from a table based on the condition you provide
  • cut(): Selects specific columns from the table you provide
  • aggregate(): Performs aggregation tasks such as adding, counting and averaging in one or more tables
  • join(): Combines two or more tables based on common keys
• Load functions
  • tocsv(): Loads the provided table in a CSV file
  • tojson(): Loads the provided table in JSON file
  • toxml(): Loads the provided table in an XML file
  • todb(): Loads the provided table in the SQL database

Pros

• Ease of use
• Memory efficiency
• Compatibility with various sources

Cons

• Lacks advanced features compared to other ETL tools
• May not be sufficient for complex ETL workflows

PySpark 

PySpark is a Python library for Apache Spark, a distributed computing framework for big data processing. It provides a high-level API for scalable and efficient data processing.

Key features

• The flexibility to create custom ETL pipelines, unlike many GUI-based ETL tools
• Advanced scalability due to its distributed computing framework that allows it to scale to handle large datasets
• Easy code automation using tools such as Apache Airflow or Prefect
• Optimal performance due to the ability to take advantage of multiple cores and processors

Pros

• Versatile interface that supports Apache Spark’s features, including machine learning and Spark Core
• Fault tolerance
• Easy integration with other Spark components

Cons

• Harder to learn for beginners compared to other Python ETL tools
• Requires a distributed cluster environment to leverage all capabilities

The workload automation approach to ETL 

Instead of limiting your data team to a Python-specific solution or ETL testing tool, consider how much greater efficiency you can achieve with a platform built to develop your true automation fabric.

RunMyJobs by Redwood is a workload automation solution that can effectively manage and schedule ETL jobs, but it’s also designed to orchestrate complex workflows, monitor job executions and handle dependencies between tasks for any type of process. While not a Python-specific tool, Redwood can seamlessly integrate with Python scripts and other ETL tools — it’s an end-to-end automation solution.

Teams can easily automate repetitive tasks with Redwood’s no-code connectors, sequences and calendars and execute workflows in real time based on source files, events, messages from apps and more. Plus, you can engage in custom workflow management using consumable automation services and native SOA APIs and formats.

RunMyJobs expands as your DevOps activities evolve to support new business requirements. By coordinating resource management in hybrid environments, your team can use it to automate common ETL and testing, data warehousing and database tasks. Access real-time dashboards to manage big data, business intelligence tools and more, all through an interactive, drag-and-drop interface.

Integration with a variety of web services and microservices allows your team to use the tools and technologies they prefer. RunMyJobs makes it easy to automate tasks between services, including Apache Airflow, Google TensorFlow, GitHub, Microsoft Office 365, ServiceNow, DropBox and more.

Developers can choose from more than 25 supported scripting languages, including Python code and PowerShell, and can work from a command-line user interface with built-in parameter replacement and syntax highlighting.

Your team will have the resources they need for quick adoption in Redwood University, which offers tutorials for countless use cases and ETL jobs. Demo RunMyJobs to explore how to enhance your Python-driven ETL processes.

Here, we’ll explain how job orchestration tools can bring your enterprise into the next generation of efficient, reliable, automated IT orchestration solutions.

What job orchestration is

Simply put, job orchestration is taking disparate business processes throughout your enterprise and bringing their oversight and management together in one location. Orchestration in the IT world is, as the name suggests, like the process of a conductor leading an orchestra. Each section of instruments (string, brass, percussion, etc.) has its own instructions for playing different parts of the music, and sections must harmonize with one another. The conductor is responsible for monitoring all of the sections and making sure they work together to create a symphony of sound. The conductor is the orchestration tool.

In the same way, job orchestration integrates business processes and workflows that may have formerly been done in silos across an entire enterprise using job scheduling and workload automation (WLA).

Why you need job orchestration tools

Workload automation solutions and job scheduling tools can only get your IT teams and initiatives so far on their own. They need to be able to quickly and seamlessly integrate new technologies into existing IT processes so that the end-to-end process lifecycle remains stable. Now, more than ever, there are a myriad of choices when it comes to finding applications and platforms to suit different IT workload management needs.

A job orchestration tool gives you a simple, low-code, high-observability platform that allows you to use your IT resources, whether something like servers or people, efficiently. You get real-time, event-driven job scheduling and automated processes that use machine learning to resolve issues that arise, such as jams in your data pipeline, failed batch jobs or unsuccessful big data file transfers when your server dependencies are not properly aligned.

Orchestration can streamline your workflows so that automated processes are as cohesive as possible without needing custom code or manual intervention. And, through orchestration tools, you gain the ability to oversee everything happening in your system at once through a single pane of glass. Real-time auditing and SLA monitoring allow you to make sure your workload automation solutions and job scheduling tools are giving you the optimization you need to succeed.

Where you can use job orchestration

The applications for job orchestration in your enterprise are far-reaching, with use cases including:

• Data management automation: Synchronize your ETL and data orchestration tools with real-time data feeds managed from one place.

