Workload automation solutions have the power and flexibility to handle virtually any data warehouse operation, regardless of complexity.
Data centers are exceedingly busy; managing many data warehouse operations poses significant challenges. Amidst the complexity of data sources, ensuring data quality, efficient data modeling and executing SQL queries, IT teams often struggle with time-consuming tasks. Add the demand for real-time data, enterprise-wide data integration and streamlined decision-making processes increases the strain on traditional data warehousing approaches.
Data warehouse automation: Gain data warehouse control and visibility
Today’s intelligent automation platforms have the power and flexibility to handle virtually any traditional data warehouse operation, regardless of complexity. Tested logic and drag-and-drop convenience of workload automation solutions streamline the process, allowing staff to design, build, implement and monitor workflows faster, more reliably and with less custom scripting, increasing time-to-value.
Let’s look at the positive impact of workload automation on data warehouse operations by looking at some use cases of experienced users.
Optimize ETL processes with a single job scheduler
According to Gartner, most IT organizations have, on average, three to eight different scheduling and automation tools to learn and maintain. This siloed approach often leads to inefficiencies and operational challenges. Adopting a unified cross-platform workload automation solution offers a remedy to this issue by seamlessly accommodating the diverse array of data sources, applications and environments—including on-premise, cloud-based and virtual setups—enabling streamlined management of IT operations while enhancing adaptability and efficiency across the organization’s IT infrastructure.
Minnesota-based Xcel Energy employs such a solution. It operates a hybrid Windows/UNIX environment and, among many other tools, uses Informatica PowerCenter to manage its ETL tasks. With its comprehensive workload automation solution, Xcel Energy can pull data from its in-house work order management application via an FTP operation, then execute an Informatica PowerCenter workflow to upload that information into a data repository for reporting purposes, managing it all from a single platform.
Benefits of data warehouse automation: decreasing dependence on custom scripts
The prebuilt job steps in workload automation solutions can significantly reduce coding time—by as much as half, in many cases. “[Our batch processes] are dynamic, constantly changing,” noted the senior director for IT for The Retail Equation, a “big data” retail analytics provider. “Using a script-driven solution added a lot of man hours to building and managing these workflows.” By replacing its script-driven job scheduler with an automation platform, The Retail Equation’s IT department now spends less than 5% of its time building and managing batch workloads. In one case, it was able to reduce the number of job steps required to execute its nightly SFTP/FTPS file system processes from 131 individual steps to just 4 or 5.
Speed up workflow design and minimize repetitive tasks
Lamar Advertising, one of the largest outdoor advertising companies in North America, uses workload automation to ease workloads for busy in-house developers. Using the automation platform’s workflow designer, Lamar can use templated job steps for virtually every scheduled job, assembling workflows with drag-and-drop simplicity. “Any time we can take an IT assignment out of the development area, that’s a plus,” notes the company’s MIS operations manager. Lamar currently runs 10 to 12 thousand data warehousing, ETL and accounts receivable jobs each day, with a 99% success rate.
Dynamic event-driven triggers
Standard date/time job triggers have serious limitations, as Children’s Hospital & Medical Center of Omaha can attest. “A job may take one hour one day and 20 minutes on another—yet we would have to schedule a ‘worst-case scenario’ each time,” said the center’s ETL architect. By moving to the event-based triggers in his automation solution, the center was able to eliminate downtime between jobs altogether. It allows the hospital to execute and manage multi-job workflows based on IT events such as a file constraint or file being received, ensuring that jobs aren’t run until feeder systems are fully updated.
Third-party integrations and connectors
Workload automation with data warehouse automation tools provides a host of benefits—especially for those using third-party tools and products like Informatica PowerCenter and Informatica Cloud.
In his role as a senior IT architect for one of the world’s largest biotech companies, Peter MacDonald utilizes Informatica PowerCenter to manage data warehouse operations. At one time, MacDonald’s financial warehouse processes were not automated—and it took up to 12 hours each day to extract MDM data sets, execute PowerCenter workflows, administer database processes from Informatica mappings and build Hyperion cubes.
“Clearly, we needed to replace manual steps with automation,” MacDonald stated. After moving to a workload automation solution that provided prebuilt job steps for Informatica Cloud and PowerCenter, MacDonald implemented a four-phase program that now runs workflows of substantially increased complexity, involving three to four times the volume of data (as much as 90 million rows per day), in just 45 minutes.
“Done well, IT Automation drives progress and brings value across the business,” notes MacDonald. “It increases visibility and scales multi-dimensionally to increase speed, efficiency and accuracy. It even reduces IT labor, since the intuitive user interface allows business analysts to do scheduling, instead of IT.”
See Peter MacDonald discuss how his organization reduced data processing workflow times by more than 93% while improving reliability and adapting faster to changing business and IT conditions:
What Every Informatica User Needs to Know About Data Management Automation
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Data Automation FAQs
Data warehouse automation (DWA) refers to the process of streamlining and automating the data warehouse development cycles using technology to automate routine tasks. This includes code generation for data modeling and ETL (Extract, Transform, Load) processes, managing data workflows and optimizing data storage and retrieval. Automation reduces manual effort, decreases the likelihood of errors and speeds up the data delivery process, which is crucial for timely business intelligence and data-driven decision-making.
Automated tools work with metadata to facilitate system understanding, integrate with APIs for connectivity and often leverage machine learning to improve performance. Cloud platforms like AWS, Azure and Snowflake are commonly integrated with such tools to enable scalable, efficient data warehouse solutions that meet evolving business requirements and support data visualization and analytics for better business decisions.
Learn more about IT automation how to improve data warehouse orchestration.
Automating an ETL process is achieved by utilizing software tools that orchestrate the data flow from source to destination with minimal human intervention. These tools manage the entire lifecycle of data movement, facilitating the extraction of data from various sources, appling predefined transformation rules to cleanse and formating the data and then load the processed data into a data warehouse or data lake. This automation enhances efficiency, reduces errors and accelerates the availability of data for analysis, relying on scheduled executions, event triggers and real-time monitoring to maintain the integrity and quality of the data pipeline.
Get a deeper understand of the ETL process and ETL automation.
