Big Data Architecture, Use Cases, and Infrastructure Needs

Name: Big Data Architecture, Use Cases, and Infrastructure Needs
Author: Chintan Thakkar

Learn about infrastructure for Big Data on the public cloud.

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Course: On-Demand

(gentle music) Welcome to big data architecture, use cases, and infrastructure needs. In these lessons, you'll learn about structured and unstructured data, and big data architecture on the public cloud. There are several use cases related to big data analytics, and you'll learn how to generate information from big data and use it for driving business sales. We'll discuss the details of structured and unstructured data, big data architecture, big data use cases for public cloud, big data infrastructure needs. Structured data is highly organized and often stored in a relational database in a row and column format. It's quite easy to search for a structured data format because it follows the data model that the SQL database administrator has created, and contains a fixed schema. If your database table contains three columns, then you can only store the data in three columns. The data is created by humans or from enterprise applications. Here are the main applications that typically generate structured data, customer management systems, retail inventories, enterprise resource planning systems, student information systems. Unstructured data often means a large data set. It doesn't contain a fixed data model or schema, and it can be difficult to search within the unstructured data due to the size and the structure of the data. The data is generated by the internet of things, or humans. Typical examples of unstructured data include image processing, data analytics, provide recommendations, predictive systems. The unstructured data is often stored in a NoSQL database or data lake type of solution. There are several vendors that provide a database as a service for storing unstructured data. One option is MongoDB Atlas MongoDB Atlas provides a database as a service for storing document data that works as a fully managed solution that's deployed on the public cloud. It can be easily scaled based on requirements, and it's also possible to have always-on settings with cross-region replication. You can encrypt documents and store them in the database. If there is a higher load, then it can also scale automatically based on the configuration. MongoDB Atlas is an on-demand database, meaning it can be provisioned within five minutes or less. Once you're done with the database, you can terminate the resource with a single click. MongoDB Atlas can store entire documents. Let's review a few examples of MongoDB documents. Here is a sample JSON schema. You can generate the JSON document from this schema and store it. MongoDB Atlas also provides the functionality of region failover. So if the entire cloud region fails, then MongoDB will instead run from another region. You can configure the cluster size, public cloud, and several shards based on your requirements. Your team can focus on application development instead of focusing on MongoDB infrastructure, which also allows you to set up the automated path deployment on the cluster. Semi-structured data supports HTML, JSON and XML formats. This format is in between structured and unstructured data, and provides limited functionality compared to the relational database. The storage requirements are fewer than unstructured data, but more than structured data. You can store server logs, internet of things data, and more, all using its flexible schema. Most of big data is unstructured. In fact, it's estimated that 90% of the big data use cases are run against unstructured data. This means that you need to have a solid plan to determine how you're going to store and utilize your unstructured data.

Beginner Provider : Chintan Thakkar 4 Lessons · 18m in Arabic, German, English, Spanish, French, Portuguese, Urdu, Chinese

Course Description

Big Data can help us in many ways. Most of Big Data is unstructured. In fact, it’s estimated that 90% of the Big Data use cases are run against unstructured data. This means that you need to have a solid plan to determine how you’re going to store and utilize your unstructured data. In these lessons, you will learn about structured and unstructured data, as well as Big Data architecture on the public cloud. You will also learn how to use Big Data for driving business sales.

Big Data analytics is a lot easier to deploy on the public cloud compared to an on-premises environment. You’ll need hundreds of virtual machines to run the analytics depending on the size of the data. It would be difficult to provision that many virtual machines in an on-premises environment, but can be done relatively easily over the cloud. This Big Data Architecture, Use Cases, and Infrastructure Needs course gives you real business use cases to show you the potential of Big Data.

What You'll Learn

Distinguish structured, semi-structured, and unstructured data and plan how to store and use unstructured data
Explain Big Data architecture on the public cloud
Identify Big Data infrastructure needs for the public cloud
Plan server provisioning for running Big Data analytics
Recognize real business use cases that show the potential of Big Data, including driving business sales

Key Takeaways

Most Big Data is unstructured, with an estimated 90% of Big Data use cases run against unstructured data, so a solid plan for storing and using unstructured data is essential.
Big Data analytics is easier to deploy on the public cloud than in an on-premises environment.
Running the analytics can require hundreds of virtual machines depending on the size of the data, which is hard to provision on-premises but relatively easy on the cloud.
Big Data can be used to drive business sales, and the course presents real business use cases to demonstrate its potential.

Frequently Asked Questions

What topics does this course cover?

It covers structured and unstructured data, Big Data architecture on the public cloud, Big Data infrastructure needs, and Big Data use cases, along with how to use Big Data for driving business sales.

Why does the course focus on the public cloud rather than on-premises?

Because Big Data analytics is easier to deploy on the public cloud; running analytics can require hundreds of virtual machines depending on the data size, which is difficult to provision on-premises but can be done relatively easily over the cloud.

What skills will I gain from this course?

You will build skills in Big Data, Big Data Analytics, Data Architecture, Data Infrastructure, semi-structured data, and unstructured data.

Does the course include real-world examples?

Yes, the course gives you real business use cases to show you the potential of Big Data.

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