title
Data Warehouse Concepts | Data Warehouse Tutorial | Data Warehouse Architecture | Edureka
description
🔥 Data Warehousing & BI Training (Use Code "𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎"): https://www.edureka.co/search
This tutorial on data warehouse concepts will tell you everything you need to know in performing data warehousing and business intelligence. The various data warehouse concepts explained in this video are:
1. What Is Data Warehousing?
2. Data Warehousing Concepts:
3. OLAP (On-Line Analytical Processing)
4. Types Of OLAP Cubes
5. Dimensions, Facts & Measures
6. Data Warehouse Schema
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How it Works?
1. This is a 5 Week Instructor led Online Course, 25 hours of assignment and 10 hours of project work
2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course.
3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a Verifiable Certificate!
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About the Course:
Edureka's Data Warehousing and Business Intelligence Course, will introduce participants to create and work with leading ETL & BI tools like:
1. Talend 5.x to create, execute, monitor and schedule ETL processes. It will cover concepts around Data Replication, Migration and Integration Operations
2. Tableau 9.x for data visualization to see how easy and reliable data visualization can become for representation with dashboards
3. Data Modeling tool ERwin r9 to create a Data Warehouse or Data Mart
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Who should go for this course?
The following professionals can go for this course:
1. Data warehousing enthusiasts
2. Analytics Managers
3. Data Modelers
4. ETL Developers and BI Developers
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Why learn Data Warehousing and Business Intelligence?
All the successful companies have been investing large sums of money in business intelligence and data warehousing tools and technologies. Up-to-date, accurate and integrated information about their supply chain, products and customers are critical for their success.
With the advent of Mobile, Social and Cloud platform, today's business intelligence tools have evolved and can be categorized into five areas, including databases, extraction transformation and load (ETL) tools, data quality tools, reporting tools and statistical analysis tools. This course will provide a strong foundation around Data Warehousing and Business Intelligence fundamentals and sophisticated tools like Talend, Tableau and ERwin.
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For more information, please write back to us at sales@edureka.co or call us at IND: 9606058406 / US: 18338555775 (toll-free).
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Customer Review:
Kanishk says, "Underwent Mastering in DW-BI Course. The training material and trainer are up to the mark to get yourself acquainted to the new technology. Very helpful support service from Edureka."
detail
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Alright, great, so have a couple of yeses from here.', 'start': 98.057, 'duration': 6.686}, {'end': 108.006, 'text': 'Alright, Anita is saying yes, Nofil is saying yes.', 'start': 105.523, 'duration': 2.483}, {'end': 111.249, 'text': 'Alright, great, and Akbar is saying yes.', 'start': 108.506, 'duration': 2.743}, {'end': 113.39, 'text': "Alright guys, so let's get started then.", 'start': 111.349, 'duration': 2.041}], 'summary': 'Acknowledgements and confirmations were requested before starting the session.', 'duration': 25.625, 'max_score': 87.765, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CHYPF7jxlik/pics/CHYPF7jxlik87765.jpg'}], 'start': 0.129, 'title': 'Basics of data warehousing', 'summary': 'Covers the basics of data warehousing, discussing olap, dimensions, facts, and measures, and outlines the agenda and interaction with participants.', 'chapters': [{'end': 108.006, 'start': 0.129, 'title': 'Webinar on data warehousing', 'summary': 'Covers the basics of data warehousing, including olap, dimensions, facts, and measures, and discusses the agenda and interaction with participants.', 'duration': 107.877, 'highlights': ['The chapter covers the basics of data warehousing, including OLAP, dimensions, facts, and measures, which are important topics related to data warehousing.', 'The instructor discusses the agenda for the session and encourages participants to interact by acknowledging their understanding and asking for doubts during the session.', 'The instructor introduces the concept of OLAP (Online Analytical Processing) in detail and mentions working with various OLAP cubes in data warehousing.', 'The chapter also includes a quick brief about data warehousing, data warehousing architecture, and the interrelation of dimensions, facts, and measures.', 'The instructor emphasizes interaction with participants, urging them to acknowledge their understanding and ask doubts through the chat box for immediate clarification.']}], 'duration': 107.877, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CHYPF7jxlik/pics/CHYPF7jxlik129.jpg', 'highlights': ['The chapter covers the basics of data warehousing, including OLAP, dimensions, facts, and measures.', 'The instructor introduces the concept of OLAP in detail and mentions working with various OLAP cubes in data warehousing.', 'The chapter also includes a quick brief about data warehousing, data warehousing architecture, and the interrelation of dimensions, facts, and measures.', 'The instructor discusses the agenda for the session and encourages participants to interact by acknowledging their understanding and asking for doubts during the session.', 'The instructor emphasizes interaction with participants, urging them to acknowledge their understanding and ask doubts through the chat box for immediate clarification.']}, {'end': 750.742, 'segs': [{'end': 284.382, 'src': 'heatmap', 'start': 218.272, 'weight': 0.751, 'content': [{'end': 224.896, 'text': "So that's why we need to put them all into the data warehouse and once it's here, we can perform these kind of analysis and, you know,", 'start': 218.272, 'duration': 6.624}, {'end': 227.158, 'text': 'get insights from the data warehouse.', 'start': 224.896, 'duration': 2.262}, {'end': 232.844, 'text': 'and, of course, since the data warehouse, your all your end users, your business analysts, your data analyst,', 'start': 227.158, 'duration': 5.686}, {'end': 235.827, 'text': 'they can all use it for analysis and visualization, right.', 'start': 232.844, 'duration': 2.983}, {'end': 242.072, 'text': "so that's the thing about a data warehouse and that's the use of a data warehouse, and it functions on the basis of OLAP, right.", 'start': 235.827, 'duration': 6.245}, {'end': 245.456, 'text': 'so I told you, OLAP stands for online analytical processing.', 'start': 242.072, 'duration': 3.384}, {'end': 251.02, 'text': 'So the whole process of doing analysis or running queries on data warehouse, right?', 'start': 245.916, 'duration': 5.104}, {'end': 254.262, 'text': "It's done on the basis of online analytical processing, right?", 'start': 251.42, 'duration': 2.842}, {'end': 255.883, 'text': "So that's one of the activities that is done.", 'start': 254.302, 'duration': 1.581}, {'end': 262.757, 'text': "So if you're running any kind of queries on the database, then it will be called as OLTP, online transaction processing.", 'start': 256.774, 'duration': 5.983}, {'end': 268.718, 'text': "But any kind of activity or querying and all these things, when it happens on data warehouse, it's called OLAP.", 'start': 263.117, 'duration': 5.601}, {'end': 270.359, 'text': "So that's what an OLAP is, guys.", 'start': 269.158, 'duration': 1.201}, {'end': 275.34, 'text': "And yeah, basically it's a center location where consolidated data from multiple locations are stored.", 'start': 270.899, 'duration': 4.441}, {'end': 279.861, 'text': 'So you get data in here from multiple locations, from multiple sources.', 'start': 275.7, 'duration': 4.161}, {'end': 282.542, 'text': 'You can get it from one or two databases.', 'start': 279.901, 'duration': 2.641}, {'end': 284.382, 'text': 'You can get it from one or two flat files.', 'start': 282.722, 'duration': 1.66}], 'summary': 'Data warehouse enables olap for analysis, visualization and consolidated data storage.', 'duration': 66.11, 'max_score': 218.272, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CHYPF7jxlik/pics/CHYPF7jxlik218272.jpg'}, {'end': 262.757, 'src': 'embed', 'start': 235.827, 'weight': 1, 'content': [{'end': 242.072, 'text': "so that's the thing about a data warehouse and that's the use of a data warehouse, and it functions on the basis of OLAP, right.", 'start': 235.827, 'duration': 6.245}, {'end': 245.456, 'text': 'so I told you, OLAP stands for online analytical processing.', 'start': 242.072, 'duration': 3.384}, {'end': 251.02, 'text': 'So the whole process of doing analysis or running queries on data warehouse, right?', 'start': 245.916, 'duration': 5.104}, {'end': 254.262, 'text': "It's done on the basis of online analytical processing, right?", 'start': 251.42, 'duration': 2.842}, {'end': 255.883, 'text': "So that's one of the activities that is done.", 'start': 254.302, 'duration': 1.581}, {'end': 262.757, 'text': "So if you're running any kind of queries on the database, then it will be called as OLTP, online transaction processing.", 'start': 256.774, 'duration': 5.983}], 'summary': 'Data warehouse uses