In this DP-900 lecture, we dive into the fundamentals of data analytics, a key topic in the Microsoft Azure Data Fundamentals certification. If you're preparing for the DP-900 exam or want to understand how data analytics works in Azure, this session covers essential concepts, tools, and real-world applications.
🔍 What You’ll Learn in This Session: ✅ Introduction to Data Analytics – Importance & Use Cases ✅ Key Phases of Data Analytics – Ingestion, Storage, Processing & Visualization ✅ Exploring Azure Data Analytics Services – Azure Synapse Analytics, Azure Data Lake, Power BI ✅ Batch vs. Stream Processing – How Data is Processed in Real-Time ✅ Fundamentals of Data Warehousing & ETL Pipelines ✅ Introduction to Business Intelligence (BI) with Azure
🛠️ Who Should Watch This? Beginners & IT professionals looking to understand data analytics Students & aspiring data professionals preparing for DP-900 certification Business analysts & decision-makers exploring data-driven insights Developers & cloud architects working with Azure data services
📌 Key Highlights: ✅ Simple & clear explanations of data analytics concepts ✅ Live demos of Azure Synapse, Power BI & Data Lake ✅ Real-world use cases & best practices ✅ DP-900 exam-focused insights 💡 Master non-relational data concepts & get ahead in cloud database management!
Explore Our Other Courses and Additional Resources on: https://skilltech.club/
00:00Now in this video lesson we are going to explore concepts of data analytics. We have seen
00:14transitional data processing and we have seen the non-relational and relational data but now
00:20data analytics is quite different than that. We will focus on data ingestion and data processing.
00:27As we have discussed earlier we have something called ETL ELT kind of processing which is
00:33associated with this. We will focus on that. We will explore data visualization and what is it
00:39all about and then finally we have to see types of data analytics so that we can know what kind
00:45of analytics we want to do at what time. Now let's focus on the data journey first which starts with
00:52data ingestion then it is going to data processing and then finally it's going to data visualization.
00:58Data ingestion is a process of importing data which might come from various resources like
01:05streaming or batch data processing. It can come from an IOT device or some kind of financial
01:12transactions or there are chances that you have already stored data on on-premise data sources
01:18or maybe some cloud data sources and then from there you want to fetch the data. Data ingestion
01:24is like you are providing data you are ingesting data into your database which can be relational or
01:30non-relational and then from those databases we have to fetch data and we have to do data processing.
01:37Data processing takes the data in the raw format it cleans it and then it's going to convert into a
01:44a much more meaningful format. Which actually covers two parts inside that we have ETL which
01:52stands for extract transform and load and then we have ELT which is like extract first load and
02:00then transform. What is the difference? Well in ETL the raw data is retrieved and transformed
02:07before being saved while in ELT the data is going to be retrieved and then it's going to be
02:14saved and then after that it's going to be transformed. The ETL extract transform and load steps can
02:23be performed as a continuous pipelines of operations. It is suitable for the systems that are required
02:29simple models with very little dependency between items while the ELT is something which is more
02:36suitable for constructing more complex models that depends on multiple items in the database and often using
02:44periodic batch processing. Finally after this data processing we have data visualization which is
02:51like a better representation or pictorial representation of your data so that you can understand data and
02:57analyze data properly. Data visualization is nothing but a graphical representation of your information
03:04and the data. Now obviously we have various tools available for data ingestion, data processing and data
03:10visualization and we have a very important tool to understand in this course which is Azure Synapse when
03:17we focus on these three parts. Now let's focus on data visualization little bit in depth. A business model can
03:24contain an enormous amount of information and there are techniques to analyze and understand the information
03:30information in your model. And mainly we focus on three different techniques reporting, business intelligence and data
03:38visualization. Reporting is a process of organizing data into an informational summary to monitor how the
03:46different areas of an organization are performing. Reporting shows you what was happen while analysis focus on explaining and why it happened and what you can do about it.
03:51In other hand, business intelligence refers to the technologies, applications and the practices for the collection, integration, analysis and presentation of the business information.
04:11Business intelligence system provides historical, current and predictive views of business operations.
04:19Most often using the data that has been gathered into a data warehouse and occasionally working from live operational data.
04:31So like reporting is something which can done with the live data also. Data visualization can also be done with that.
04:37But BI is something which is mostly going to be stored data in data warehouses or something similar to that kind of big data stores.
04:46Finally, the third one which is data visualization is a graphical representation. That's what we know of your information and your data.
04:54By using visual elements like charts, graphs, maps and other data visualization tools, you can get the proper insights from your data.
05:05It provides an accessible way to spot and understand the trends, outliners and patterns in the data.
05:13So that you can do the future prediction or some other further analysis on your data.
05:18Now finally, it's a time to see and explore our data analytics.
05:22Basically data analytics is divided into five parts.
05:26Descriptive analytics, diagnostic, predictive, perspective and cognitive.
05:34And obviously if you want to understand analytics properly, then you need to understand what types of analytics are there, which are five, which is visible on screen.
05:43As well as you also need to know what exactly each analytics is doing and at what time you're going to use this, in which scenario you're going to use which one.
05:53If we start with descriptive, descriptive is focusing on what is happening in a specific case, like what is happening in your business.
06:01If you want to figure out that what, then actually you have to do descriptive analytics.
06:07Then we have diagnostics, which is logically like the next step of that.
06:12Once you know what is happening, you have to figure out why is it happening and then to what I'm going to do about it.
06:18So, what you can do about it is something which you're going to mention inside a diagnostic analytics.
06:25Then we have predictive analytics, which is all about predicting about the future.
06:31So, what is likely to happen in the future based on the previous trends and patterns which you have seen in the previous analytics.
06:38You can do the predictive analytics based on that.
06:41And then once predictive analytics are done, you have perspective analytics, which determines the best course of action to choose to bypass or eliminate future issues.
06:53So, once you have done predictive analytics, you have recognized that there are some issues which can happen.
06:59Now, out of the available options, which one is going to be the best course of action, that you have to choose in perspective analytics.
07:07Which is again something like, you detected the problem in predictive analytics and now we are trying to fix it.
07:13After all this, we have a last one which is cognitive analytics, which is maybe one of the most complex out of all five.
07:20This type of analytics is inspired by how the human brain is processing information.
07:26It draws a conclusion and codifies the indistinct and experience into a learning such as understanding not only in the words and the text, but in the full context of what is being written or spoken.
07:39Most of the time in the cognitive analytics, we are going to take a help of some kind of machine learning algorithms and some kind of services are required, which can actually process the information like a real human brain.
07:52And then from the data which is provided into this analysis, it is going to take a logical and cognitive decisions like a human brain.
08:02And that is what which is one of the coolest thing about cognitive analytics.
08:06So, in this video, we have explored the concept of data analytics and then what kind of types and when exactly we have to do with kind of analytics.
08:15With this video, I think our first module is done.
08:19We understood various data formats and the options to store data as well as what kind of options we have to process our data and to get the proper analytics on that.
08:30Now, obviously, you have to understand all these things with the practical hands-on and in a much deeper way.
08:36And that's what we are going to do in the coming modules.
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