Data warehouse automation (DWA) is fast replacing conventional approaches to building data warehouses.

Enterprise data warehouses (EDW) are critical for utilizing historical data for Business Intelligence and reporting. But, the traditional approaches to compiling and managing colossal data volumes through manual ETL are no longer effective. In today’s competitive enterprise market, business agility and time-to-market are crucial. For such requirements, data warehouse automation software stand out in minimizing the manual efforts involved in building and deploying data warehouses and synthesizing data for business reporting.

This detailed guide will explore various aspects of data warehouse automation and how it helps simplify business processes.

What is an Enterprise Data Warehouse?

An enterprise data warehouse helps centralize a business’s data from various sources and applications. As a result, it makes data easily accessible for business intelligence, visualization, and forecasting. The function of an EDW is to consolidate data from multiple departments across the organization into a centralized location.

What is Data Warehouse Automation?

A modern data warehouse uses next-generation technology for automation. It relies on advanced design patterns and processes to automate the planning, modeling, and integration steps of the entire lifecycle of data sets in industries. It provides an efficient alternative to traditional data warehouse design by reducing time-intensive tasks, such as generating and deploying ETL codes to a database server.

Using data warehouse design tools, businesses can execute business intelligence projects within hours compared to months at a fraction of the cost of manual programming.

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How has DWA Evolved?

The functionalities found in data warehouse automation tools have evolved over several decades. This progression is due to the growth in data storage and integration requirements. Another reason is the spread of many data sources, such as CRM systems, REST APIs, and cloud data warehouses and databases.

Here is a brief overview of the evolution of data warehouse repositories.

The various levels of data warehouse automation explained

Database Management Systems and Data Warehouse Architectures

Before traditional data warehouses, the invention of disk storage in the 1960s spurred the need for storing and processing large amounts of data in databases. These requirements enabled the development of dimensional data marts and entity relationships. By the early 1980s, several vendor-specific ETL tools and SQL-powered relational database management systems (DBMS) were available on the market.

Standardization of Data Warehouse Architectures

By the next decade, the business requirements for managing heterogeneous business data evolved substantially. Data warehouse technology converged on standardized architectures, which enabled businesses to weave together data from multiple formats and sources for a consolidated view.

EDW Challenges and Need for Data Automation

The inherent data warehouse development challenges, such as long development cycles, poor metadata management within the existing data warehouse, and costly development resources, made traditional data warehouse architectures unsuitable for a dynamic market.

By the turn of the millennium, businesses found that many of their systems had been poorly integrated with databases and application systems and were unable to integrate volumes of fragmented data. This paved the way for an agile platform that can automate ETL processes and easily integrate with enterprise applications.

Today, data warehouse automation tools have evolved to account for new technology and business requirements. These include real-time data extraction, analysis of cloud data and web application services, such as REST APIs and SOAP, and integration with data visualization tools.

How Does Data Warehouse Automation (DWA) Work?

Understanding the working of automation tools requires first looking at how traditional data warehouses operate data.

Traditional Data Warehouse Architecture

In a conventional data warehouse design, all data goes through three distinct stages:

1. Relational Database (OLTP):  In this stage, users employ SQL scripts to extract all transactional data from relational databases. Before moving the data, it is cleansed to verify erroneous and inaccurate information for consistency.
2. Analytical Data Warehouse (OLAP): The transactional data is then modeled on star or snowflake schemas and transferred onto an online analytical processing server or OLAP through a relational OLAP or multidimensional data model. Next, the data is transformed and loaded into the data warehouse.
3. Analytics and Reporting: Once the ETL processes are complete, the data from the data warehouse is exported to BI and analytics tools to derive insights for decision-making.

Usually, a user must define the ETL processes from scratch to move the data from the data warehouse to front-end BI tools.

The manual coding for ETL and data cleaning tasks also makes data warehousing projects error-prone and time-consuming. Because of this, business users often have insufficient accurate data available for reporting and face higher risks of budget overruns and project failures.

