Data Warehousing in the age of Big Data

Data warehousing in the age of Big Data has gone through various paradigm shifts and architectural updates. However, the central value proposition of the data warehouse remains. Deliver clean and integrated data for better decision making to the enterprise. In our view, the data warehouse is the central foundation for a successful enterprise data strategy.

"The data warehouse is here to stay. We will always need clean and integrated data. "

The technologies that underpin the Enterprise Data Warehouse may have changed. This is a good thing. We now have more options. For certain use cases a traditional relational database (MPP) is the best choice. In other cases Big Data technologies such as Hadoop fit the bill. In most cases both technologies complement each other.

"Big Data technologies such as Hadoop and Spark can complement the traditional Enterprise Data Warehouse architecture in certain cases."

Why Sonra?

Sonra has years of experience in traditional data warehousing AND the new world of Big Data. We have a unique mix of skills that best positions us in Ireland to help you reach your data warehouse goals.

You have to consider many things when rolling out a data warehouse to your enterprise. We have listed some of them below. Sonra can answer any of those question to implement and run a super successful data warehouse initiative.

We are here to help you liberate your data from silos and complex data structures.

"We chose Sonra for the high caliber and unrivaled technical expertise of its consultans in data warehousing and Oracle Data Integrator. Sonra has enabled us to greatly increase our knowledge and implementation of Oracle Data Integrator."

Diarmaid O'Reilly,
Enterprise Data Warehouse Manager at ICON

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Important Data Warehouse Considerations

Below are a few items you have to take into account when implementing a data warehouse:

  • What skills and resources do I need? Where do I find the right skillset?
  • How should I structure my team? What roles do I need?
  • What works best? In-house resources or a third party?
  • How do I get buy-in from my business? What is the value proposition? How do I save or make money?
  • Should I build my data warehouse in the cloud? What are the benefits?
  • How do I ensure data governance and management?
  • Can I do advanced analytics without a data warehouse?
  • What should my DevOps strategy be?
  • What technologies should I use? A relational database or one of the Big Data technologies (Spark, Hadoop) or a combination of both?
  • Should I build a full Enterprise Data Warehouse or does my use case lend itself to implementing data marts?
  • How do I architect a data warehouse to meet real-time requirements?
  • Do I need an enterprise data model and a modelling tool?
  • What change data capture strategy should I implement and how do I best audit changes to my data?
  • How does my data warehouse interact with other elements of my data architecture, e.g. a Master Data Management solution or a data science platform.
  • What style of architecture is better suited to my needs? Inmon or Kimball? Is this still relevant in the Big Data age?
  • How can my data warehouse support my advanced analytics and data science requirements?
  • What is the best practice around data lineage?

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