 Now, since you have studied databases or you have been working in the domain of databases, so whenever somebody says databases, what comes to your mind is relational database. That is fine, because relational databases have been around for maybe three decades or maybe longer. But as we have been discussing and we will discuss further that for the new challenging problems we need to have new solutions. One of those new solutions is offered through the NoSQL databases. So in this module, I will describe about the NoSQL database paradigm. So I have an ambitious module coverage, which I show you right now. So we will looking at database construction, structure, records, association, organization, replication, technology, and of course, search tools. Why search tools? Because as I mentioned earlier, I will mention here. And in the next module also, that there is a lot of similarity between the search engine and NoSQL databases. So let's go into more details. So what is a database? A database is a single logical unit. Single logical unit. What is a data form? Data form is that I have clusters of servers on which the data has been distributed and there are many benefits of doing this. And then we do a partition. When we use the cluster, of course, I have to do a partition also. Partition separates places, distributes the data over those clusters. And I have this collection and of course the schema. The schema is the structure of the database. The schema is defined before creating the database. But remember that in the context of NoSQL, there is no requirement of schema. Just I mentioned it over here for the sake of comparison. So let's look into more details. Records. So what is a record? A record is a single atomic unit, right? And you have no these things from the context of relational model. But what I'll be discussing over here is a table. A single class of record in big table for the context of, you remember I discussed, they're also called tables in a triple store. They may be called subject RDF types or named graphs, depending on the context. In this document store, they may be collections, okay? So we have these over here. I was talking about this in the context of the big table, which you remember, which introduced by which company. Associations. Primary key, you know, a key, and other than that concept of RDVMS, RDVMS concept, a key in a key value store URI. URI in a document store or IRI, IRI in a triple graph store. Foreign key, you know, a relationship, the link or an edge in graph theory that indicates two records have a semantic link. That relationship can be between records in our same tables. Of course, that relationship for a triple, for a triple, this is different. And for RDVMS, VMS, this is, this is basically a foreign key, primary key relationship. These are different things. Just for the sake of understanding, I have mentioned them over here. Let's look at more details. Now storage organization, we have this server over here, user one. This is original data. And this is the full replication, okay? This is the database replication over here. Of course, we can have this replication also. But the point is more interesting over here is flexible replication. Provides application, controlled application of data between databases in different clusters. Updates may not arrive in the same order they were applied to the first database. Typically involves some custom processing. So you can see over here that this replication is through the replica server into the replica database. So these are some of the differences. And how the replication technology works in the context of disk replication, database replication and flexible replication. And these are true in the context of NoSQL. Some of those things which I've been explaining work for the relational model which are described over here just for the sake of understanding. Search tools. Search tools indexing is there. Now what is the benefit of indexing that the indexing takes me directly to what I want without searching all of the data, all of the documents. And of course, there is the reverse indexing also, which points back towards what I'm looking for. Now there is one difference between what we get through a query and what we get through a search engine result. The query gives an exact match. But over here in the context of search, in the context of NoSQL, where there is NoSQL structured query language, we get the results which are close to what we are looking for. And then we differentiate, then we go into more details, then we perform further search and that's how we work. And this is one of the similarity between the NoSQL solutions and the search engine in terms of architecture, in terms of functionality, in terms of search. So for the sake of understanding, I have described all of this. Thank you for your time. That's all I have.