The Latest

28 April 2021:  AWS has introduced AQUA (Advanced Query Accelerator) for Amazon Redshift, a distributed and hardware-accelerated cache that, according to AWS, “delivers up to ten times better query performance than other enterprise Cloud data warehouses”.

Why it’s Important

AWS is not the only vendor that offers distributed analytics computing. Architectures from Domo and Snowflake both make use of elastic, distributed computing resources (often referred to as nodes) to enable analytics over massive data sets. These architectures not only speed up the analytics of data, but also provide massively parallel ingestion of data. 

By introducing AQUA, AWS has added a layer of specialised, massively parallel and scalable cache over its Redshift analytics platform. This new layer comes at a cost, but initial calculations suggest it is a fraction of the cost of deploying and maintaining traditional big data analytics architecture, such as specialised BI hyperconverged appliances and databases.

Given the rapid growth in self-service data analytics (aka citizen analytics) organisations will face increasing demands to provide analytics services for increasing amounts of both highly curated data, and ‘other’ data with varied levels of quality. In addition, organisations need to consider a plan for rise in non-structured data. 

As with email, we have reached a tipping point in the demands of performance, complexity and cost where Cloud delivered analytics outstrip on-premises in most scenarios. The question now becomes one of Cloud architecture, data governance and, most important of all, how to mature data literacy across your organisation.

Who’s impacted

  • Business intelligence / analytics team leads
  • Enterprise architects
  • Cloud architects

What’s Next?

Organisations should reflect honestly on the way they are currently supporting business intelligence capabilities, and develop scenarios for Cloud-based analytics services. 

This should include a re-evaluation of how adherence to compliance and regulations can be met with Cloud services, how data could be democratised, and the potential impact on the organisation. BAU cost should be considered, not just for the as-in state, but also for a potential future states. While savings are likely, such should not be the overriding factor: new capabilities and enabling self-service analytics are just as important. 

Organisations should also evaluate data literacy maturity among staff, and if needed (likely) put in place a program to improve staff’s use of data.

Related IBRS Advisory

  1. IBRSiQ: AIS and Power BI Initiatives
  2. Workforce transformation: The four operating models of business intelligence
  3. Staff need data literacy – Here’s how to help them get it
  4. The critical link between data literacy and customer experience
  5. VENDORiQ: Fujitsu Buys into Australian Big Data with Versor Acquisition