Data Warehousing and Big Data
An Enterprise Data Warehouse is the traditional foundation for Business Intelligence. Business users across the enterprise can access the data in a way that is easy for them to understand and use. The Ralph Kimball approach of a star schema assures this.
The design of a robust data model based on:
- business functions and processes (conformed in an Enterprise Business Matrix), and
- the development of effective ETL (Extraction, Transformation & Loading processes)
are keys to success. For most businesses - agile B.I. means bypassing the rigour of the Bill Inmon approach to a normalised data warehouse with data mart satellites in favour of fast deliverables and business insights.
Big data has disrupted the traditional with a massive approach - capture all the data and analyse all the data in parallel. New open source tools and techniques like the Hadoop stack, object storage, and commodity hardware / cloud facilities have driven the physical and licence cost of data analytics off a cliff. However the design of a robust data model is still a challenge:
- ELT (Extraction, Load, then Transform) shifts the focus to massive, streaming collection of all structured and unstructured data for loading into a data lake, and
- Dan Linstedt advocates the use of his Data Vault 2.0 schema to maintain an auditable history.
Most practitioners never get to work on greenfield projects and to evaluate the alternative approaches, and are more familiar with the bad experiences of working with inelegant or expensive solutions. Texuna's extensive experience of building and engineering the right solution for any given situation gives us deep, unbiased expertise that can help you.
What we do?
Texuna have a rich history when it comes to data warehousing dating back to the company’s foundation in 2000. Since then, we have worked with numerous major clients across the UK and Ireland. Our primary area of expertise is the delivery of data warehouses and management applications for non-profit (Education) and corporate sectors. As a vendor neutral systems integrator, we continually search for the “best of breed”, offering value for money so you “achieve more for less” with a dedicated solution precisely tailored to your needs.
Our history shows our commitment to the big data, warehousing and Business Intelligence space while ensuring solutions are low risk, fit for purpose and reuse existing tools and expertise.
Our agile methods and cloud-linked data pipeline design have been deployed and thoroughly tested on an industrial scale with huge amounts of sensitive data in secure environments.
Data Warehousing and OLAP
Patrick Lynch, CEO and Founder of Texuna
What we offer and why we are different
We place business needs and benefits at the heart of any data warehouse:
Business benefits:
The foundations for all of our solutions are business drivers, governance and data quality improvements. This promotes the long term success of a warehouse project, and allows for the delivery of an early and valuable Minimum Viable Product (MVP) with Minimum Viable Quality (MVQ) of data.
Agile methods and user engagement ensures projects "keep it real" with continually meaningful increments to a warehouse over the life of the project.
Financial benefits:
Huge savings are created through infrastructure as software, just-in-time resources, and the elimination of capital expenditure in favour of operating expenses.
We work with customers to standardise their data pipelines and cleanse sources. This in turn drastically reduces troubleshooting time, while also speeding up productivity.
Self-service facilities reduce the time to value for users, with a conformed warehouse providing a single accurate version of the truth, eliminating any debates over the meaning or location of data.
Technical benefits:
Cloud flexibility and Texuna metadata driven frameworks give simple support to changing business needs and changes in data pipelines.
Software as a Service (SaaS) infrastructure provides ease of configuration, maintenance and scalability for changing demands.