olap for analysis, oltp for queries.', 'duration': 26.93, 'max_score': 235.827, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CHYPF7jxlik/pics/CHYPF7jxlik235827.jpg'}, {'end': 395.975, 'src': 'embed', 'start': 354.132, 'weight': 0, 'content': [{'end': 357.312, 'text': 'So these are the entire set of activities that are involved.', 'start': 354.132, 'duration': 3.18}, {'end': 360.373, 'text': 'Now you can also consider this to be the data warehousing architecture.', 'start': 357.612, 'duration': 2.761}, {'end': 367.459, 'text': 'Now, before I explain all the activities and all the different stages that are there in this data warehousing architecture,', 'start': 361.175, 'duration': 6.284}, {'end': 368.961, 'text': 'let me first of all go through the definition.', 'start': 367.459, 'duration': 1.502}, {'end': 372.983, 'text': 'So this whole act is called as data warehousing.', 'start': 369.921, 'duration': 3.062}, {'end': 380.489, 'text': 'And data warehousing is the act of organizing and storing data in a way so as to make its retrieval efficient and insightful.', 'start': 373.404, 'duration': 7.085}, {'end': 385.993, 'text': 'So this is the key terms here, to make its retrieval efficient and insightful.', 'start': 381.269, 'duration': 4.724}, {'end': 390.574, 'text': 'so you organize the data and you store the data in a data warehouse.', 'start': 386.793, 'duration': 3.781}, {'end': 395.975, 'text': 'in a way, you can access the data at a later point of time and that kind of an access should be easy.', 'start': 390.574, 'duration': 5.401}], 'summary': 'Data warehousing involves organizing and storing data for efficient retrieval and insightful analysis.', 'duration': 41.843, 'max_score': 354.132, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CHYPF7jxlik/pics/CHYPF7jxlik354132.jpg'}, {'end': 580.935, 'src': 'embed', 'start': 552.687, 'weight': 5, 'content': [{'end': 556.088, 'text': 'so all these things will be controlled and governed by metadata.', 'start': 552.687, 'duration': 3.401}, {'end': 560.249, 'text': 'So without your metadata, your data warehouse is basically useless.', 'start': 557.368, 'duration': 2.881}, {'end': 568.091, 'text': "And metadata is the so anyone who's working on a data warehouse, they will tell you that metadata is what differentiates everything.", 'start': 560.809, 'duration': 7.282}, {'end': 574.593, 'text': "that's what makes their life simpler, and that's what is actually the difference between a database and a data warehouse.", 'start': 568.091, 'duration': 6.502}, {'end': 578.074, 'text': 'So this serves the whole purpose and the whole difference between the two.', 'start': 574.613, 'duration': 3.461}, {'end': 580.935, 'text': 'so that, and then you have aggregate data, right.', 'start': 578.854, 'duration': 2.081}], 'summary': 'Metadata is crucial for data warehouse, differentiates database and warehouse, simplifies work.', 'duration': 28.248, 'max_score': 552.687, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CHYPF7jxlik/pics/CHYPF7jxlik552687.jpg'}, {'end': 611.727, 'src': 'heatmap', 'start': 502.095, 'weight': 6, 'content': [{'end': 506.058, 'text': "So you'll have your raw data, you will have your metadata and you will have your aggregate data.", 'start': 502.095, 'duration': 3.963}, {'end': 511.923, 'text': "So first of all your raw data is the rows or columns or the actual data that's being transferred.", 'start': 506.779, 'duration': 5.144}, {'end': 513.825, 'text': 'So this makes up the junk of the data.', 'start': 512.023, 'duration': 1.802}, {'end': 522.172, 'text': 'But metadata is the most important aspect which powers the data warehouse and which differentiates between a database and a data warehouse.', 'start': 514.626, 'duration': 7.546}, {'end': 528.077, 'text': "Why? Because metadata is something that's going to give you data about your raw data.", 'start': 522.611, 'duration': 5.466}, {'end': 535.504, 'text': 'so whatever rules that are there in your data warehouse or whatever information that is there inside your data warehouse right,', 'start': 528.482, 'duration': 7.022}, {'end': 539.664, 'text': 'so information about that particular data will be stored inside your metadata.', 'start': 535.504, 'duration': 4.16}, {'end': 543.165, 'text': "so we'll have data about which are the different tables that are there in your data warehouse,", 'start': 539.664, 'duration': 3.501}, {'end': 547.806, 'text': 'what each table does and what kind of attributes are there inside each table?', 'start': 543.165, 'duration': 4.641}, {'end': 548.946, 'text': 'what kind of information?', 'start': 547.806, 'duration': 1.14}, {'end': 552.687, 'text': 'what will be the data, type of the data present on each of those attributes?', 'start': 548.946, 'duration': 3.741}, {'end': 556.088, 'text': 'so all these things will be controlled and governed by metadata.', 'start': 552.687, 'duration': 3.401}, {'end': 560.249, 'text': 'So without your metadata, your data warehouse is basically useless.', 'start': 557.368, 'duration': 2.881}, {'end': 568.091, 'text': "And metadata is the so anyone who's working on a data warehouse, they will tell you that metadata is what differentiates everything.", 'start': 560.809, 'duration': 7.282}, {'end': 574.593, 'text': "that's what makes their life simpler, and that's what is actually the difference between a database and a data warehouse.", 'start': 568.091, 'duration': 6.502}, {'end': 578.074, 'text': 'So this serves the whole purpose and the whole difference between the two.', 'start': 574.613, 'duration': 3.461}, {'end': 580.935, 'text': 'so that, and then you have aggregate data, right.', 'start': 578.854, 'duration': 2.081}, {'end': 582.916, 'text': 'so all these three things together.', 'start': 580.935, 'duration': 1.981}, {'end': 589.919, 'text': 'they form the data warehouse and then, once your data is inside your data warehouse, your end users can use this data to perform analysis.', 'start': 582.916, 'duration': 7.003}, {'end': 590.799, 'text': 'so how do they do it?', 'start': 589.919, 'duration': 0.88}, {'end': 591.699, 'text': 'they run queries.', 'start': 590.799, 'duration': 0.9}, {'end': 594.861, 'text': 'okay, so you have user groups here and they perform queries.', 'start': 591.699, 'duration': 3.162}, {'end': 599.983, 'text': 'okay, now, the act of performing queries on your data warehouse is called online analytical processing.', 'start': 594.861, 'duration': 5.122}, {'end': 601.323, 'text': "Okay, that's the OLAP.", 'start': 600.383, 'duration': 0.94}, {'end': 607.706, 'text': 'Okay, so such queries are called OLAP queries because they will be analysis based, right? So such queries will be analysis based.', 'start': 601.784, 'duration': 5.922}, {'end': 611.727, 'text': "So that's why it's called on an analytical processing and you have something called as data matcher.", 'start': 607.726, 'duration': 4.001}], 'summary': 'Metadata is crucial for data warehouse, differentiates from database. olap queries used for analysis.', 'duration': 109.632, 'max_score': 502.095, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CHYPF7jxlik/pics/CHYPF7jxlik502095.jpg'}], 'start': 108.506, 'title': 'Data warehousing fundamentals', 'summary': 'Covers the basics and architecture of data warehousing, emphasizing its purpose, benefits, and the process of olap. it also discusses data organization, storage, analysis, etl process, metadata importance, and data marts.', 'chapters': [{'end': 332.263, 'start': 108.506, 'title': 'Data warehousing basics', 'summary': 'Explains the basics of data warehousing, including its purpose, benefits, and the process of olap, emphasizing the need for data warehousing for analytical purposes and consolidation of data from multiple sources.', 'duration': 223.757, 'highlights': ['Data warehousing is essential for analytical purposes and can store and analyze large amounts of data from different sources, providing insights and visualization. Data warehousing enables the storage and analysis of large amounts of data from various sources, allowing for insights and visualization.', 'The process of OLAP, Online Analytical Processing, is fundamental to data warehousing and distinguishes it from OLTP, Online Transaction Processing. OLAP, Online Analytical Processing, is a key aspect of data warehousing, differentiating it from OLTP, and is used for analysis and querying.', 'Data warehousing involves consolidating data from multiple locations and sources into a centralized location for analysis and storage. Data warehousing consolidates data from diverse locations and sources into a central repository for analysis and storage.']}, {'end': 750.742, 'start': 333.888, 'title': 'Data warehousing architecture', 'summary': 'Discusses the process of data warehousing, including the activities involved, such as data organization, storage, and analysis, as well as the architecture, etl process, metadata importance, olap queries, and data marts.', 'duration': 416.854, 'highlights': ['The process of data warehousing involves organizing and storing data to make its retrieval efficient and insightful, differentiating it from data stored in a database, with a focus on transforming data into information and serving a specific purpose.', 'The data warehousing architecture includes the stages of transferring data from different sources to the data warehouse through a staging area using the ETL process, categorizing data into raw data, metadata, and aggregate data, and allowing end users to perform analysis through OLAP queries.', 'The significance of metadata in data warehousing lies in providing information about the raw data, controlling attributes, and differentiating a data warehouse from a database, making it crucial for the functionality and purpose of the data warehouse.', 'OLAP queries enable end users to perform analysis on the data warehouse, categorized as analysis-based queries, while data marts serve as restricted access points within the data warehouse, allowing specific user groups to access relevant information based on their domain or field.']