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Oracle MovieStream Workshop helps you discover the analytic power of data warehousing in the cloud. During the workshop you will explore a broad set of unique analytic technologies such as pattern matching, what-if data modeling, machine learning, spatial and graph.
Oracle MovieStream is a fictitious movie streaming service. They face challenges that are typical to many organizations across industries. MovieStream needs to:
...and much, much more
MovieStream has designed their solution to leverage the world class Autonomous Database and OCI Data Lake services. Their data architecture is following the Oracle Reference Architecture Enterprise Data Warehousing - an Integrated Data Lake - which is used by Oracle customers around the world. It's worthwhile to review the architecture so you can understand the value of integrating the data lake and data warehouse - as it allows you to answer more complex questions using all your data.
Most workshops focus on teaching you about a cloud service or performing a series of tasks. This workshop is different. You will learn how to deliver high value solutions using Oracle Cloud data platform services. And, the workshop will do this in the context of a company that we all can relate to and understand.
MovieStream's data warehouse is explored through a series of labs within a single workshop. We'll start with two key component of MovieSteam's architecture. MovieStream is storing their data across Oracle Object Storage and Autonomous Database. Data is captured from various sources into a landing zone in object storage.
This data is then processed (cleansed, transformed and optimized) and stored in a gold zone on object storage. Once the data is curated, it is loaded into Autonomous Database where it is analyzed by many (and varied) members of the user community.
You will learn how they built their solution and performed sophisticated analytics through a series of labs that highlight the following:
If you don't already have an Oracle Cloud account then we will walk you through the sign-up process for a free trial with US$300 of free credits which are valid for up to 30 days. Alternatively, you can sign up for an Always Free services which are available for an unlimited period of time. Learn More
Sit back and enjoy our movie trailer for this workshop
Oracle Autonomous Data Warehouse provides an easy-to-use, fully autonomous database that scales elastically and delivers fast query performance. As a service, Autonomous Database does not require database administration.
This lab walks you through the steps to get started provisioning a new Autonomous Data Warehouse using the Oracle Cloud Interface. After answering a few simple questions your new data warehouse will begin provisioning. In a few minutes it will be available and ready to use! Learn More
This lab takes you through the steps needed to load and link data from the MovieStream data lake on Oracle Cloud Infrastructure (OCI) Object Storage into your Autonomous Data Warehouse in preparation for exploration and analysis.
You can load data into your Autonomous Data Warehouse using tools such as Oracle Database tools, as well as other Oracle and non-Oracle data integration tools. You can load data:
You can also leave data in place in cloud-based object storage, and link to it from your Autonomous Database. Learn More
Sit back and enjoy our movie trailer for this data loading lab
In most real-world scenarios, queries against your data warehouse would normally involve the use of a data visualization tool such as Oracle Analytics Cloud (or other business intelligence products such as Qlik, Tableau, PowerBI, and many more, currently support Autonomous Data Warehouse). Within this part of the workshop we will use SQL commands to query our data using the built-in SQL Worksheet.
You will learn many of the basics for analyzing data across multiple tables. This includes using views to simplify sophisticated queries, performing time series analyses and more. Learn More
Sit back and enjoy our movie trailer for this SQL analytics lab
Semi-structured data does not have predefined fields that have a clearly defined purpose. Typically most semi-structured data looks similar to a text-based document but most types of semi-structured data lack a precise structural definition and come in all shapes and sizes. This can make it very challenging to work with this type of data.
The Autonomous Data Warehouse is based on a converged database model that has native support for all modern data types and the latest development paradigms built into one product. It supports spatial data for location awareness, JSON and XML for document store type content, streams for IoT device integration, in-memory technologies for real-time analytics, and of course, traditional relational data. n this section of the workshop, you are going to work with some semi-structured data which is in a common format called JSON. Learn More
Sit back and enjoy our movie trailer for this lab on working with semi-structured data.
To understand customer behavior, we need to look into the their geo-demographic information, but also their transactional behavior. For transactional data, we need to be able to provide summaries of number of transactions and aggregated values per month for each type of transaction that we would like to explore, since the algorithms need to receive as input a single row per customer, with all their attributes spread out in columns.
In this lab, you will use the Oracle Machine Learning (OML) SQL notebook application provided with your Autonomous Data Warehouse, as well as the OML AutoML UI and its features to identify customers with a higher likelihood of churning from Oracle MovieStream streaming services to a different movie streaming company. Learn More
To reduce customer churn, our business has partnered with a pizza chain to offer coupons for free pizza. We will offer this promotion to customers identified as both likely to churn and within reasonable proximity to a pizza chain location. Likelihood to churn is addressed in a separate Machine Learning lab (available soon). In this lab you determine which customers are near one or more pizza chain locations, and for those customers, which location is the closest. Specifically, we will answer the following question: "for customers that are within 10km of pizza chain location(s), which is the closest and what is the distance?"
In this lab you will learn how to create function-based spatial indexes for tables with latitude, longitude columns and then perform spatial queries to identify the nearest pizza location to customers. Learn More
Sit back and enjoy our movie trailer for this spatial lab
When you model your data as a graph, you can run graph algorithms on your data. Graph algorithms analyze your data based on the connections and relationships in your data. Graph queries find patterns in your data, such as cycles, paths between vertices, anomalous patterns, and so on. Graph algorithms are invoked using a Java or Python API, and graph queries are run using PGQL (Property Graph Query Language, see pgql-lang.org).
You have the choice of over 60 pre-built algorithms to use to analyze this graph.
This lab will use the WhomToFollow algorithm to identify who to follow in a social network and then use that data to identify possible movie recommendations for each customer. Learn More
Sit back and enjoy our movie trailer for this graph lab
Get started now with the MovieStream Workshop on Oracle LiveLabs