An All-Encompassing Guide for Business Data Warehouse Automation

Enterprises handle vast volumes of data on a daily basis, including transactions, customer interactions, market research, social media, and more. Making sense of this data can be a daunting task, particularly when relying on traditional manual data management methods.

With automation, you can swiftly compile, manage, and analyze your business data, reducing the laborious tasks involved in building and maintaining data warehouses. This not only frees up valuable resources and allows you to focus on strategic tasks but also accelerates the delivery of insights, helping you stay ahead of the curve.

Read our guide to find out how to enable data warehouse automation, learn about data warehouse architecture, and explore tools that can facilitate your automation journey.

What is data warehouse automation?

Data warehouse automation involves the centralization of data from disparate sources and applications into one place to make business intelligence, data visualization, and forecasting easier.

An automated data warehouse relies on comprehensive design and development patterns that help automate the entire lifecycle of data sets, from planning and modeling to integration and deployment.

What are the benefits of data warehouse automation?

Data warehouse automation can help business leaders switch to data-driven decision-making and improve data quality while ensuring faster time-to-value and lower error rates. Below we will take a look at some of the most prominent benefits brought by automated data warehouses.

Improved data quality

The point-and-click interface of data warehouse automation software simplifies the process of extracting diverse data from various sources such as databases, Excel, and delimited files. This allows enterprises to prevent inconsistencies found in manual Extract, Transform, Load (ETL) processes and minimize data errors. As a result, businesses can benefit from enhanced data quality and more accurate business intelligence.

Better decision-making

By reducing the time required for data processing and improving its accuracy, automation can support more informed business decisions.

Furthermore, without having the need to perform repetitive coding tasks associated with data warehouse management, developers can focus on more strategic and game-changing solutions for your enterprise.

Enhanced compliance and data standardization

Data warehouse automation enhances data standardization by ensuring it is consistently formatted and classified. Consistent data standards across the business improve data quality, promote better integration, and facilitate analytics and reporting.

By automating your data warehouse you can also enforce compliance measures at every stage of data management, thereby making sure that all data operations are up to required standards. Furthermore, automation can generate necessary audit trails and documentation that can assist in demonstrating compliance during audits.

Faster time-to-value and lower error rates

Streamlined deployment of data warehouses and faster access to data insights often lead to a quicker time-to-value, enabling you to rapidly respond to changing business conditions. For instance, a supply chain company can use an automated data warehouse to decrease the time needed to identify bottlenecks. This automation allows for faster responses to sudden inventory shortages or surges in demand, facilitating more accurate and timely adjustments in operations.

Furthermore, data warehouse automation can help you minimize the risk of failures or mistakes in repetitive data handling tasks by eliminating the human factor.

Real-time analytics

Automated data warehouses can be effectively used to enhance data analytics solutions. It helps streamline the process of real-time data ingestion, processing, and analysis. This allows businesses to react swiftly to business trends and customer behaviors.

Let’s use eCommerce as an example. With a constant influx of data from online transactions, customer interactions, and supply chain updates, an eCommerce platform can leverage data warehouse automation to analyze this data in real-time. Consequently, if there’s an unexpected surge in demand for a particular product, eCommerce platforms can promptly adjust their inventory and marketing strategies, preventing stock-outs and capitalizing on the increased demand.

Such swift responses allow businesses across all industries to optimize workflows and maintain a competitive advantage.

Data warehouse automation architecture

Automated data warehouse architecture offers a code-free method of aggregating information from multiple sources into a data warehouse and beyond. Developed with agile methodologies in mind, it encompasses several key components, including:

• Data source integration. Connects the data warehouse to various sources such as databases, files, APIs, and applications.
• Data storage. Includes a data lake and data warehouse, serving as centralized repositories for collected data.
• ETL processes. Responsible for cleaning, transforming, and loading data from different sources into the data warehouse.
• Data modeling. Involves automated tools for generating data models and using them effectively.
• Data serving. Delivers the processed data to various business intelligence tools and applications, providing users with valuable insights.
• Data management and monitoring. Ensures data quality, monitors performance, and delivers timely alerts in case of any issues.

Process breakdown

So, here how the overall process works:

1. The system connects to multiple data sources and extracts the necessary data. Pre-configured connectors for integration enable easy data management across a wide range of data providers.

2. The extracted data is then transformed into a suitable format for analysis. The system streamlines repetitive tasks by automating the extraction, transformation, and loading processes, and incorporating features like auto-mapping and job scheduling.

3. The transformed data is loaded into the data warehouse, where users can employ automated tools to create data models that ensure better utilization of data.

4. Finally, the processed data becomes available to different business intelligence tools for analysis and decision-making purposes.

Overall, automated data warehouse architecture significantly reduces the time and resources required for data management, while increasing efficiency and accuracy of the processes. This ultimately leads to more reliable insights and improved decision-making.

Traditional data warehouse architecture vs automated data warehouse architecture

Unlike traditional data warehouse architecture, automated data warehousing greatly simplifies batch execution and ETL code deployment. This enhances efficiency and reduces the need for manual intervention in the data warehousing process.

These improvements lead to significant differences between the two types of data warehouse architecture in terms of implementation time, data quality, and cost, among other factors. In the infographic below, we’ve outlined these key differences.

What are the best data warehouse automation tools?

