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Workload Automation is Here to Stay

For decades, workload automation (WLA) has quite literally been running society. And yet, in recent years, IT seems to have forgotten the critical role WLA plays in running its business. All the talk is about data pipelines and the tools to manage them. But data pipeline tools alone will not deliver the service availability and accuracy levels that mission-critical data pipelines demand. They must be coupled with proven WLA solutions that ironically have been in place at most enterprises for decades.

When you shop, whether it is online or at your favorite store, chances are WLA is used to manage inventory, generate purchase orders and schedule deliveries. WLA is critical to improving efficiency and accuracy for retailers’ supply chain operations and to meet customer demands in a timely manner.

Similarly, most manufacturers use WLA solutions to manage inventory levels, plan production scheduling, monitor equipment performance and quality control, and manage logistics processes, including shipping and receiving of raw materials and finished goods.

So, why does it seem like IT is turning its back on workload automation?

Introducing Data Pipelines

To answer this question, we need to understand a bit of the history of data pipelines. The concept of data pipelines originates from data warehousing as a way to consolidate data from disparate sources into a centralized repository for reporting and analysis.

As the volume and variety of data being processed grew, new technologies and approaches emerged to address the challenges of data integration and processing. One of these approaches was the concept of ETL (extract, transform, load) to automate data integration and processing. ETL tools typically involved designing data flows and transformations, as well as a runtime engine for executing the data pipeline.

With the rise of big data technologies, such as Hadoop and Spark, data pipelines evolved to become more flexible, scalable, and distributed. And with the sheer scale of the data being processed, the cloud became a natural fit to stream, ingest, process, transform, analyze, store, and deliver data. As such, data pipelines have evolved to become an essential component of modern data architectures. With that evolution the cloud has developed its own tools to manage automation, forgetting that WLA has already solved this problem.

Apache Airflow: Data Pipeline Automation Or Workload Automation?

Initially created by Airbnb in 2015, Apache Airflow was developed to manage its data pipelines. Today, it is an open-source platform used to programmatically schedule, manage and monitor workflows or data pipelines. Workflows in Airflow are defined using Python code, allowing for the creation of complex and reusable workflows.

The use of Python makes Airflow friendly to both the developer and the data scientist community. Python has become the lingua franca for machine learning and data manipulation. Today, Airflow has a very large and active community of contributors and has become the most popular data pipeline solution among developers. With such popularity, why should we consider traditional WLA tools that run billions of jobs daily?

Not Just About Running Jobs Or Processing Data

One of the biggest challenges an organization faces is managing service level agreements (SLAs). It’s not just about processing data; it’s about processing and delivering data in a timely and accurate manner. For a large investment firm, for example, that needs to analyze market data, generate trading reports and execute trades, missing a market deadline SLA can represent massive losses.

But managing SLAs presents some real challenges considering how complex data pipelines are, such as:

  • Data volume and complexity – Data pipelines often process large volumes of data, and the variability of the volume and complexity of data can result in tasks taking longer than expected.
  • Dependencies on external services or other data pipelines – Data pipelines often depend on the completion of other tasks or pipelines such as external services, cloud storage systems, message brokers, and compute resources.
  • Variability of data and accuracy –Some data being processed can be structured, semi-structured, or unstructured. Data pipelines need to have built-in mechanisms to ensure the data is processed and delivered with a high degree of accuracy and quality.
  • Data pipelines don’t live in isolation – A data pipeline is a component of a larger end-to-end business process that may require setting up infrastructure, configuring systems, installing software, etc.

For the last 50 years, WLA solutions have addressed these challenges, albeit almost silently. In fact, most large enterprises have hardened these pipelines in such a way that most of them experience less than 0.05 percent failure rate in their daily job runs. Best-in-class organizations achieve less than 0.01 percent failure rates.

How Can Data Pipelines Consistently Meet SLAs?

When it comes to ensuring SLAs are being met, WLA solutions have proven themselves. Beyond the capabilities offered by data pipeline tools such as Airflow, WLA tools provide the following:

  • Scalability and availability – WLA solutions can scale to millions of jobs executed per day from a single controller, which can be configured for high availability.
  • Visibility – WLA solutions provide real-time visibility into the status of jobs and workflows, allowing teams to monitor performance, identify bottlenecks, and quickly resolve issues.
  • Risk and capacity analysis – WLA solutions provide sophisticated predictive analytics that analyze and compare the execution of jobs and workflows to past executions to anticipate possible SLA risks.
  • Operational control – WLA can automate and integrate your complex workload, workflows, and business processes across automation platforms, ERP systems, etc.
  • Flexibility – WLA solutions can be customized to integrate with a range of systems and services and allows organizations to build workflows that can be adapted to changing business requirements.

While workload automation may not be the trend du jour, WLA has already solved several of data pipeline challenges. WLA’s offer the ability to create robust data pipelines that can scale to the needs of the largest organizations while ensuring effective operational management and meet the needs of the business by delivering accurate data on time every single day!

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