2 min read
Microsoft Azure SQL DW Gen2 is recognized in independent tests as one of the best, if not the best Massive Parallel Processing database in the cloud. This was independently verified in benchmarking tests between Azure SQL DW, Google BigQuery, Amazon Redshift and Snowflake by Gigom.com (https://gigaom.com/report/data-warehouse-cloud-benchmark/). Gigom.com tested all aspects of the platforms including single and multi-table query times and the crucial price per performance test. So, I think it is safe to say that Azure SQL DW Gen2 is #simplyunmatched when it comes to the raw data processing power that is often required in modern cloud scale analytics environments.
However, only testing the raw processing power is like only testing a car in a straight-line speed test. Realistically, that is just not the way we drive. We go around curves and bends in the road, there are bumps, there are dips and there are inclines that all affect how a vehicle performs. In much the same way, just looking at Azure SQL DW Gen2 to meet your cloud scale analytics requirements does not give you the full picture. You need to understand how data is ingested, where it is stored and how it is stored at scale, how is it processed from raw data into meaningful data models that business can use and understand. In addition, the need for governance means you also need to understand where data originates, who has access to it throughout the data lifecycle and what gets done to the data during its lifecycle, all while juggling the compliance.
In many cloud scale analytics environments, the areas of ingestion, storage, modelling & serving the data as well as compliance are handled in isolation. That means that from a technical perspective, while working with data, all the way through to business perspective where users are analyzing the insights from the data and driving compliance and governance, there is no golden thread running through the solution. Often, you end up with a patchwork of tools used to build ingestion pipelines, other tools used to explore and model the data and a totally different set of tools used to process data from the store into the MPP data platform. This is an extremely common problem, as an extremely diverse set of hard to come by skills are required for building a cloud scale analytics solution.
We think that using TimeXtender to build a cloud scale analytics solution centered around the extreme scalability and unmatched performance of Azure SQL DW is a natural solution to the problem. TimeXtender brings together all these elements into a single, cohesive and easy to use platform that uses all the best Azure Data Services to build and manage your cloud scale analytics environment. It manages ingestion of data by giving you the ability to effortlessly connect to 100’s of structured and semi-structured enterprise data sources, it builds and deploys data pipelines to move the data and store it in an Azure Data Lake Storage Gen2 environment, it dynamically manages the flow into and out of the Azure Data Lake to ensure you don’t have a stagnant pool of data, it models and builds data models in Azure SQL DW and builds business friendly semantic models in Azure Analysis Services. All of this is done while maintaining the integrity of the data from start to finish - through data lineage - to create that proverbial golden thread through the entire solution as well as documenting every step of the way through system generated documents that support your governance and compliance journey.
Together, the thought that has gone into building TimeXtender, coupled with the processing power and structure of Azure SQL DW Gen2, is a match that cannot be beaten by any other combination of MPP platform and dataops or data management platform.
