Discover how-to unlock exciting new...
Big Data Analytics WhereScape RED
Big Data and Advanced Analytics using WhereScape RED
We at WhereScape have been making significant investments recently integrating our automation software WhereScape RED with big data platforms. There has been a lot of interest from customers who recognise the value that automation can bring to these powerful yet complex solutions.
We have recently implemented a financial forecasting solution at an enterprise customer to prove the value of big data technology within the organisation. The solution developed in partnership between WhereScape and the customer delivered multiple benefits:
- Delivered a high value forecasting model to the business
- Used big data technology to deliver a model where traditional relational technology failed
- Proved the value of big data technology within the organisation
- Highlighted the potential of big data technologies to solve new and interesting problems
Problem
The customer needed to generate an accurate set of key performance indicators each month. The process required large daily volumes (approx. 20 million rows per day) of customer and detailed product revenue data. Because of storage and processing limitations, data was only available for the current month which impacted the forecast accuracy.
Solution
To solve the storage and processing problem the customer decided to implement a Cloudera Hadoop big data platform to store full historical datasets, along with the WhereScape RED for Big Data Adaptor to enable data lake automation. The completed solution delivered the following functionality:
- An automated process to extract large volumes of daily transactions from source each day and save them to the Cloudera platform using Hive
- Based on the extracted data, a forecast model was built in Hive and SQL Server. This model was built using WhereScape RED via rapid, iterative development cycle
- Once the forecast numbers were prepared, an automated process saved the forecast as an incremental snapshot in Hive and refreshed Cloudera Impala for interactive querying via Tableau
Some of the key features / benefits of using WhereScape RED for Big Data are:
- Ability to transfer large amounts of data from source system in to Hive seamlessly
- Common metadata across the Extended Data Warehouse environment (Hive, SQL etc)
- Consistent tools for developers
- Easily generate DDL and ELT SQL for Hive with data movement using Sqoop
- Centralised audit and error logging
- Integrated documentation across the full environment (i.e. SQL Server and Hive)
- Automated and integrated scheduling and workflow engine across the full environment
Solution Overview
Outcome
The project sponsors (both Business and IT) were impressed with how easily and quickly WhereScape RED could deliver a solution to solve their big data problem. Now that data is easily accessible for several months, business stakeholders are excited about their ability to easily generate accurate forecasts in less than 60 minutes as opposed to several days. Financial analysts can also query the big data platform directly via Tableau to rapidly gain insight, without the need to wait for data to be transferred to a relational data warehouse and enterprise reporting suite.
IT have learned that the WhereScape Big Data Adaptor and Cloudera Big Data platform can be used to solve complex and valuable business problems. It was also proved that big data technologies from WhereScape and Cloudera are functional and robust, and means that that projects previously deemed impossible can be looked at again.
Technologies Used
To build this solution, following technologies were used:
- Oracle
- SQL Server
- WhereScape RED
- WhereScape RED Big Data Adaptor
- Cloudera Big Data Platform
- Hive
- Sqoop
- Impala
About WhereScape RED for Big Data
WhereScape RED is data warehouse and big data automation software for building, deploying and renovating your analytic solutions whatever the size of your data. WhereScape RED sets the standard for delivery speed using familiar industry standards, frameworks and best practices to dramatically accelerate time to value.
WhereScape RED customers are able to fully manage their Apache Hive™ big data environments through the WhereScape RED data automation platform. This centralises development of the entire decision support infrastructure into one integrated platform and toolset.
There is no need to license separate ETL, data integration, or data modelling tools because WhereScape RED supports industry standard SQL. Customers can leverage their existing resources and training rather than having to rely on tool or platform-specific expertise.
ETL vs ELT: What are the Differences?
In working with hundreds of data teams through WhereScape’s automation platform, we’ve seen this debate evolve as businesses modernize their infrastructure. Each method, ETL vs ELT, offers a unique pathway for transferring raw data into a warehouse, where it can be...
Dimensional Modeling for Machine Learning
Kimball’s dimensional modeling continues to play a critical role in machine learning and data science outcomes, as outlined in the Kimball Group’s 10 Essential Rules of Dimensional Modeling, a framework still widely applied in modern data workflows. In a recent...
Automating Data Vault in Databricks | WhereScape Recap
Automating Data Vault in Databricks can reduce time-to-value by up to 70%—and that’s why we hosted a recent WhereScape webinar to show exactly how. At WhereScape, modern data teams shouldn't have to choose between agility and governance. That's why we hosted a live...
WhereScape Recap: Highlights From Big Data & AI World London 2025
Big Data & AI World London 2025 brought together thousands of data and AI professionals at ExCeL London—and WhereScape was right in the middle of the action. With automation taking center stage across the industry, it was no surprise that our booth and sessions...
Why WhereScape is the Leading Solution for Healthcare Data Automation
Optimizing Healthcare Data Management with Automation Healthcare organizations manage vast amounts of medical data across EHR systems, billing platforms, clinical research, and operational analytics. However, healthcare data integration remains a challenge due to...
WhereScape Q&A: Your Top Questions Answered on Data Vault and Databricks
During our latest WhereScape webinar, attendees had fantastic questions about Data Vault 2.0, Databricks, and metadata automation. We’ve compiled the best questions and answers to help you understand how WhereScape streamlines data modeling, automation, and...
What is Data Fabric? A Smarter Way for Data Management
As of 2023, the global data fabric market was valued at $2.29 billion and is projected to grow to $12.91 billion by 2032, reflecting the critical role and rapid adoption of data fabric solutions in modern data management. The integration of data fabric solutions...
Want Better AI Data Management? Data Automation is the Answer
Understanding the AI Landscape Imagine losing 6% of your annual revenue—simply due to poor data quality. A recent survey found that underperforming AI models, built using low-quality or inaccurate data, cost companies an average of $406 million annually. Artificial...
RED 10: The ‘Git Friendly’ Revolution for CI/CD in Data Warehousing
For years, WhereScape RED has been the engine that powers rapidly built and high performance data warehouses. And while RED 10 has quietly empowered organizations since its launch in 2023, our latest 10.4 release is a game changer. We have dubbed this landmark update...
The Assembly Line for Your Data: How Automation Transforms Data Projects
Imagine an old-fashioned assembly line. Workers pass components down the line, each adding their own piece. It’s repetitive, prone to errors, and can grind to a halt if one person falls behind. Now, picture the modern version—robots assembling products with speed,...
Related Content

ETL vs ELT: What are the Differences?
In working with hundreds of data teams through WhereScape’s automation platform, we’ve seen this debate evolve as businesses modernize their infrastructure. Each method, ETL vs ELT, offers a unique pathway for transferring raw data into a warehouse, where it can be...

Dimensional Modeling for Machine Learning
Kimball’s dimensional modeling continues to play a critical role in machine learning and data science outcomes, as outlined in the Kimball Group’s 10 Essential Rules of Dimensional Modeling, a framework still widely applied in modern data workflows. In a recent...

Automating Data Vault in Databricks | WhereScape Recap
Automating Data Vault in Databricks can reduce time-to-value by up to 70%—and that’s why we hosted a recent WhereScape webinar to show exactly how. At WhereScape, modern data teams shouldn't have to choose between agility and governance. That's why we hosted a live...

WhereScape Recap: Highlights From Big Data & AI World London 2025
Big Data & AI World London 2025 brought together thousands of data and AI professionals at ExCeL London—and WhereScape was right in the middle of the action. With automation taking center stage across the industry, it was no surprise that our booth and sessions...