}], 'duration': 642.236, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CHYPF7jxlik/pics/CHYPF7jxlik108506.jpg', 'highlights': ['Data warehousing enables the storage and analysis of large amounts of data from various sources, allowing for insights and visualization.', 'OLAP, Online Analytical Processing, is a key aspect of data warehousing, differentiating it from OLTP, and is used for analysis and querying.', 'Data warehousing consolidates data from diverse locations and sources into a central repository for analysis and storage.', 'The process of data warehousing involves organizing and storing data to make its retrieval efficient and insightful, differentiating it from data stored in a database, with a focus on transforming data into information and serving a specific purpose.', 'The data warehousing architecture includes the stages of transferring data from different sources to the data warehouse through a staging area using the ETL process, categorizing data into raw data, metadata, and aggregate data, and allowing end users to perform analysis through OLAP queries.', 'The significance of metadata in data warehousing lies in providing information about the raw data, controlling attributes, and differentiating a data warehouse from a database, making it crucial for the functionality and purpose of the data warehouse.', 'OLAP queries enable end users to perform analysis on the data warehouse, categorized as analysis-based queries, while data marts serve as restricted access points within the data warehouse, allowing specific user groups to access relevant information based on their domain or field.']}, {'end': 1538.686, 'segs': [{'end': 822.663, 'src': 'heatmap', 'start': 750.742, 'weight': 0.876, 'content': [{'end': 756.386, 'text': "but yeah, if you do have any doubts, please put them in the chat box and I'll get back to you all right?", 'start': 750.742, 'duration': 5.644}, {'end': 759.268, 'text': 'So, moving on the data warehousing concepts.', 'start': 756.666, 'duration': 2.602}, {'end': 766.013, 'text': "Now let's understand the various concepts revolving around data warehousing, like OLAP, dimensions, facts, and schemas.", 'start': 759.428, 'duration': 6.585}, {'end': 770.156, 'text': 'So first of all, let me give you a brief about OLAP again.', 'start': 767.074, 'duration': 3.082}, {'end': 771.097, 'text': "Let's go into details here.", 'start': 770.196, 'duration': 0.901}, {'end': 777.294, 'text': 'As it says, OLAP is a flexible way for you to make complicated analysis of multi-dimensional data.', 'start': 772.248, 'duration': 5.046}, {'end': 784.121, 'text': 'Okay, so when we say multi-dimensional data, then whatever data is stored in a data warehouse, it has multiple views right?', 'start': 777.794, 'duration': 6.327}, {'end': 787.025, 'text': 'So they will all be stored in such a way that you can perform analysis.', 'start': 784.181, 'duration': 2.844}, {'end': 790.008, 'text': "They'll be stored, all the different tables will be linked with each other.", 'start': 787.405, 'duration': 2.603}, {'end': 796.488, 'text': "So there'll be different views, there'll be different categories of data and all these things to perform analysis.", 'start': 790.786, 'duration': 5.702}, {'end': 798.529, 'text': 'Then you use the OLAP activities.', 'start': 796.608, 'duration': 1.921}, {'end': 802.011, 'text': 'So the OLAP queries you run on that data.', 'start': 798.869, 'duration': 3.142}, {'end': 806.592, 'text': "And whatever data is stored in your data warehouse, that's called multidimensional data.", 'start': 802.731, 'duration': 3.861}, {'end': 812.495, 'text': 'And the very act of storing it is in the form of OLAP cubes.', 'start': 807.173, 'duration': 5.322}, {'end': 817.179, 'text': 'Right. so you have OLTP on one side and you have OLAP on the other side.', 'start': 813.195, 'duration': 3.984}, {'end': 822.663, 'text': 'So, as you can see this line, it says OLTP systems use data stored in form of two-dimensional tables.', 'start': 817.179, 'duration': 5.484}], 'summary': 'Introduction to data warehousing concepts including olap, dimensions, facts, schemas, and multidimensional data storage for analysis.', 'duration': 71.921, 'max_score': 750.742, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CHYPF7jxlik/pics/CHYPF7jxlik750742.jpg'}, {'end': 798.529, 'src': 'embed', 'start': 772.248, 'weight': 0, 'content': [{'end': 777.294, 'text': 'As it says, OLAP is a flexible way for you to make complicated analysis of multi-dimensional data.', 'start': 772.248, 'duration': 5.046}, {'end': 784.121, 'text': 'Okay, so when we say multi-dimensional data, then whatever data is stored in a data warehouse, it has multiple views right?', 'start': 777.794, 'duration': 6.327}, {'end': 787.025, 'text': 'So they will all be stored in such a way that you can perform analysis.', 'start': 784.181, 'duration': 2.844}, {'end': 790.008, 'text': "They'll be stored, all the different tables will be linked with each other.", 'start': 787.405, 'duration': 2.603}, {'end': 796.488, 'text': "So there'll be different views, there'll be different categories of data and all these things to perform analysis.", 'start': 790.786, 'duration': 5.702}, {'end': 798.529, 'text': 'Then you use the OLAP activities.', 'start': 796.608, 'duration': 1.921}], 'summary': 'Olap allows analysis of multi-dimensional data in data warehouses for flexible decision-making.', 'duration': 26.281, 'max_score': 772.248, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CHYPF7jxlik/pics/CHYPF7jxlik772248.jpg'}, {'end': 945.723, 'src': 'embed', 'start': 917.086, 'weight': 1, 'content': [{'end': 925.245, 'text': "Now that's where your data warehouse is, but with your database, Database, however, are modeled on the concept of OLTP, Online Transaction Processing.", 'start': 917.086, 'duration': 8.159}, {'end': 932.271, 'text': 'So your databases are modeled on the concept of OLTP, and your data warehouse is modeled on the concept of OLAP.', 'start': 925.526, 'duration': 6.745}, {'end': 937.996, 'text': "So that's the key difference between the two, the basis on which these two are modeled.", 'start': 933.172, 'duration': 4.824}, {'end': 945.723, 'text': 'And your OLTP systems use data stored in the form of two-dimensional tables with rows and columns, and your OLAP will have multiple views.', 'start': 938.677, 'duration': 7.046}], 'summary': 'Oltp databases use 2d tables, olap data warehouses have multiple views.', 'duration': 28.637, 'max_score': 917.086, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CHYPF7jxlik/pics/CHYPF7jxlik917086.jpg'}, {'end': 1000.589, 'src': 'embed', 'start': 974.927, 'weight': 2, 'content': [{'end': 981.897, 'text': "So you'll get new insights, you can think of different things and your whole job of making analysis and getting insights will become simpler.", 'start': 974.927, 'duration': 6.97}, {'end': 985.241, 'text': "It'll become easier with the help of OLAP activities.", 'start': 982.157, 'duration': 3.084}, {'end': 989.427, 'text': 'Because it supports activities like filtering and sorting of data.', 'start': 986.142, 'duration': 3.285}, {'end': 995.248, 'text': "So maybe filtering and sorting of data may be possible even inside OLTP, but there's a limit on that.", 'start': 989.927, 'duration': 5.321}, {'end': 1000.589, 'text': 'So when you have data stored in multiple tables, then you cannot do filtering and sorting of data.', 'start': 995.608, 'duration': 4.981}], 'summary': 'Olap activities simplify analysis with efficient filtering and sorting of data.', 'duration': 25.662, 'max_score': 974.927, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CHYPF7jxlik/pics/CHYPF7jxlik974927.jpg'}, {'end': 1106.171, 'src': 'heatmap', 'start': 1068.156, 'weight': 0.765, 'content': [{'end': 1073.097, 'text': 'So the first one is MOLAP, ROLAP and HOLAP, okay?', 'start': 1068.156, 'duration': 4.941}, {'end': 1074.517, 'text': 'So we also call it MOLAP.', 'start': 1073.117, 'duration': 1.4}, {'end': 1076.018, 'text': 'MOLAP or MOLAP.', 'start': 1074.777, 'duration': 1.241}, {'end': 1079.599, 'text': 'So this stands for multi-dimensional online analytical processing.', 'start': 1076.238, 'duration': 3.361}, {'end': 1088.042, 'text': 'ROLAP stands for relational online analytical processing and whole app stands for hybrid online analytical processing.', 'start': 1080.139, 'duration': 7.903}, {'end': 1089.483, 'text': 'all right.', 'start': 1088.042, 'duration': 1.441}, {'end': 1091.764, 'text': 'so guys, the whole topic here is olap cubes.', 'start': 1089.483, 'duration': 2.281}, {'end': 1095.006, 'text': 'right, so your olap cubes is where your data will be stored.', 'start': 1091.764, 'duration': 3.242}, {'end': 1098.227, 'text': "okay, now, the analysis that you'll do, the kind of queries that you'll run,", 'start': 1095.006, 'duration': 3.221}, {'end': 1102.269, 'text': 'they will all be olap queries and they will all be on the multi-dimensional