Speaker 1: Welcome, everyone. Today we are going to discuss about rendering millions of records quickly and use this capability to make real-time decisions. In this session, we will cover about the conventional industry challenges in handling huge amount of data, why speed matters in decision making, challenges involved in rendering larger data, and we will look at a practical example to discover the values hidden inside a data, some of the best practices for rendering, and advantages. All industries rely on their historical data to make business decisions. Nowadays, the volume of data usually reaches into millions or beyond. Here are some of the common challenges linked with the data. Data volume. Everything now moved to data, so most industries having data in lakhs, millions, or more. Increasing data becomes difficult to handle and process it efficiently. With huge data, aggregation, data modeling leads to performance issues and a difficulty in analysis. Larger data requires more time for processing that impacts the overall business performance. To prevent delay and clarity issues, the visualization elements are displayed with groups because huge data in chart becomes complicated to understand. The time it takes to fetch and analyze the large data impacts the ability to gain real-time insights. From the above discussed, the speed has a major importance in data analysis since everyone wish to view results quicker. Let's explore further about this. In data-driven world, speed with accurate visualization plays a crucial role because poor performance leads to poor results. Few points on why speed is important. User experience. Fast, efficient data rendering guarantees a smooth experience for the users that improves their interaction with the system. In sectors like finance, healthcare, logistics, where decisions need to be made in real-time, the quicker the data is visualized, the more efficient the decision can be made. Nobody likes to wait, especially when it comes to loading pages. Speedy data rendering reduces waiting time and increases the user satisfaction. When data is rendered quickly, it saves time that increases the productivity, whether an analyst examining through your data or executives making strategic decision. Now we understood the importance of speed, but it is not easier to achieve it with huge data. Next, we will look at the challenges in handling larger data. As the volume and complexity of data increases, rendering performance becomes slow. Some of the challenges are resource utilization. Larger data requires larger computational resource for processing and rendering. This leads to high resource utilization that impacts the performance of other tasks. Processing larger data involves intensive operations such as shorting, filtering, and aggregation. These operations are time consuming and expensive, which slow down the rendering process. Inconsistent or incomplete data leads to errors in decision making, so the quality of data is important. The cost of processing larger data is high, especially when specialized hardware is required. Balancing the speed with cost effectiveness is a key challenge. Finding the efficient techniques for rendering larger data is crucial. In the upcoming, we will discuss about on-demand rendering, which is faster, also scalable to handle large volume of data. We have discussed most of the required elements to analyze millions or record faster. Next, we will explore the real-time use cases. In most of the industries, real-time data processing is essential where every millisecond counts. Imagine an e-commerce website that receives millions of visitors or transactions every day. Each transaction generates data from user and product that result in massive volume of data. Also, it needs to be processed and visualized in real time. An analyst from the company creates a report to investigate and build business improvisation plans. Since the data is huge, he adds groups into the data query and inserts chart to the visualization and to understand the top-level business trends and patterns. From the pattern, he understands that specific area is affecting the business. Each user detailed data is hidden inside the chart due to aggregated value, so he is unable to make action plan with that. Let us see how to explore the hidden value in the data. Next, we will move on to the demo. I'm using bold report server to visualize the fastest rendering of larger records. I click on the create report, naming it as larger records.

Speaker 2: This gives us a design area to create the report. I'm adding header, so here we can see two items,

Speaker 1: one is default, another report parts. The report parts are already saved widgets. You can create one and save it to the server and other user can utilize it. So, I'm reusing the already created report parts,

Speaker 2: arranging the positions.

Speaker 1: Similarly, I'm reusing the already created chart item. Here, whenever you use the report parts in your report, it automatically creates a data set data source bounded. Here, I can see the data source and data sets that are used in the chart already added to my report by adding this report parts. Here, we are using this chart to display the visitors by category. Here, the visitor is added to the y values and column as the categories. Similarly, we have another chart that shows the conversion trend. In this, we have three line series. One is the list of visitors, another shows the list of purchase and another shows the users who have added the items to the cart. As same as the column chart, here we are using the category as column value. In these two charts, I have used group values because I want to view the trends of the data. So, I'm just clicking preview. Here, I can see the visitors by category like clothing, electronics like that. The second chart shows the conversion trend. This line series shows number of visitors and the another series shows the user who are all added the items to the cart. The third series shows the purchase list. So, it shows there is some problem with the user who are all added to cart but they left without purchasing it. So, I want to investigate it further but in this chart, I can't see any specific user detail. So, I am unable to analyze it. So, I am going back to design and I am going to add the detail report item.

Speaker 2: Before that, I am adding new data to fetch detail data item.

