‘Turbo charging Amazon Redshift’s columnar efficiencies 

Amazon Redshift is the data warehousing service that forms a key part of the overall AWS (Amazon Web Services) cloud solution. Redshift enables organisations, both large and small, to store a variety of different kinds of data in a central cloud-based ‘warehouse’ and have it ready for querying.

Speed of thought Analytics next level GPU

In order to get the full potential and flexibility of Amazon Redshift, “speed of thought’ processing, with the world’s fastest GPU data processing engine. This enables analytics and BI visualisation tools, such as Tableau, Power BI and TIBCO Spotfire, to be pushed to their limits, without the need for costly, resource-hungry and time-consuming pre-loading & pre-aggregating of datasets. Data pre-aggregation projects in themselves can take many months of specification, planning and development, thereby consuming valuable resource, and potentially narrowing the data being made available to end-users and consumers of such projects.

With the demands that are being put on analytics today, by the sheer growth of datasets and the multitude data points available to businesses, ever more efficiencies are required from data warehouses to meet the demands that are being put upon it and maximise the insights and competitive advantages that can be drawn out.

Redshift has commonality with relational databases as it stores its data in a structured way, however, a key difference that Redshift has versus conventional databases is that it uses a column-orientated structure as opposed to row-orientated. Storing database tables in this columnar structure drastically reduces the overall requirements on disk Input/Output, thereby reducing the amounts of data that are being loaded from the disk. This is a key factor in enabling Redshift to optimise analytics performance.

In essence, the column-orientated structure means that each data block stores values of a single column for multiple rows of data. As data is loaded onto the Redshift data warehouse, it is automatically converted to the columnar structure.

This structure enables Redshift to use a compression scheme which is optimised for each particular data type, providing disk space savings and efficiencies. These space savings, in turn, enable more data to be loaded directly into memory as analytic work is being carried out. This drastically reduces the requirements on disk Input/Output.

Generate smarter intelligence, faster.

The unique GPU patent pending IP technology used by Brytlyt has been developed to be at the very forefront of the transformational analytics revolution. The next generation platform with ‘speed of thought’ analytics is built for ambitious businesses who want to harness the power of their rapidly growing datasets of today and for tomorrow.

We understand the importance of data-driven business and can help you achieve it, to find patterns and insight across huge amounts of data, in an instant.

Author Bio:

Tech & Finance blogger and digital agency consultant – Dave has worked in digital 10 years in client-side, agency-side, and self-employed capacities.
He now writes engaging content and creates innovative digital strategies for the finance and tech industries. He is the creator of Enviroute – A new travel app to check the Severn bridge status – that is currently seeking investment.
Dave spends more time than he cares to admit watching skateboarding videos and likes to express himself through the medium of internet memes!

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