data.', 'start': 1098.227, 'duration': 4.042}, {'end': 1106.171, 'text': "okay, so the data that's going to be stored inside your cubes is going to be multi-dimensional data.", 'start': 1102.269, 'duration': 3.902}], 'summary': 'Molap, rolap, holap are types of olap for multi-dimensional data storage and analysis.', 'duration': 38.015, 'max_score': 1068.156, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CHYPF7jxlik/pics/CHYPF7jxlik1068156.jpg'}, {'end': 1153.112, 'src': 'embed', 'start': 1128.838, 'weight': 3, 'content': [{'end': 1134.88, 'text': 'So here, MOLAP is a form of OLAP that processes and stores data directly into a multi-dimensional database.', 'start': 1128.838, 'duration': 6.042}, {'end': 1139.803, 'text': 'So you have a multi-dimensional cube and then your data will be stored inside that particular database.', 'start': 1135.28, 'duration': 4.523}, {'end': 1145.227, 'text': 'Now the advantage here is that it will give you excellent performance and it can perform complex calculations.', 'start': 1140.143, 'duration': 5.084}, {'end': 1149.57, 'text': 'Okay, but the problem is only limited amount of data can be handled in your molab.', 'start': 1145.467, 'duration': 4.103}, {'end': 1153.112, 'text': "Okay, but then there's a difference between molab and olab.", 'start': 1149.93, 'duration': 3.182}], 'summary': 'Molap stores data in multi-dimensional database for excellent performance, but handles limited amount of data.', 'duration': 24.274, 'max_score': 1128.838, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CHYPF7jxlik/pics/CHYPF7jxlik1128838.jpg'}, {'end': 1328.309, 'src': 'embed', 'start': 1300.354, 'weight': 5, 'content': [{'end': 1302.636, 'text': 'And then these are the two basic differences.', 'start': 1300.354, 'duration': 2.282}, {'end': 1305.838, 'text': "and then you have a third one, that's called as a hybrid OLAP, right?", 'start': 1302.636, 'duration': 3.202}, {'end': 1310.722, 'text': 'So your hybrid OLAP is basically a combination of both your MOLAP and your ROLAP.', 'start': 1306.159, 'duration': 4.563}, {'end': 1315.206, 'text': 'So the positives and your advantages of both is used in your hybrid OLAP.', 'start': 1310.742, 'duration': 4.464}, {'end': 1322.745, 'text': 'So the advantage with OLAP is that OLAP can drill through from the cube into the underlying relational data right?', 'start': 1316.021, 'duration': 6.724}, {'end': 1328.309, 'text': 'So what it means is you will have your cube here and you will have the underlying relational data right?', 'start': 1323.026, 'duration': 5.283}], 'summary': 'Hybrid olap combines advantages of molap and rolap for improved data analysis.', 'duration': 27.955, 'max_score': 1300.354, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CHYPF7jxlik/pics/CHYPF7jxlik1300354.jpg'}, {'end': 1428.635, 'src': 'embed', 'start': 1401.697, 'weight': 7, 'content': [{'end': 1405.32, 'text': 'So the next topic that we have for today is that of OLAP operations.', 'start': 1401.697, 'duration': 3.623}, {'end': 1413.926, 'text': 'So we have five different operations and they are roll up, drill down, slice, dice, and pivot.', 'start': 1406.2, 'duration': 7.726}, {'end': 1419.93, 'text': 'So these are the five different OLAP operations that we can do on our OLAP multidimensional data.', 'start': 1414.886, 'duration': 5.044}, {'end': 1423.752, 'text': 'Going to our first operation that is rollup.', 'start': 1420.55, 'duration': 3.202}, {'end': 1428.635, 'text': "Now before I explain this, let me tell you why I'm explaining operations first of all.", 'start': 1424.092, 'duration': 4.543}], 'summary': 'Introduction to five olap operations: roll up, drill down, slice, dice, pivot.', 'duration': 26.938, 'max_score': 1401.697, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CHYPF7jxlik/pics/CHYPF7jxlik1401697.jpg'}, {'end': 1469.474, 'src': 'heatmap', 'start': 1385.271, 'weight': 6, 'content': [{'end': 1387.892, 'text': 'short question in any of your interviews, right?', 'start': 1385.271, 'duration': 2.621}, {'end': 1394.614, 'text': 'So any job interview regarding data warehousing and all these things, you will be asked and you should know all these things, right?', 'start': 1387.912, 'duration': 6.702}, {'end': 1398.215, 'text': "So that's the differences and the types of OLAP cubes.", 'start': 1394.974, 'duration': 3.241}, {'end': 1400.877, 'text': 'So moving on to the next slide.', 'start': 1398.835, 'duration': 2.042}, {'end': 1405.32, 'text': 'So the next topic that we have for today is that of OLAP operations.', 'start': 1401.697, 'duration': 3.623}, {'end': 1413.926, 'text': 'So we have five different operations and they are roll up, drill down, slice, dice, and pivot.', 'start': 1406.2, 'duration': 7.726}, {'end': 1419.93, 'text': 'So these are the five different OLAP operations that we can do on our OLAP multidimensional data.', 'start': 1414.886, 'duration': 5.044}, {'end': 1423.752, 'text': 'Going to our first operation that is rollup.', 'start': 1420.55, 'duration': 3.202}, {'end': 1428.635, 'text': "Now before I explain this, let me tell you why I'm explaining operations first of all.", 'start': 1424.092, 'duration': 4.543}, {'end': 1438.34, 'text': "So I'm explaining OLAP operations so that you can understand what are the kind of operations that you can do on your data warehousing and things that you cannot do on your database.", 'start': 1428.655, 'duration': 9.685}, {'end': 1444.664, 'text': 'So with the help of OLAP and since data is stored in such a multi-dimensional fashion, these kind of operations can be performed.', 'start': 1438.38, 'duration': 6.284}, {'end': 1447.606, 'text': "So first of all, let's get started with our rollup operation.", 'start': 1444.884, 'duration': 2.722}, {'end': 1450.287, 'text': "So let's read the definition first.", 'start': 1448.166, 'duration': 2.121}, {'end': 1457.949, 'text': 'So rollup is something that forms aggregation or data cube by either climbing up a concept hierarchy for a dimension or for a dimension reduction.', 'start': 1450.347, 'duration': 7.602}, {'end': 1464.451, 'text': "Okay, so when we say climbing up a concept hierarchy for a dimension, it basically means you'll have a particular dimension here, right?", 'start': 1458.269, 'duration': 6.182}, {'end': 1469.474, 'text': 'So in this case we have the dimension of Cities, so we have four different cities.', 'start': 1464.471, 'duration': 5.003}], 'summary': 'Explanation of olap operations and types of olap cubes in data warehousing.', 'duration': 84.203, 'max_score': 1385.271, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CHYPF7jxlik/pics/CHYPF7jxlik1385271.jpg'}], 'start': 750.742, 'title': 'Data warehousing concepts and olap cubes', 'summary': 'Explains olap, dimensions, facts, schemas, olap vs. oltp, advantages of olap, and modeling data warehouse. it discusses molap, rolap, holap, and olap operations - roll up, drill down, slice, dice, pivot, with a focus on explaining rollup operation and its impact on data hierarchy and dimensions.', 'chapters': [{'end': 1014.592, 'start': 750.742, 'title': 'Data warehousing concepts', 'summary': 'Explains the concepts of olap, dimensions, facts, and schemas in data warehousing, highlighting the difference between olap and oltp, the advantages of olap, and the modeling of data warehouse and database on olap and oltp concepts.', 'duration': 263.85, 'highlights': ['The OLAP concept in data warehousing involves the storage of multidimensional data in the form of OLAP cubes, enabling analysis from multiple views, dimensions, and categories, making analysis and insights simpler with activities like filtering and sorting.', 'Data warehousing is modeled on the concept of OLAP, storing data in a multi-dimensional form, and running OLAP queries on cubes, while databases are modeled on the concept of OLTP, using two-dimensional tables for data storage.', 'The advantages of OLAP over OLTP include the ability to view data from multiple angles, gaining new insights and making analysis easier, supported by activities like filtering and sorting of data.']}, {'end': 1538.686, 'start': 1014.872, 'title': 'Olap cubes and operations', 'summary': 'Discusses the differences between olap and oltp, the types of olap cubes (molap, rolap, holap), and olap operations (roll up, drill down, slice, dice, pivot) used for multidimensional analysis and queries, with a focus on explaining rollup operation and its impact on data hierarchy and dimensions.', 'duration': 523.814, 'highlights': ['MOLAP is a form of OLAP that processes and stores data directly into a multi-dimensional database, offering excellent performance and complex calculations but limited data handling capacity. MOLAP offers excellent performance and complex calculations but has limited data handling capacity.', 'ROLAP is a form of OLAP that performs dynamic multidimensional analysis of data stored in a relational database, allowing for greater data processing but requiring more processing time and disk space than MOLAP. ROLAP allows for greater data processing but requires more processing time and disk space than MOLAP.', 'Hybrid OLAP combines the advantages of both MOLAP and ROLAP, enabling drill through from the cube into the underlying relational data for efficient multidimensional analysis. Hybrid OLAP combines the advantages of MOLAP and ROLAP and enables efficient multidimensional analysis.', 'The chapter also focuses on explaining the rollup operation, which involves climbing up a concept hierarchy for a dimension or dimension reduction, and provides examples of its impact on data hierarchy and dimensions. The chapter explains the rollup operation and provides examples of its impact on data hierarchy and dimensions.', 'The chapter introduces five OLAP operations: roll up, drill down, slice, dice, and pivot, which are used for multidimensional analysis and queries on OLAP data. The chapter introduces the five OLAP operations used for multidimensional analysis and queries on OLAP data.']