ActiveBatch, Redwood RunMyJob, Tidal Automation, WhereScape, and Oracle Data Warehouse are some of the popular data warehouse automation tools that are commonly named as market leaders. Let’s take a look at each.

ActiveBatch

ActiveBatch Workload Automation is a comprehensive solution designed to optimize real-time data warehousing and ETL processes. It has event-driven architecture, enabling users to seamlessly manage data and dependencies across various systems. To facilitate the creation of reliable end-to-end workflows, ActiveBatch leverages an integrated Jobs Library packed with pre-built, platform-neutral connectors.

Key features

• Provides full API accessibility for the integration of WSDLs, SOAP Web Services, and RESTful Services
• Enables constraint-based and granular date/time scheduling
• Allows multiple checkpoints within data warehouse processes for seamless restarts
• Includes auditing and governance features for streamlined business rules across teams and departments
• Ensures security with granular permissions, multi-factor authentication, and privileged access management

Redwood RunMyJobs

Redwood RunMyJobs is an ideal solution for businesses seeking to integrate data from multiple sources in line with process dependencies and requirements. This data warehouse automation software ensures efficient scheduling and executing of data warehouse jobs with minimal manual intervention. The automation capabilities lead to significant time and cost savings and improve the accuracy and efficiency of data warehouse operations.

Key features

• Provides simple API wizards for quick integration of REST or SOAP web services
• Enables feeding of data to dashboards, reporting, and BI tools
• Automates exception management to reduce failures
• Supports creation of automated data pools from any application or database
• Can be integrated with CRM, ERP, big data solutions, financial platforms, etc.

Tidal Automation

Tidal Automation is a comprehensive data warehouse automation software that boasts advanced workload automation capability and support for over 60 integrations with modern and legacy solutions. Tidal Automation 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

• Enables clear definition of SLA policies
• Provides critical path tracking
• Offers both time-based and event-based job scheduling
• Provides highly configurable dashboard tailored to IT and business requirements
• Has integrated resource management

WhereScape

WhereScape is best known for its data infrastructure project planning, modeling, and designing capabilities, making it a perfect choice for fast-track projects and design automation. It supports both on-premise and cloud data platforms and offers a variety of products like WhereScape 3D, WhereScape Red, and WhereScape Data Vault Express.

Key features

• Supports a diverse range of data sources and platforms including Microsoft SQL Server, IBM DB2, IBM Netezza, Oracle, Snowflake, Teradata, Hadoop, and Hive
• Enables rapid prototyping for improved collaboration with stakeholders
• Reduces the delivery time, effort, cost, and risks of new data infrastructure projects
• Supports diverse file formats like CSV, JSON, and XML

Oracle Data Warehouse

Oracle Data Warehouse is a cloud-based data warehouse automation software that helps efficiently manage data warehouse complexities. Equipped with self-service data loading, data transformations, business modeling, and automatic insights, the platform automates numerous processes, including provisioning, configuration, security, tuning, scaling, and data backups.

Key features

• Enables the creation of data-driven applications and sophisticated analytical models
• Provides comprehensive data and privacy protection by encrypting data at rest and in motion
• Facilitates data consolidation from various enterprise systems, spreadsheets, and third-party data sources into an integrated data store
• Reduces administration effort by up to 90% and empowers business teams to operate independently from the IT team

How to prepare for data warehouse automation?

Preparing for data warehouse automation involves careful planning, analysis, and consideration of various aspects of your business and data infrastructure.

Here are the steps your organization should take to get ready for this transformation:

Step 1. Evaluate your current data warehouse architecture

You should evaluate how your current data warehouse architecture works. It includes knowing whether it uses unique, specialized items or adheres to a mixture of industry best practices. This will help you better understand what specific changes you need to implement to ensure successful automation.

Step 2. Define business requirements

Before starting data warehouse automation, consider multiple factors, including:

• Compatibility with data sources. Your chosen automation tool should be capable of seamlessly integrating with these sources.
• Scalability. The automation tool must be scalable to handle increasing data volume and complexity over time.
• Security measures. You need to make sure that the chosen tool has all necessary capabilities to protect your data from breaches and unauthorized access.
• Compliance with regulations. If your business operates in a regulated industry, you need to make sure that your chosen data warehouse automation tool complies with applicable laws, such as HIPAA and GDPR.

Step 3. Analyze your existing procedures

Identify and prioritize areas that will benefit most from automation. Think of workflows and procedures that are time-consuming, complex, detailed, and labor-intensive. Also, be aware of any possible bottlenecks that can impede the automation process.

Step 4. Evaluate maintenance processes

Examine how your data infrastructure maintenance is managed. In case the process is time-consuming, challenging, and reliant on a few key individuals, automation could help distribute responsibility and minimize risk.

Step 5. Select a data warehouse automation tool

Align your choice of data warehouse automation tools with the existing architecture of your business. Your task is to evaluate the capabilities of various solutions on the market and see how well they fit in with your requirements.

Closing thoughts

Data warehouse automation allows businesses to significantly optimize their operations and gain a competitive edge. By eliminating manual tasks, improving data quality, and increasing efficiency, it paves the way for organizations to leverage their data to its fullest and make informed, strategic decisions.

Ready to embark on your data warehouse automation journey? Our big data consultants are here to help you find the right tools that align with your unique business needs and integrate them seamlessly into your processes.

Contact us today and take the first step towards unlocking your data’s true potential.