Yesterday we were discussing storj  as a  peer to peer cloud storage network. Regardless of whether we store data via a distributed hash table, in a streaming storage, object storage, or some cloud database, the enablement of data usages matter most in the choice of storage. 

Let us take a look at one of the most common form of data usage which is querying. We had brought up earlier that querying language is usually independent of the data source and even the architecture. The language is mostly to enable the data usage and is a form of expression of logic.

We saw two different forms of expression of this language 

First - we saw the expression in the form of standard query operators which became popular across languages in the form of expressions such as .Where() and .Sum() as in LINQ. This was convenient primitives taking inspiration from the tried, tested and well established SQL Query language. The language is very inspiring to express queries in succinct manner often enabling chaining to refine and improve resultsets.

The second form of expression was with the search query language which has had a rich history in shell scripting and log analysis where the results of one command are piped into another command for transformation and analysis. Although similar in nature to chaining operators the expressions of this form of query involved more search like capabilities across heterogenous data such as with the use of regular expressions for detecting patterns.  This form of expression not only involved powerful query operators but facilitated data extract, transform and load as it made its way through the expressions. 

Operators used in both these forms of query expressions expanded to include more involved from of computations such as grouping, ranking, sorting and such analysis. 

In particular there are two new operators possible - one for the use of additional columns to generate groups that facilitate hierarchical view of entities and another for additional rows to describe new pseudo-entities transformed from exiting entities that is helpful to view entities in the form of layers.
The ability to view the entities as a hierarchical organization let us expand our logic to perform tree-based searches and to assign different roles to entities so that the data may have augmented information to improve the queries. More importantly the addition of columns for groups may be a temporary table so that we don't necessarily write on the original data. This form of data extract, transform and load is popular in data reporting stacks that are used to publish charts and graphs from data.
The ability to view the entities as not just those existing but composites that behave the same as existing entities but also in simpler abstraction of entities purposes different from those involving original entities. Since there is a separation of concern involving the original and the composites, this is considered layering. Since the selection of original entities determine the composites for use in the higher layer, this comes as a different technique from grouping and hierarchies. 
#codingexercise

#codingexercise

We were discussing the count of the number of decreasing paths in a matrix if the traversal is permitted only on adjacent horizontal and vertical cells The same works for increasing paths 

• Solution: we can sum the number of paths with each cell as the starting location if we know how to do it for  a given starting position. 

initialize the sum to one for the number of paths at the center where the center forms a standalone single element  path

foreach of the four adjacent positions around a center:

          if the value is greater than the center:

                recurse to find the count at the adjacent cell of its not available in the dp matrix or insert the value there after finding.

                add the count through that adjacent cell to the sum at the center.

return the sum of paths for this position