}], 'duration': 787.944, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CHYPF7jxlik/pics/CHYPF7jxlik750742.jpg', 'highlights': ['The OLAP concept in data warehousing involves the storage of multidimensional data in the form of OLAP cubes, enabling analysis from multiple views, dimensions, and categories.', 'Data warehousing is modeled on the concept of OLAP, storing data in a multi-dimensional form, and running OLAP queries on cubes, while databases are modeled on the concept of OLTP, using two-dimensional tables for data storage.', 'The advantages of OLAP over OLTP include the ability to view data from multiple angles, gaining new insights and making analysis easier, supported by activities like filtering and sorting of data.', 'MOLAP is a form of OLAP that processes and stores data directly into a multi-dimensional database, offering excellent performance and complex calculations but limited data handling capacity.', 'ROLAP is a form of OLAP that performs dynamic multidimensional analysis of data stored in a relational database, allowing for greater data processing but requiring more processing time and disk space than MOLAP.', 'Hybrid OLAP combines the advantages of both MOLAP and ROLAP, enabling drill through from the cube into the underlying relational data for efficient multidimensional analysis.', 'The chapter also focuses on explaining the rollup operation, which involves climbing up a concept hierarchy for a dimension or dimension reduction, and provides examples of its impact on data hierarchy and dimensions.', 'The chapter introduces five OLAP operations: roll up, drill down, slice, dice, and pivot, which are used for multidimensional analysis and queries on OLAP data.']}, {'end': 1967.182, 'segs': [{'end': 1640.199, 'src': 'heatmap', 'start': 1597.597, 'weight': 0, 'content': [{'end': 1602.059, 'text': 'So we are doing a roll-up on the items based on any particular dimension, right?', 'start': 1597.597, 'duration': 4.462}, {'end': 1603.6, 'text': 'this dimension.', 'start': 1602.979, 'duration': 0.621}, {'end': 1609.143, 'text': 'we aggregated a part of this dimension and we converted this dimension from my cities to country.', 'start': 1603.6, 'duration': 5.543}, {'end': 1614.447, 'text': 'so this is the kind of operations that you can do on the set of attributes that will be present inside your dimensions.', 'start': 1609.143, 'duration': 5.304}, {'end': 1616.388, 'text': "so that's about the roll up.", 'start': 1614.447, 'duration': 1.941}, {'end': 1620.371, 'text': 'okay, now, the next operation that I want to talk about is that of a drill down.', 'start': 1616.388, 'duration': 3.983}, {'end': 1623.873, 'text': 'now, drill down is something that is just the reverse of for roll up.', 'start': 1620.371, 'duration': 3.502}, {'end': 1632.297, 'text': "so what we did in roll up was we aggregated set of attributes, right, so let's break down the entire Attribute into smaller attributes, all right.", 'start': 1623.873, 'duration': 8.424}, {'end': 1640.199, 'text': 'so we can do that by there stepping down a concept hierarchy for a dimension, Okay, and by also introducing a new dimension.', 'start': 1632.297, 'duration': 7.902}], 'summary': 'Explaining roll-up and drill down operations for dimension attributes.', 'duration': 34.7, 'max_score': 1597.597, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CHYPF7jxlik/pics/CHYPF7jxlik1597597.jpg'}, {'end': 1882.518, 'src': 'heatmap', 'start': 1823.199, 'weight': 3, 'content': [{'end': 1827.341, 'text': "and since we've drilled down into the quarter and since we've taken time as reference,", 'start': 1823.199, 'duration': 4.142}, {'end': 1831.083, 'text': "we've replaced the time dimension here with the location dimension.", 'start': 1827.341, 'duration': 3.742}, {'end': 1836.587, 'text': 'So this comes to the y-axis, and the whole representation here is only with respect to q1.', 'start': 1831.484, 'duration': 5.103}, {'end': 1839.989, 'text': "So that's what you mean with respect to slice.", 'start': 1837.307, 'duration': 2.682}, {'end': 1847.153, 'text': 'So you slice one particular dimension and you get into the details of that particular dimension with respect to the other dimensions.', 'start': 1840.409, 'duration': 6.744}, {'end': 1849.734, 'text': "so that's the slice operation.", 'start': 1847.153, 'duration': 2.581}, {'end': 1852.775, 'text': 'and then, moving on to the next slide, we have our dice right.', 'start': 1849.734, 'duration': 3.041}, {'end': 1858.498, 'text': 'so the dice operation provides the new sub cube from two or more dimensions in a given cube, right.', 'start': 1852.775, 'duration': 5.723}, {'end': 1862.439, 'text': 'so in the earlier example we saw you know, we saw slice right.', 'start': 1858.498, 'duration': 3.941}, {'end': 1864.48, 'text': 'so slice what it does, is it?', 'start': 1862.439, 'duration': 2.041}, {'end': 1869.262, 'text': 'you know, gives us a new sub cube from one by using one particular dimension in a given cube.', 'start': 1864.48, 'duration': 4.782}, {'end': 1874.685, 'text': 'okay, but the dice here it gives us a new sub cube from two or more dimensions in a given cube.', 'start': 1869.262, 'duration': 5.423}, {'end': 1882.518, 'text': 'So if you look at this example again, we have the location dimension, we have the time dimension and we have our items dimension here, correct?', 'start': 1875.265, 'duration': 7.253}], 'summary': 'Explaining slice and dice operations in data analysis with examples.', 'duration': 42.109, 'max_score': 1823.199, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CHYPF7jxlik/pics/CHYPF7jxlik1823199.jpg'}], 'start': 1538.686, 'title': 'Data analysis and olap operations', 'summary': 'Explores roll-up and drill-down concepts in data analysis, with an example of converting cities into countries and aggregating sales for usa and canada. it also details olap operations, highlighting the differences between roll-up and drill-down, and providing examples and explanations for slice and dice operations in olap cubes.', 'chapters': [{'end': 1632.297, 'start': 1538.686, 'title': 'Roll-up and drill-down in data analysis', 'summary': 'Explains the concepts of roll-up and drill-down in data analysis, with an example of converting cities into countries and the resulting aggregation of sales for usa and canada.', 'duration': 93.611, 'highlights': ['The roll-up operation involves converting dimensions with a different set of attributes, such as converting cities into countries, resulting in the aggregation of sales for USA and Canada.', 'The drill down operation involves breaking down the entire attribute into smaller attributes, which is the reverse of roll-up.']}, {'end': 1967.182, 'start': 1632.297, 'title': 'Olap operations: roll-up, drill-down, slice, dice', 'summary': 'Explains olap operations including roll-up, drill-down, slice, and dice. it details the difference between roll-up and drill-down operations, and provides examples and explanations for slice and dice operations in olap cubes.', 'duration': 334.885, 'highlights': ['The chapter explains the difference between roll-up and drill-down operations on dimensions, demonstrating how a roll-up reduces the number of attributes while a drill-down increases the number of attributes. Roll-up operation reduces the number of attributes from 12 to 2 in the dimension of cities, while the drill-down operation increases the number of attributes from 4 to 12 in the dimension of time.', 'It provides an example and explanation of the slice operation, which creates a new subcube by breaking down one particular dimension in a given cube, such as slicing the time dimension to focus on quarter one. Slice operation is demonstrated by focusing on quarter one in a two-dimensional cube after replacing the time dimension with the location dimension.', 'The chapter also describes the dice operation, which creates a new subcube by using two or more dimensions in a given cube, and provides an example of dicing for location, time, and items dimensions. Dice operation is exemplified by focusing on specific locations, quarters, and items to create a new subcube with limited dimensions.']}], 'duration': 428.496, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CHYPF7jxlik/pics/CHYPF7jxlik1538686.jpg', 'highlights': ['The roll-up operation involves converting dimensions with a different set of attributes, such as converting cities into countries, resulting in the aggregation of sales for USA and Canada.', 'The drill down operation involves breaking down the entire attribute into smaller attributes, which is the reverse of roll-up.', 'The chapter explains the difference between roll-up and drill-down operations on dimensions, demonstrating how a roll-up reduces the number of attributes while a drill-down increases the number of attributes.', 'It provides an example and explanation of the slice operation, which creates a new subcube by breaking down one particular dimension in a given cube, such as slicing the time dimension to focus on quarter one.', 'The chapter also describes the dice operation, which creates a new subcube by using two or more dimensions in a given cube, and provides an example of dicing for location, time, and items dimensions.']