Today we continue reviewing U-SQL.It unifies the benefits of SQL with the expressive power of your own code. This is said to work very well with all kind of data stores – file, object and relational. U-SQL works on the Azure ecosystem which involves the Azure data lake storage as the foundation and the analytics layer over it. The benefit of the Azure storage is that it spans several kinds of data formats and stores.
U-SQL supports big data analytics which generally have the characteristics that they require processing of any kind of data, allow use of custom algorithms, and scale to any size and be efficient.
This lets queries to be written for a variety of big data analytics. In addition, it supports SQL for Big Data which allows querying over structured data Also it enables scaling and parallelization.
U-sql has declarative SQL and can execute local and remote queries.
If we look at the pattern of separating query from data source, we quickly see it's no longer just a consolidate of data sources. It is also pushing down the query to the data sources and thus can act as a translator. Projections, filters and joins can now take place where the data resides. This was a design decision that came from the need to support heterogeneous data sources. Moreover, it gives a consistent unified view of the data to the user.
The improvements in query language include user-defined extractors, user-defined output, user-defined processors, user-defined appliers, user-defined combined and user-defined reducers. It is scaled out with explicit U-Sal syntax such as those that include extract, output, cross apply, process, combine and reduce. These show the additions over T-SQL.
UDO'sDUO's can be written in any .Net language and registered as assemblies.
My take on query improvements : https://1drv.ms/w/s!Ashlm-Nw-wnWsFqBcG-mBhjPLbC8
#codingexercise
Given a room and a robot that can move in one of four directions, find the size of the room.
find start by walking left towards the wall
from the start walk the boundary of the room
noting the four corners
use it to calculate width and length
start at top left
we have a position on the wall, now we run along the boundary
U-SQL supports big data analytics which generally have the characteristics that they require processing of any kind of data, allow use of custom algorithms, and scale to any size and be efficient.
This lets queries to be written for a variety of big data analytics. In addition, it supports SQL for Big Data which allows querying over structured data Also it enables scaling and parallelization.
U-sql has declarative SQL and can execute local and remote queries.
If we look at the pattern of separating query from data source, we quickly see it's no longer just a consolidate of data sources. It is also pushing down the query to the data sources and thus can act as a translator. Projections, filters and joins can now take place where the data resides. This was a design decision that came from the need to support heterogeneous data sources. Moreover, it gives a consistent unified view of the data to the user.
The improvements in query language include user-defined extractors, user-defined output, user-defined processors, user-defined appliers, user-defined combined and user-defined reducers. It is scaled out with explicit U-Sal syntax such as those that include extract, output, cross apply, process, combine and reduce. These show the additions over T-SQL.
UDO'sDUO's can be written in any .Net language and registered as assemblies.
My take on query improvements : https://1drv.ms/w/s!Ashlm-Nw-wnWsFqBcG-mBhjPLbC8
#codingexercise
Given a room and a robot that can move in one of four directions, find the size of the room.
find start by walking left towards the wall
from the start walk the boundary of the room
noting the four corners
use it to calculate width and length
start at top left
we have a position on the wall, now we run along the boundary
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