 So, students in this module, we will look one of the types of data structures, which is the document, which can be stored in a no SQL database. And we will also look at the products and the hybrid no SQL solutions. And I will match that this product of no SQL is for this type of application or vis-a-vis the type of data structure. So, let's look at the outline of the module. So, we will look at the document database, which is not like a relational database which we've already discussed. And we will look at the search engine because search engines are very similar in their architecture and in their functionality vis-a-vis no SQL database which I will explain in this module. And look at the hybrid no SQL databases. For example, I have a solution which is not addressed, okay? Or I have a problem which is not addressed by a particular solution. So, I can combine no SQL solutions together and maybe put an index on top of it. So, that gives me a lot of flexibility and that gives me a lot of functionality to solve those problems which are difficult to solve in a relational environment. And finally, I will talk about the no SQL products and solutions. So, let's first talk about the document database. So, as you can see over here in a document database, are sometimes called aggregate databases because they tend to hold documents that combine information in a single logical unit. So, this aggregate over here, this is not maths, it is not mathematics, okay? Although in online stores, orders and related delivery and payment addresses and order items can be thought of as a tree structure, data structures are known upfront, okay? They are known upfront and it's likely they won't vary and that you'll want to do column operations over them. So, they are not varying, okay? And this is the document database I am talking about and this is the other databases which we have already discussed in the prior modules. And very interesting is for the XML document, a table for example can be modeled as a very flat XML document, that is one with only a single set of elements and no sub-element hierarchies. A set of triples like a subgraph can be stored within a single document or across documents too. The utility of doing so depends, of course, on the indexing and query mechanism supported. There's no point storing triples in documents if you can't query them. So, you can store this over here, but you should be also able to query it, okay? So now after this we will talk about the search engine. So, why the search engines I am discussing in the context of no SQL databases because search engines are very architecturally similar to no SQL databases. The search engines are not very precise, they are not very exact, they are not required to be precise and exact. Search engines work on diverse structures or unstructured type of data which is supported and stored by a no SQL database also. And as you have seen in the context of search engines, that there are many structures but there is standard query, there's a box. You can type what you're looking for, there are no drop down things, there are no check boxes or radio buttons. You type the text, so the search engine provides you a kind of a uniformity in the type of query to get what you're looking for. And finally, like no SQL databases, search is imperfect. Imperfect means that as opposed to the relational query, where there is an exact match, okay? There is an exact match as per on or where clause, okay? Over here, we are working with imperfections. Because the search result gives me a variety of ranked results. Which are not exact and from there I can pick and choose what I'm looking for and I can go further. So SQL is exact matching but search engine is imperfect. And finally, let's look at some of the hybrid solutions also and some of the different solutions and products which are there in the market. So although each no SQL database has its core audience, several can be used to manage two or more of the previously mentioned data structures. Hybrid databases can easily handle document and key value storage needs, while also allowing fast aggregate operations similar to how column store works. There are more than 250 products in the market, but we are only looking at some of the leaders over here. So columnar is for data stacks Apache Cassandra, key value Bashor Iac Redis and Oracle NoSQL. Cripple graph is Neo4j, okay? And of course you can see over here and search engine Apache Solar, MarkLogic, Hybrid are Orient DB and MarkLogic and Arango DB. So you see that there are many things which are available over here in the market. And you as a developer or you as a chief technology officer or you as a chief information officer should be aware of what is available in the market and maximize the potential.