}, {'end': 3143.094, 'segs': [{'end': 2084.199, 'src': 'heatmap', 'start': 1967.182, 'weight': 0, 'content': [{'end': 1969.724, 'text': 'and then finally, we have one operation called as pivot operation.', 'start': 1967.182, 'duration': 2.542}, {'end': 1973.104, 'text': 'So the pivot operation is also known as the rotation operation.', 'start': 1970.123, 'duration': 2.981}, {'end': 1980.345, 'text': "It basically transposes both the axis, whether it's the X and Y axis, it transposes them in order to provide an alternative presentation of data.", 'start': 1973.404, 'duration': 6.941}, {'end': 1987.307, 'text': 'Now if you look at this example here, we have the location dimension on the Y axis and we have the item dimension on the X axis.', 'start': 1980.686, 'duration': 6.621}, {'end': 1989.388, 'text': 'Now these two are transposed.', 'start': 1987.587, 'duration': 1.801}, {'end': 1990.948, 'text': "Now that's what a pivot does.", 'start': 1989.668, 'duration': 1.28}, {'end': 1996.009, 'text': 'So when you transpose them, your item comes here and your location, it comes over here.', 'start': 1991.068, 'duration': 4.941}, {'end': 1998.83, 'text': "Your location dimension's here and your item dimension is here.", 'start': 1996.189, 'duration': 2.641}, {'end': 2002.471, 'text': 'So even your data corresponding to them are transposed.', 'start': 1999.45, 'duration': 3.021}, {'end': 2006.192, 'text': 'So I hope you guys got the whole concept of operations.', 'start': 2002.971, 'duration': 3.221}, {'end': 2010.393, 'text': 'So the five different operations which can be done with the help of OLAP data warehouse.', 'start': 2006.412, 'duration': 3.981}, {'end': 2013.394, 'text': 'So I hope you guys got the entire concept here.', 'start': 2011.113, 'duration': 2.281}, {'end': 2020.816, 'text': 'So the kind of operations that you can do using your OLAP activities on your data warehouse.', 'start': 2013.694, 'duration': 7.122}, {'end': 2023.357, 'text': "So that's the thing here.", 'start': 2021.416, 'duration': 1.941}, {'end': 2025.237, 'text': 'So these were the five different OLAP operations.', 'start': 2023.377, 'duration': 1.86}, {'end': 2032.589, 'text': "And moving on to the next slide, So the next topic that I'm gonna talk about is that of dimensions.", 'start': 2025.877, 'duration': 6.712}, {'end': 2038.414, 'text': 'So the tables that describe the dimensions involved are called dimension tables.', 'start': 2032.889, 'duration': 5.525}, {'end': 2044.018, 'text': 'So first of all, to give you an example of what a dimension is, in database you have something called as tables.', 'start': 2038.794, 'duration': 5.224}, {'end': 2047.061, 'text': 'So you have different tables which will be a part of your database.', 'start': 2044.239, 'duration': 2.822}, {'end': 2050.904, 'text': 'Now similar to tables, we have something called as dimensions in a data warehouse.', 'start': 2047.321, 'duration': 3.583}, {'end': 2054.105, 'text': 'so we have dimension tables which will have a set of attributes.', 'start': 2051.123, 'duration': 2.982}, {'end': 2061.786, 'text': 'so our customer dimension will have the customer details, like the customers ID, the customer name and the customer address okay,', 'start': 2054.105, 'duration': 7.681}, {'end': 2067.829, 'text': 'and your product or dimension will have other sort of attributes like your product ID, product name and your product type.', 'start': 2061.786, 'duration': 6.043}, {'end': 2072.69, 'text': 'and similarly you will have a date dimension which will have the order date, the shipment date and delivery date.', 'start': 2067.829, 'duration': 4.861}, {'end': 2075.273, 'text': 'So each of these dimensions.', 'start': 2073.992, 'duration': 1.281}, {'end': 2078.655, 'text': 'here they talk about a different aspect of your entire data warehouse.', 'start': 2075.273, 'duration': 3.382}, {'end': 2084.199, 'text': 'So your data warehouse may be one for, in this example, it is that of e-commerce company.', 'start': 2078.935, 'duration': 5.264}], 'summary': 'Olap operations include pivot, rotation, and transposing dimensions for alternative data presentation in a data warehouse.', 'duration': 56.175, 'max_score': 1967.182, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CHYPF7jxlik/pics/CHYPF7jxlik1967182.jpg'}, {'end': 2268.227, 'src': 'heatmap', 'start': 2109.214, 'weight': 0.861, 'content': [{'end': 2117.579, 'text': 'the answer to that is dividing your entire data warehouse project into dimensions provides structured information for analysis and reporting, right,', 'start': 2109.214, 'duration': 8.365}, {'end': 2123.029, 'text': 'and So when you break down your entire data warehouse into different dimensions, like for your customer details,', 'start': 2117.579, 'duration': 5.45}, {'end': 2125.41, 'text': "you'll have a separate dimension called as customer dimension.", 'start': 2123.029, 'duration': 2.381}, {'end': 2129.191, 'text': 'For your product details, you can have a separate dimension called product dimension.', 'start': 2125.45, 'duration': 3.741}, {'end': 2132.372, 'text': 'And similarly for other things, you can have other different dimensions.', 'start': 2129.571, 'duration': 2.801}, {'end': 2137.294, 'text': 'And when you break it down in a structured form like this, your analysis and reporting can be very easier.', 'start': 2132.512, 'duration': 4.782}, {'end': 2141.877, 'text': 'why? because when you store data it will be in this form right.', 'start': 2137.914, 'duration': 3.963}, {'end': 2143.358, 'text': 'so you will have your customer here.', 'start': 2141.877, 'duration': 1.481}, {'end': 2149.822, 'text': 'you will have your customer ID, the name address and the product that particular person purchased, the name of the product, the idea of that product,', 'start': 2143.358, 'duration': 6.464}, {'end': 2156.886, 'text': 'the type of the product and then the date as to when it was ordered, when was it shipped and when was the delivery completed.', 'start': 2149.822, 'duration': 7.064}, {'end': 2159.428, 'text': 'so you have all these details in a structured format.', 'start': 2156.886, 'duration': 2.542}, {'end': 2162.59, 'text': "so that's what the benefit that dimensions and data warehouse gives us.", 'start': 2159.428, 'duration': 3.162}, {'end': 2168.092, 'text': 'So end users can simply fire queries on this dimension tables which contain descriptive information.', 'start': 2163.51, 'duration': 4.582}, {'end': 2176.736, 'text': "So you'll be here as an end user and you can just fire your simple queries on your data warehouse and you will get the answer that you need.", 'start': 2168.632, 'duration': 8.104}, {'end': 2181.117, 'text': "So that's what is the use of having dimensions and that's the benefit.", 'start': 2177.316, 'duration': 3.801}, {'end': 2189.741, 'text': "So any doubts here guys? That's about the dimensions and that's why we should have different dimensions in our data warehouse project.", 'start': 2181.698, 'duration': 8.043}, {'end': 2192.002, 'text': 'So moving on to the next slide.', 'start': 2190.281, 'duration': 1.721}, {'end': 2196.806, 'text': 'So the next topic that we have here is that of facts and measures.', 'start': 2192.842, 'duration': 3.964}, {'end': 2197.066, 'text': 'all right?', 'start': 2196.806, 'duration': 0.26}, {'end': 2200.65, 'text': 'Now, in the previous slide, I spoke about dimensions right?', 'start': 2197.667, 'duration': 2.983}, {'end': 2209.499, 'text': 'So we spoke about there being different dimensions in our data warehouse and then we being able to sort them, filter them up using different queries,', 'start': 2200.67, 'duration': 8.829}, {'end': 2212.302, 'text': 'right?. Now, how do you think you can run your queries and result?', 'start': 2209.499, 'duration': 2.803}, {'end': 2213.884, 'text': 'Because by the look of this table,', 'start': 2212.322, 'duration': 1.562}, {'end': 2220.129, 'text': 'you might not be able to get an idea of what kind of dimensions you have to filter or which kind of dimensions here to sort.', 'start': 2214.324, 'duration': 5.805}, {'end': 2225.333, 'text': "So you have that kind of doubt, right? So that's where the whole concept of facts comes into picture.", 'start': 2220.509, 'duration': 4.824}, {'end': 2226.394, 'text': 'Facts and measures.', 'start': 2225.573, 'duration': 0.821}, {'end': 2229.756, 'text': 'So fact is something that helps you measure your dimensions.', 'start': 2226.854, 'duration': 2.902}, {'end': 2232.258, 'text': "So what it does, let's look at the definition first.", 'start': 2230.036, 'duration': 2.222}, {'end': 2236.722, 'text': 'A fact is a measure that can be summed, averaged, or manipulated.', 'start': 2232.538, 'duration': 4.184}, {'end': 2239.864, 'text': 'Okay and every fact table.', 'start': 2237.262, 'duration': 2.602}, {'end': 2243.387, 'text': 'it contains two kinds of data a dimension key and a measure.', 'start': 2239.864, 'duration': 3.523}, {'end': 2243.887, 'text': 'All right.', 'start': 2243.387, 'duration': 0.5}, {'end': 2250.552, 'text': 'now, what this means is all you will have all your dimensions in your data warehouse, but if you want to form any kind of query,', 'start': 2243.887, 'duration': 6.665}, {'end': 2257.557, 'text': 'like any sorting or analysis or Some kind of drill down or all these