Speaker 1: So, here I am dragging and dropping the analytics data. This table has more columns but I need only few of them because I am going to analyze only the purchased and overall added to the cart and

Speaker 2: their comments. So, I am removing other unnecessary columns. Renaming the data set as detail data and click on finish.

Speaker 1: Now, I am going to add a table report item to view the detailed data which is created in the data set. I am selecting the details data data set and selecting the user id, category,

Speaker 2: added to cart and then inserting few more columns, purchased and the comments. I am giving the titles.

Speaker 1: So, I know that this reason going to be a bigger in size because it has some text content. So,

Speaker 2: I am resizing it. Also, selecting the cells to do some styling. Similarly, setting the alignment for the detail cell. Adding one text box to give title to this.

Speaker 1: Here, I am going to list out the number of data details. I am giving some customized name.

Speaker 2: I am clicking ok. Similarly, I am doing the style changes.

Speaker 1: Now, I know that this detailed data set has more number of rows. So, I am going to give the smart rendering option which is used to render faster. So, when you set the smart rendering, it automatically enables better options to view the reports faster. So, I am going to click preview. Here, you can see the top 1000 record has been displayed. If you want to view entire data, you can click on the load full data. The reason for displaying only the 1000 records, the smart rendering by default has the limiting support. So, we are going back again and changing it.

Speaker 2: So, we have the limiting option under the fetch limit. This fetch limit is used to

Speaker 1: set the number of records you need to display. You can utilize it when you need to render the records faster and limited record. Also, when you need to verify the design output. I am setting it as zero since I don't want to use limiting. Now, I am clicking on the preview. Here, you can see the total number of visitor in the data set is 1 million. Also, earlier we had the limit settings enabled here. Now, it has gone. Also, the available page has been displayed here. From this table, we can understand the reason for not purchasing the product even after adding to the cart. So, this way we can analyze the in-depth details of the report. For this scenario, we have added table like this. If you have some other scenario, you can design based on your scenario. However, here we also noticed that the progress dialog still remains 1 percentage and the pages still in 3. So, if you click to the next page, it goes to the next page but it is not increasing. It is why because in smart rendering by default the page creation property set as on demand. So, it will create the page only when you click on the next page. Otherwise, it will not create the next page. Only it displays the first page. So, we will go back to the design and change the property to see all the pages.

Speaker 2: I am giving the page creation as background.

Speaker 1: This property setting shows that we need to create pages in background and display all the pages. I am clicking ok. I am previewing it. Compared to the earlier, we can now see the pages has been created in the background instead of waiting us to click on the next. Also, you can see the progress bar. So, that creates pages in background. You can navigate. You can navigate at the meantime. So, this option gives the faster rendering as well as the interaction enabled during the page has been created in the background.

Speaker 2: So, it takes some time to finish it. So, once all pages creation has been completed, it gives the exact page number count and you can

Speaker 1: freely navigate to any pages. Also, you can search and find whatever data you need for analyzing this report. Now, we will move back to the slide. Now, we understood how to create table in order to display all the records and analyze the trends easier. This is just one example. There are many other industries from healthcare, finance, logistic and manufacturing where the ability to render larger data quickly have a big impact. Now, we will revisit the important best practices for rendering larger data. Set page creation as on-demand which will create only the required page. Other pages are created while navigating in the toolbar. Specify virtual evaluation as true always for better memory and resource utilization. Use auto grow text to false to avoid extensive size calculations. Set some numeric value to the limit record when you need to verify design output or quick preview of reports. The rendering efficiency starts with the query that fetches the data from database. You can select only the necessary columns, use where classes and avoid complex joins. By creating index on columns that are frequently searched or used in join operation, we can speed up the data retrieval. Reducing number of the dynamic styles used in the report helps to speed up the processing. Avoid using rectangle and shape items inside the table. Instead, use text box cells that are simpler and good choice for faster rendering. The on-demand option helps to overcome the challenges of rendering larger data. The table shows the metrics for displaying millions of records, both in flat table and with three-level groups. Using on-demand, data analysis becomes significantly faster and completed just in seconds. The on-demand processing provides various advantages. Let us see some of them. The report page created in seconds, which allows user to interact with the page immediately and enhances the user experience. You can render the table with deep insights of data that helps to identify opportunities for business improvement or growth. The huge data rendering is faster. Having instant access to real-time data allows to act faster and provide a significant competitive advantage. Optimized evaluation leads to better improved accuracy and proper resource utilization. So far, we have achieved rendering printable reports with millions of records in seconds by utilizing the bold reports product. All the features discussed in the session are available in our bold reports website. Also, the on-demand functionality is available online with our latest v6.1 public release. I hope this session has provided you with deeper understanding. Use bold reports with your industry data and make bolder decisions. This brings us to the end of today's webinar session. Thank you for your time and participation. Thank you.