• DevOps: Keep track of your CI/CD processes throughout the entire lifecycle of a new build and release.
• ERP automation: Logically integrate automated processes across ERPs like SAP and Oracle, eliminating limitations from built-in tools.
• IT ecosystem: Coordinate resource provisioning and automate maintenance tasks to free up your IT team’s time.
• Managed file transfer (MFT): Securely transfer millions of files seamlessly and with total confidence across systems and locations, internally and externally.

When you need to implement job orchestration tools

If more than one part of your enterprise is automated or needs automating, then the answer is right now! The sooner you adopt the modern practice of job orchestration, the better off you will be in the long run. Automation is a key component of digital transformation. The evolution of workflow management across all types of business processes is going to continue to require full scalability, cost savings and ease of use in new innovations, and job orchestration tools are the key to that future.

Who to trust for job orchestration expertise

In vetting a job orchestration tool, look for a platform that will give you complete control over all current technologies, as well as future ones, that your enterprise employs.

RunMyJobs by Redwood is a cloud-native SaaS job orchestration tool that does just that. It gives you secure, reliable job and workflow orchestration with accessible, self-service design and deployment using low-code/no-code automation tools and drag-and-drop templates.

Recognized by Gartner® as a Leader, positioned furthest in Completeness of Vision and highest in Ability to Execute in its 2025 Magic Quadrant™ for Service Orchestration and Automation Platforms (SOAP), RunMyJobs offers agile, efficient, cost-saving business process automation with cross-platform adaptation to any infrastructure and architecture — cloud-native, on-premises or hybrid. Use pre-built connectors or build reusable adapters with APIs to incorporate the capabilities of your existing tech stack into your end-to-end processes.

RunMyJobs is infinitely extensible — it seamlessly integrates with ERP providers like SAP and Oracle, programming languages like JavaScript and Python and operating systems, including open-source offerings like Linux. The orchestration platform also works with cloud-based service providers like Google Cloud, AWS and Microsoft Azure.

Read the full Gartner report to learn more about RunMyJobs’ SOAP capabilities.

Magic Quadrant is a trademark of Gartner, Inc. and/or its affiliates.

This isn’t simply another name for workload automation solutions (WLA solutions). Rather, Gartner® describes a SOAP as a workload automation platform designed for event-driven business models and cloud infrastructure. Infrastructure and operations leaders (I&O leaders) can use these advanced tools to drive customer-focused agility to support their cloud, big data and DevOps initiatives.

The 2025 Gartner® Magic Quadrant™ for SOAP report states that “by 2029, 90% of organizations currently delivering workload automation will be using service orchestration and automation platforms (SOAPs) to orchestrate workloads and data pipelines in hybrid environments across IT and business domains.” 

This type of automation platform provides a mix of workflow orchestration and resource provisioning across an organization’s hybrid digital infrastructure. SOAPs can adapt to cloud-native infrastructure and application architecture, unlike legacy job scheduling and workload automation tools.

These capabilities integrate with DevOps toolchains to provide better agility so businesses can quickly develop IT automation strategies that directly improve customer outcomes and experiences.

The evolution and impact of modern orchestration tools

The 2025 Gartner Critical Capabilities for SOAP report states that, “The service orchestration and automation platform (SOAP) market is mature, with vendors differentiated by core capabilities, innovation, integration breadth, use-case support and market responsiveness.“

Many WLA tools have evolved to offer SOAP capabilities to keep up with enterprise demands. Instead of focusing only on core workflows in siloed domains, SOAPs automate workflows that span diverse domains, including cloud-based and big data applications. These include complex IT processes, large volumes of file transfers, orchestration of data pipelines and extract, transform, load (ETL) processes.

Minimizing complexity and increasing speed and agility with a single platform to manage all business processes is far more efficient than relying on a mix of separate tools.

Benefits and use cases of SOAPs

SOAPs enable both IT teams and business users to take advantage of real-time, event-driven orchestration of business services. This capability is increasingly essential for businesses to respond quickly and decisively to customer needs. Event-driven automation runs processes the instant they are needed instead of waiting for a pre-scheduled time.

They also provide self-service automation tools and integrate with other platforms, systems, data centers and more via APIs and built-in connectors.

SOAPs offer:

• Increased agility: Responding to shifting business and market demands and customer preferences is essential today. With the breadth of a SOAP, IT teams can rapidly adjust.
• Cost savings: A SOAP streamlines operations and reduces the need for manual intervention in mundane tasks and processes such as billing, appointment scheduling and more.
• Seamless coordination: Cross-departmental collaboration for critical operations like IT service management and data management are easier and more efficient with a SOAP.
• Improved customer experience: SOAPs support both internal, SLA-focused tasks and customer-facing automation, such as real-time order tracking or automated customer service workflows.