things, you can only do it if you have a fact.', 'start': 2250.552, 'duration': 7.005}, {'end': 2263.001, 'text': 'so for every dimension You will have an associated fact and using the fact, you can measure your dimensions.', 'start': 2257.557, 'duration': 5.444}, {'end': 2268.227, 'text': "Okay, so that's what it says here, and If you want to measure the data that you have in your data warehouse,", 'start': 2263.401, 'duration': 4.826}], 'summary': 'Dividing data warehouse into dimensions eases analysis and reporting, with associated facts for measuring dimensions.', 'duration': 159.013, 'max_score': 2109.214, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CHYPF7jxlik/pics/CHYPF7jxlik2109214.jpg'}, {'end': 2159.428, 'src': 'embed', 'start': 2132.512, 'weight': 3, 'content': [{'end': 2137.294, 'text': 'And when you break it down in a structured form like this, your analysis and reporting can be very easier.', 'start': 2132.512, 'duration': 4.782}, {'end': 2141.877, 'text': 'why? because when you store data it will be in this form right.', 'start': 2137.914, 'duration': 3.963}, {'end': 2143.358, 'text': 'so you will have your customer here.', 'start': 2141.877, 'duration': 1.481}, {'end': 2149.822, 'text': 'you will have your customer ID, the name address and the product that particular person purchased, the name of the product, the idea of that product,', 'start': 2143.358, 'duration': 6.464}, {'end': 2156.886, 'text': 'the type of the product and then the date as to when it was ordered, when was it shipped and when was the delivery completed.', 'start': 2149.822, 'duration': 7.064}, {'end': 2159.428, 'text': 'so you have all these details in a structured format.', 'start': 2156.886, 'duration': 2.542}], 'summary': 'Structured data storage enables easy analysis and reporting for customer details, including customer id, name, address, product purchased, product details, and order/shipping/delivery dates.', 'duration': 26.916, 'max_score': 2132.512, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CHYPF7jxlik/pics/CHYPF7jxlik2132512.jpg'}, {'end': 2263.001, 'src': 'embed', 'start': 2232.538, 'weight': 5, 'content': [{'end': 2236.722, 'text': 'A fact is a measure that can be summed, averaged, or manipulated.', 'start': 2232.538, 'duration': 4.184}, {'end': 2239.864, 'text': 'Okay and every fact table.', 'start': 2237.262, 'duration': 2.602}, {'end': 2243.387, 'text': 'it contains two kinds of data a dimension key and a measure.', 'start': 2239.864, 'duration': 3.523}, {'end': 2243.887, 'text': 'All right.', 'start': 2243.387, 'duration': 0.5}, {'end': 2250.552, 'text': 'now, what this means is all you will have all your dimensions in your data warehouse, but if you want to form any kind of query,', 'start': 2243.887, 'duration': 6.665}, {'end': 2257.557, 'text': 'like any sorting or analysis or Some kind of drill down or all these things, you can only do it if you have a fact.', 'start': 2250.552, 'duration': 7.005}, {'end': 2263.001, 'text': 'so for every dimension You will have an associated fact and using the fact, you can measure your dimensions.', 'start': 2257.557, 'duration': 5.444}], 'summary': 'Fact tables contain dimension keys and measures for analysis in data warehouse.', 'duration': 30.463, 'max_score': 2232.538, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CHYPF7jxlik/pics/CHYPF7jxlik2232538.jpg'}, {'end': 2341.557, 'src': 'embed', 'start': 2310.83, 'weight': 8, 'content': [{'end': 2315.897, 'text': "So it'll have one particular dimension key which will connect to the dimension and the results from here.", 'start': 2310.83, 'duration': 5.067}, {'end': 2319.201, 'text': 'what are the aggregates or what are the operations performed?', 'start': 2315.897, 'duration': 3.304}, {'end': 2320.923, 'text': 'the result of that will be stored as a measure.', 'start': 2319.201, 'duration': 1.722}, {'end': 2321.944, 'text': "So that's the thing.", 'start': 2321.283, 'duration': 0.661}, {'end': 2325.827, 'text': 'And it also says every dimension table is linked to a fact table, yes.', 'start': 2322.905, 'duration': 2.922}, {'end': 2326.868, 'text': "Now that's the thing.", 'start': 2326.247, 'duration': 0.621}, {'end': 2331.731, 'text': 'Now if you want to perform any kind of query or analysis on any dimension, then you need a fact table.', 'start': 2327.088, 'duration': 4.643}, {'end': 2335.313, 'text': "That's what we are talking about in the final line here.", 'start': 2332.091, 'duration': 3.222}, {'end': 2341.557, 'text': "It is only if you have a fact table can you perform any kind of queries or analysis, right? So that's the thing.", 'start': 2336.014, 'duration': 5.543}], 'summary': 'A fact table is essential for querying and analyzing any dimension in data analysis.', 'duration': 30.727, 'max_score': 2310.83, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CHYPF7jxlik/pics/CHYPF7jxlik2310830.jpg'}, {'end': 2371.969, 'src': 'embed', 'start': 2345.66, 'weight': 4, 'content': [{'end': 2352.663, 'text': 'So you cannot have any dimension table in your data warehouse without a fact table, and that is what is the whole process.', 'start': 2345.66, 'duration': 7.003}, {'end': 2359.165, 'text': 'that leads us to having a lot of flexibility with respect to querying and analysis and all these things, right.', 'start': 2352.663, 'duration': 6.502}, {'end': 2365.847, 'text': 'so take this example in your fact table, you will have one product dimension and then that will have a dimension key and a measure right.', 'start': 2359.165, 'duration': 6.682}, {'end': 2371.969, 'text': 'so your dimension key would be that of product id and your measure would be that of the number of units sold, right.', 'start': 2365.847, 'duration': 6.122}], 'summary': 'A fact table is essential for flexibility in querying and analysis, with an example of a product dimension linked to product id and units sold.', 'duration': 26.309, 'max_score': 2345.66, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CHYPF7jxlik/pics/CHYPF7jxlik2345660.jpg'}, {'end': 2425.626, 'src': 'heatmap', 'start': 2393.709, 'weight': 0.768, 'content': [{'end': 2398.535, 'text': 'Alright, so moving on to the next slide, we have that of schemas.', 'start': 2393.709, 'duration': 4.826}, {'end': 2404.922, 'text': 'So if you remember what we spoke about here about dimensions, facts and measures,', 'start': 2399.296, 'duration': 5.626}, {'end': 2409.788, 'text': 'so these are the important topics that will help you understand the whole concept of schemas.', 'start': 2404.922, 'duration': 4.866}, {'end': 2417.421, 'text': 'So getting back to schemas, a schema is basically that which gives the logical description of your entire database.', 'start': 2410.717, 'duration': 6.704}, {'end': 2425.626, 'text': 'It gives details about the constraints placed on the tables, key values present, and how the key values are linked between the different tables.', 'start': 2418.362, 'duration': 7.264}], 'summary': 'Schemas provide logical description of database, including constraints and key value relationships.', 'duration': 31.917, 'max_score': 2393.709, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CHYPF7jxlik/pics/CHYPF7jxlik2393709.jpg'}, {'end': 2535.858, 'src': 'embed', 'start': 2509.638, 'weight': 7, 'content': [{'end': 2515.162, 'text': 'So, if I can recall, then over here I told you that a fact table contains two kinds of data a dimension key and a measure, all right?', 'start': 2509.638, 'duration': 5.524}, {'end': 2519.826, 'text': 'And I told you that every dimension over here is supposed to have a fact table.', 'start': 2515.542, 'duration': 4.284}, {'end': 2524.629, 'text': "It's supposed to be linked to a fact table and it would be linked with the help of schemas, okay?", 'start': 2519.886, 'duration': 4.743}, {'end': 2527.312, 'text': 'So that is the relationship between dimensions and facts.', 'start': 2524.87, 'duration': 2.442}, {'end': 2535.858, 'text': 'Measures are something that is going to aggregate the data that is present in your dimensions and it will store it again in the fact table.', 'start': 2528.272, 'duration': 7.586}], 'summary': 'Fact table contains dimension key and measure, linked with schemas. measures aggregate data from dimensions.', 'duration': 26.22, 'max_score': 2509.638, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CHYPF7jxlik/pics/CHYPF7jxlik2509638.jpg'}, {'end': 3060.592, 'src': 'embed', 'start': 3028.406, 'weight': 6, 'content': [{'end': 3032.991, 'text': 'All right, and Those are the three different schemas that you can have in your data warehouse.', 'start': 3028.406, 'duration': 4.585}, {'end': 3036.354, 'text': "All right, so if you're gonna work on a data warehouse anytime in your future,", 'start': 3032.991, 'duration': 3.363}, {'end': 3043.662, 'text': 'then you can formulate your whole Organizations data into a data warehouse by using any of these schemas.', 'start': 3036.354, 'duration': 7.308}, {'end': 3051.824, 'text': "Okay, they are either a star snowflake or galaxy schemas So that's it about the schemas And yeah, that's it.", 'start': 3043.962, 'duration': 7.862}, {'end': 3053.365, 'text': "So that's the end of the session.", 'start': 3052.004, 'duration': 1.361}, {'end': 3060.592, 'text': "And let me just summarize what I've covered in this session, all right? So I first gave you an introduction to what is data warehousing.", 'start': 3053.826, 'duration': 6.766}], 'summary': 'Introduction to data warehousing and three different schemas: star, snowflake, and galaxy.', 'duration': 32.186, 'max_score': 3028.406, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CHYPF7jxlik/pics/CHYPF7jxlik3028406.jpg'}], 'start': 1967.182, 'title': 'Data analysis operations', 'summary': 'Covers pivot operations for data transposition, olap essentials in data warehousing, and database schema understanding, emphasizing the benefits of structured information for analysis and reporting, and the interconnection between dimensions and fact tables, enabling flexible querying and analysis.', 'chapters': [{'end': 1998.83, 'start': 1967.182, 'title': 'Pivot operation in data analysis', 'summary': 'Discusses the pivot operation, also known as the rotation operation, which transposes the x and y axis to provide an alternative presentation of data, with an example showcasing the transposition of location and item dimensions.', 'duration': 31.648, 'highlights': ['The pivot operation, also known as the rotation operation, transposes both the X and Y axis to provide an alternative presentation of data.', 'An example is provided to showcase the transposition of location and item dimensions, demonstrating the effect of the pivot operation.']