If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.
If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.
In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.
#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition
Hello! Today I’ll be going over graphs . These data structures are the most widely used on the web, given the countless forms that a graph can organize values or even the data structures that can be made from them. In fact, the worldwide web itself can be represented as a graph. Let’s jump right into it!
A week ago we learned about graph data structure. Today we will talk about how we can work with graphs. We will try to find distances between two nodes in a graph. This is one of the main uses of graphs and it’s called graph traversal. There are two main graph algorithms Breadth First Search (BFS) and Depth First Search (DFS) and today we will talk about BFS.
This is how our graph looks like:
Breadth First Search
In our example we will work with an adjacency matrix. This is how matrix represents graph above:
We will start with an input node, then visit all its neighbors which is one edge away. And then visit all their neighbors. Point is to determine how close the node is to the root node.
Function which we will write in a moment will return an object with key value pairs where key will represent node and value how far this node is from the root.
First we will loop over the adjacency matrix (2D array), create as many key value pairs as many nodes we have on the graph. Initially we will assign distance to the infinity which represents lack of connection between the nodes.
In a previous blog post we talked about how to apply the Breadth First Search algorithm to the graph data structure. Today, let’s figure out how Depth First Search (DFS) works.
DFS is one of the fundamental algorithms used to search nodes and edges in a graph. It’s a form of a traversal algorithm.
Just a reminder, this is how our graph looks:
And this is our adjacency matrix (read here about adjacency matrix representation):
Based on the name we can assume that BFS focuses on the depth of the graph. The search starts at some root node and it keeps searching as far as possible each branch before backtracking.
Our task is to write an algorithm that explores routes as deep as possible before going back and exploring other routes. Let’s see how we can do it.