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Automated Data Warehouse Architecture

A data warehouse automation software offers a code-free and fluid approach to aggregating disparate enterprise data from source systems to a data warehouse and beyond. Unlike in traditional data warehouse architecture, the software automates batch execution and ETL code deployment requirements of the data warehousing process. Built on agile methodologies, some of the most prominent data warehouse automation ideas utilize a variety of functionalities, including:

• De-normalized, normalized, and multi-dimensional data structures
• ETL and ELT data integration processes
• Source data modeling
• Connectivity to various data providers

Automated Data Warehousing Journey

A data warehouse software facilitates automation and simplifies data warehouse projects in the following ways:

• Automated ETL processes: Streamline extraction, transformation, and data load automation processes to eliminate the repetitive steps through auto-mapping and job scheduling. You can do these through two data warehouse loading strategies, full load, and incremental load.
• Clean and intuitive user interface: Design and implement data warehouses using the drag-and-drop visual UI.
• Pre-configured connectors for seamless application integration: Support integration with several enterprise application connectors, such as Salesforce, COBOL, MS Dynamics CRM, SAP, and REST APIs to manage data across a broad range of data providers.

In short, data warehouse automation software help companies create and manage data warehouses more smoothly compared to traditional data warehouse design tools. Although the capabilities of these solutions vary, enterprises can expect common design patterns and functionalities to meet their business objectives. It is always better to go for a data warehouse cost comparison of various tools before you decide on the right data warehouse automation tool.

Evaluating The Readiness For Automation

Below are some of the steps an organization needs to assess before opting for a data warehouse tool:

• Data Management Architecture: How does your DWH architecture work? Does it use unique, specialized items or a mixture of best practices?
• Requirement: How are you defining business requirements? Is it through an agile discovery process or a waterfall approach focused on business, functional and technical requirements?
• Operations: How are your organization’s procedures structured? Are the operations time-consuming, fragile, complex, detailed, or labor-intensive?
• Maintenance: How is the maintenance of data infrastructure structured? Is it difficult and reliant on a few key individuals?

• Volatility: How often do you undergo frequent changes to requirements and the overall development process?
• Testing: How do your business stakeholders expect delivery of analysis and data access? Is it fast and frequent?

Benefits of Data Warehouse Automation Tools

Automated software allows enterprises to secure a market edge with the following benefits:

1. Improved data quality and precision: Businesses can avoid the inconsistencies found in manual ETL and improve data quality. The data warehouse automation software’s point-and-click interface makes it easier to extract disparate data from databases, Excel, delimited files, and other sources. It also allows users to model slowly changing dimensions and migrate data to other destination systems, such as cloud-based BI or data visualization tools. Hence, businesses not only have access to reliable data but also more accurate reporting and analysis.
2. Increased agility and faster time-to-value: Quicker deployment of data warehouses and access to data insights equip companies with improved business agility. This enables businesses to respond quickly to ever-changing market conditions, such as unexpected changes in demand and loss of disposable income. For example, a retailer using automated data warehousing software can shorten the time taken to leverage BI reports and determine the causes of low sales across different outlets and counter accordingly. In short, decisions can be made sooner and better reflect market changes through better impact analysis.
3. Higher data warehouse project throughput and ROI: The lack of manual input in data warehouse automation software allows users to build and deploy data warehouses much more quickly, freeing up developer resources and lowering costs in the process. This enables business teams more time to uncover intelligible information, pursue strategic decisions, and ensure higher project value.

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Astera Data Warehouse Builder – An Automated Tool

Astera DW Builder is an automated, end-to-end data warehousing solution. It allows users to design, develop and deploy their own data warehouse without writing a single line of code. The solution features a robust data model designer that supports subsequent data processes, such as data mapping, and fact and dimension data population. In all, the Astera DW Builder is designed to enhance ROI, save time, and improve business intelligence, data security, and data quality capabilities.

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