}, {'end': 2404.922, 'start': 1999.45, 'title': 'Essentials of olap in data warehousing', 'summary': 'Provides an overview of olap operations, dimensions, facts, and measures in data warehousing, emphasizing the benefits of structured information for analysis and reporting, and the interconnection between dimensions and fact tables, enabling flexible querying and analysis.', 'duration': 405.472, 'highlights': ['OLAP operations are essential for data warehousing The chapter introduces five different OLAP operations and their significance in data warehousing, emphasizing their importance in the data analysis process.', 'Importance of structured information for analysis and reporting The structured format of dimension tables and the interconnection with fact tables enable easier analysis and reporting, providing a clear benefit for end users.', 'Interconnection between dimensions and fact tables The necessity of a fact table for querying and analysis on any dimension, and the rule of having a fact table for every dimension table, providing flexibility in data analysis.', 'Definition and role of facts and measures The definition and significance of facts as measures that can be summed, averaged, or manipulated, and the role of measures in calculating and storing data related to dimensions in the fact tables.']}, {'end': 3143.094, 'start': 2404.922, 'title': 'Understanding database schemas', 'summary': 'Explains the concept of schemas in both databases and data warehouses, covering the types of schemas (star, snowflake, and galaxy), their key features, and the relationship between dimensions and facts, emphasizing the importance of linking dimension tables to fact tables.', 'duration': 738.172, 'highlights': ['The chapter explains the concept of schemas in both databases and data warehouses, covering the types of schemas (star, snowflake, and galaxy), their key features, and the relationship between dimensions and facts. The chapter provides an overview of schemas in databases and data warehouses, including the types of schemas (star, snowflake, and galaxy) and their key features.', 'Emphasizing the importance of linking dimension tables to fact tables. The chapter highlights the significance of linking dimension tables to fact tables in both databases and data warehouses.', 'Explaining the relationship between dimensions and facts, particularly focusing on the concept of measures and the aggregation of data in the fact table. The chapter delves into the relationship between dimensions and facts, emphasizing the concept of measures and the aggregation of data in the fact table.']}], 'duration': 1175.912, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CHYPF7jxlik/pics/CHYPF7jxlik1967182.jpg', 'highlights': ['The pivot operation transposes both the X and Y axis to provide an alternative presentation of data.', 'An example showcases the transposition of location and item dimensions, demonstrating the effect of the pivot operation.', 'OLAP operations are essential for data warehousing, emphasizing their importance in the data analysis process.', 'Importance of structured information for analysis and reporting, enabling easier analysis and reporting for end users.', 'Interconnection between dimensions and fact tables provides flexibility in data analysis.', 'The definition and significance of facts as measures that can be summed, averaged, or manipulated, and the role of measures in calculating and storing data related to dimensions in the fact tables.', 'The chapter explains the concept of schemas in both databases and data warehouses, covering the types of schemas (star, snowflake, and galaxy), their key features, and the relationship between dimensions and facts.', 'Emphasizing the importance of linking dimension tables to fact tables in both databases and data warehouses.', 'Explaining the relationship between dimensions and facts, particularly focusing on the concept of measures and the aggregation of data in the fact table.']}], 'highlights': ['The OLAP concept in data warehousing involves the storage of multidimensional data in the form of OLAP cubes, enabling analysis from multiple views, dimensions, and categories.', 'Data warehousing is modeled on the concept of OLAP, storing data in a multi-dimensional form, and running OLAP queries on cubes, while databases are modeled on the concept of OLTP, using two-dimensional tables for data storage.', 'The advantages of OLAP over OLTP include the ability to view data from multiple angles, gaining new insights and making analysis easier, supported by activities like filtering and sorting of data.', 'The chapter introduces five OLAP operations: roll up, drill down, slice, dice, and pivot, which are used for multidimensional analysis and queries on OLAP data.', 'The pivot operation transposes both the X and Y axis to provide an alternative presentation of data.', 'Importance of structured information for analysis and reporting, enabling easier analysis and reporting for end users.', 'The definition and significance of facts as measures that can be summed, averaged, or manipulated, and the role of measures in calculating and storing data related to dimensions in the fact tables.', 'Explaining the relationship between dimensions and facts, particularly focusing on the concept of measures and the aggregation of data in the fact table.', 'Data warehousing consolidates data from diverse locations and sources into a central repository for analysis and storage.', 'The process of data warehousing involves organizing and storing data to make its retrieval efficient and insightful, differentiating it from data stored in a database, with a focus on transforming data into information and serving a specific purpose.', 'The data warehousing architecture includes the stages of transferring data from different sources to the data warehouse through a staging area using the ETL process, categorizing data into raw data, metadata, and aggregate data, and allowing end users to perform analysis through OLAP queries.', 'The significance of metadata in data warehousing lies in providing information about the raw data, controlling attributes, and differentiating a data warehouse from a database, making it crucial for the functionality and purpose of the data warehouse.', 'OLAP queries enable end users to perform analysis on the data warehouse, categorized as analysis-based queries, while data marts serve as restricted access points within the data warehouse, allowing specific user groups to access relevant information based on their domain or field.', 'The chapter also focuses on explaining the rollup operation, which involves climbing up a concept hierarchy for a dimension or dimension reduction, and provides examples of its impact on data hierarchy and dimensions.', 'The roll-up operation involves converting dimensions with a different set of attributes, such as converting cities into countries, resulting in the aggregation of sales for USA and Canada.', 'The drill down operation involves breaking down the entire attribute into smaller attributes, which is the reverse of roll-up.', 'The chapter explains the difference between roll-up and drill-down operations on dimensions, demonstrating how a roll-up reduces the number of attributes while a drill-down increases the number of attributes.', 'It provides an example and explanation of the slice operation, which creates a new subcube by breaking down one particular dimension in a given cube, such as slicing the time dimension to focus on quarter one.', 'The chapter also describes the dice operation, which creates a new subcube by using two or more dimensions in a given cube, and provides an example of dicing for location, time, and items dimensions.', 'The instructor introduces the concept of OLAP in detail and mentions working with various OLAP cubes in data warehousing.', 'The chapter also includes a quick brief about data warehousing, data warehousing architecture, and the interrelation of dimensions, facts, and measures.', 'The instructor discusses the agenda for the session and encourages participants to interact by acknowledging their understanding and asking for doubts during the session.', 'The instructor emphasizes interaction with participants, urging them to acknowledge their understanding and ask doubts through the chat box for immediate clarification.']}