The power of SOAPs is clear in a variety of industries and use cases. For example, retailers can use SOAPs to orchestrate inventory management, customer orders or supply chain operations. Those in the finance sector can streamline regulatory compliance, fraud detection and transaction processing. Utility companies can monitor and maintain power grids or water systems, detecting faults and resolving outages quickly.

Access advanced orchestration capabilities with a Gartner SOAP Leader

It’s important to choose the right SOAP solution. We believe this means choosing one that’s been recognized.

Redwood Software is a Leader in the 2025 Gartner Magic Quadrant™ for SOAP report for the second year in a row, positioned furthest in Completeness of Vision and highest for Ability to Execute. We believe this consistent recognition validates our strategy and the immense value of our automation fabric solutions.

Redwood’s WLA solutions, including RunMyJobs by Redwood, deliver SOAP capabilities for hybrid-cloud, multi-application IT environments and support event-driven business models. With RunMyJobs’ SaaS delivery, you’ll have the reliability and scalability you need without the maintenance effort and cost.

Find out how RunMyJobs meets the needs of digital businesses now and in the future: Book a demo.

Magic Quadrant is a trademark of Gartner, Inc. and/or its affiliates.

Now these benefits have been recognized by independent experts as well: Enterprise Management Associates® (EMA™) has named Redwood as Value Leader and RunMyJobs as the Best workload automation SaaS Solution in its 2019 EMA Radar™ for Workload Automation report.

According to EMA, RunMyJobs is the best WLA SaaS offering available because it is the only such solution that is ”purpose-built for that delivery model”.

Legacy workload automation tools that are simply lifted and shifted to the cloud may become cheaper to run but they retain their inherent limitations.

RunMyJobs overcomes those limitations of legacy workload automation solutions. It takes advantage of the attributes of cloud architecture to cut across silos and provide the kind of coordinated approach to processes your business needs.

It supports unique business logic so processes work in concert with each other. In this way, it avoids the accumulation of workarounds (or technical debt) that multiple legacy schedulers bring and overcomes the limitations of ‘new day’ scheduling.

As EMA president and COO Dan Twing explains: “Since it’s built from the ground up for the cloud it’s easy to use RunMyJobs to integrate applications seamlessly into next-generation operating models while covering transitional needs today.”

With no need for workarounds or multiple tools, the EMA Radar report also cites the ability of RunMyJobs “to provide simplified operational management at a low total cost of ownership”.

This low TCO is demonstrated by chipset manufacturer AMD, which has saved $200,000 on on-premises data center costs each year by using RunMyJobs to integrate automated processes across its SAP® environment, as well as other areas including shipping, payroll, enterprise data warehouse, managed file transfer and cloud applications.

As for the abilities of RunMyJobs, “there’s no end to what’s possible”, according to Bhanu Pratap Singh, an IT Architecture manager at AMD.

Don’t let the transformation potential of moving to the cloud be limited by legacy job scheduling tools. RunMyJobs provides the benefits of modern workload automation combined with the power of the cloud. All of which makes migrating to Redwood a no brainer.

RunMyJobs®, the world’s only enterprise workload automation as a service solution. Read the 2019 EMA Radar™ for Workload Automation (WLA) to find out more about why RunMyJobs by Redwood is the only solution to use.

The need for a business to be responsive is – as we and those wiser than us have noted many times before – crucial. Whether it’s customer-facing functions or back-office nuts and bolts, the ability to respond immediately to changes is a constant. That’s the reality of expectations in the digital world.

And that’s a reality that IT must be able to dynamically support by running tasks at any point. We discussed in the previous post how the sheer inflexibility of many WLA tools set to rigid daily schedules can cause problems in a process-centric world.

However, another risk is that these aging and out-of-date scheduling tools can also get really expensive when they don’t have to. One of the key reasons for this is that solutions like Control-M, for example, tie the cost to opaque licensing and per-definition charges. For an initial deployment, this doesn’t present too much of a problem, but as time passes, the cracks begin to show.

Scaling automation solutions based on the number of process definitions in use can quickly send project costs spiraling out of control – and ultimately lead to misdirected disillusionment with automation more widely, in some instances.

And that’s before you factor in the unexpected license audits and all the wasted definitions you end up paying for during testing.

It’s an out-of-date pricing model that’s ill-suited to today’s cloud- and the service-based world. It’s one that all too often means customers end up paying more than they expected. And it’s one that you won’t find with RunMyJobs by Redwood® scheduling, which means no nasty pricing surprises for our customers.

Transparency and visibility are things that we consider central tenets of our approach at Redwood. They’re supported by our per-usage product pricing and our central web-based dashboard that simplifies process automation across silos of both legacy and new applications (which, incidentally, also reduces the costs of running those processes).

We hear many complaints about rival products from new customers who migrate to our process automation solutions. While we could point out all the ways in which those other products truly cannot compete, we’d rather give you the chance to see for yourself how RunMyJobs by Redwood goes beyond your current process automation expectations.

Get your own free, no-commitment RunMyJobs by Redwood scheduling trial here.