Data warehouse automation: Unlock speed, efficiency and visibility

Workload automation solutions have the power and flexibility to optimize any data warehouse operation, regardless of complexity.

Data centers are exceedingly busy, and managing data warehouse operations poses significant challenges. With the complexity of data sources, the importance of maintaining data quality, the need for efficient data modeling and the demand to consistently execute SQL queries, IT teams often struggle with time-consuming tasks. 

Add the pressure to keep up with trends in real-time data, enterprise-wide data integration and streamlined decision-making processes, and the strain on traditional data teams is bigger than ever.

Intentional workload automation offers a solution to minimize reliance on manual coding and enhance adaptability.

What is data warehouse automation?

Data warehouse automation (DWA) refers to the use of intelligent software solutions to streamline the creation, deployment and management of data warehouse systems. Traditional data warehouses rely heavily on manual coding for tasks such as data integration, modeling and querying. DWA eliminates much of this manual effort by automating these repetitive tasks, making data warehousing faster, more efficient and scalable.

Benefits of data warehouse automation

Today’s intelligent automation solutions have the power and flexibility to juggle the needs of virtually any traditional data warehouse operation, regardless of complexity. Tested logic and drag-and-drop convenience streamline data processes, allowing your team to design, build, implement and monitor workflows faster, more reliably and with less custom scripting, increasing time-to-value.

Better decision-making

Automating data warehousing ensures decision-makers have consistent access to up-to-date and accurate data. With faster data processing and delivery, business leaders can rely on insights that reflect the most current trends and metrics. This improved data availability reduces the time-to-insight, enabling timely, data-driven decisions that can boost competitive advantage.

Fewer errors

Manual processes are prone to human error — in coding, data transformation or query execution. DWA eliminates these risks by standardizing workflows and automating complex tasks. By reducing manual intervention, organizations can improve the reliability of their data systems and prevent costly errors that might disrupt operations or decision-making.

Improved data quality and standardization

Data warehouse automation tools often come with built-in data quality frameworks that ensure data is accurate, consistent and formatted according to organizational standards. Automated processes can identify and correct issues such as missing values, duplicate records or inconsistencies, ensuring that all data entering the warehouse meets stringent quality requirements.

Real-time insights

Modern DWA tools are designed to handle real-time data ingestion and processing, enabling organizations to derive insights as events occur. This is especially critical for industries like retail, finance and healthcare, where timely data can influence customer interactions, market strategies or operational efficiencies.

The essentials of data warehouse architecture

Understanding the foundational components of data warehouse architecture and how it has changed is crucial to leveraging automation effectively. A well-designed architecture ensures data flows seamlessly from sources to insights.

Components of data warehouse architecture

1. Data source integration connects the data warehouse to various sources such as databases, files, APIs and applications.
2. Data storage includes a data lake and data warehouse, serving as centralized repositories for collected data.
3. ETL processes are responsible for cleansing, transforming and loading data from different sources into the data warehouse.
4. Data modeling involves automated tools for generating data models and using them effectively.
5. Data serving delivers the processed data to various business intelligence tools and applications, providing users with valuable insights.
6. Data management and monitoring ensures data quality, monitors performance and delivers timely alerts in case of any issues.

Traditional data warehouse architecture vs. automated data warehouse architecture

Use cases for automation in the data warehouse lifecycle

Let’s look at some areas where workload automation can have a positive impact on enterprise data warehouse operations.

Optimizing ETL processes with a single job scheduler

According to Gartner, on average, most IT organizations have 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 remedies this issue by seamlessly accommodating the diverse array of data sources, applications and environments, including on-premises, cloud-based and virtual setups.

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 app 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.

Decreasing dependence on custom scripts

The pre-built job steps and templates in some workload automation solutions can significantly reduce coding time — by as much as half in many cases. These convenient features make automation development accessible for business users and targeting specific business needs.

“[Our batch processes] are dynamic, constantly changing,” noted Senior Director of 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 to just four or five.

Speeding up workflow design and reducing 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 a built-in 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.

Building uninterrupted workflows with 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 implementing event-based triggers, the center eliminated downtime between jobs. It allows the hospital to execute and manage multi-job workflows based on IT events such as a file constraint or a file being received, to ensure that jobs don’t run until feeder systems are fully updated.

Enhancing data platforms with 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 pre-built job steps for Informatica Cloud and PowerCenter, MacDonald implemented a four-phase program that now runs substantially more complex workflows that involve three to four times the volume of data (as many 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

Choose the right data warehouse automation software

Before you choose a tool for DWA, you’ll need a clear understanding of your organization’s data needs, scalability goals and existing infrastructure.

Look to evaluate a platform’s:

• Ability to handle increasing data volumes
• Data governance, compliance and security features
• Integration capabilities
• Interface user-friendliness
• Total cost of ownership (TCO)

Use the overview of three top DWA solutions below to begin your search.

ActiveBatch by Redwood

ActiveBatch optimizes real-time data warehousing and ETL processes. With event-driven architecture, it enables users to seamlessly manage data and dependencies across various systems. To facilitate the creation of reliable data workflows, ActiveBatch leverages an integrated Jobs Library packed with pre-built, platform-neutral connectors.

Key features

• Constraint-based and granular date/time scheduling
• Full API accessibility for the integration of WSDLs, SOAP web services and RESTful services

• Granular permissions, multi-factor authentication and privileged access management
• Integrations for Informatica Cloud, SAP Business Warehouse, Teradata and more
• Pre-built job steps for Hadoop, data visualization tools, cloud data warehouses and more

RunMyJobs by Redwood

RunMyJobs is an ideal solution for businesses seeking to integrate data from multiple sources in line with process dependencies and requirements, driving efficient scheduling and executing of data warehouse jobs with minimal manual intervention. Its robust automation capabilities generate significant time and cost savings.

Key features

• Ability to feed data to dashboards, reporting and BI tools
• Automated data pool creation — from any application or database
• Integrations for CRM, ERP, big data solutions, financial platforms and more
• Simple API wizards for quick integration of REST or SOAP web services
• Technical and process exception management

Learn more about RunMyJobs.

Tidal by Redwood

Tidal is a comprehensive data warehouse automation software with advanced workload automation capability and support for over 60 integrations with modern and legacy solutions. Tidal reduces the need for custom scripting, scheduling silos and manual processes. It’s an ideal solution for enterprises aiming to automate processes across all layers.

Key features

• Clear definition of SLA policies
• Critical path tracking
• Highly configurable dashboard tailored to IT and business requirements
• Integrated resource management
• Time-based and event-based job scheduling

Learn more about Tidal.

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