 Hello and welcome to a session on Data Extraction and Custom Data View in Tableau. This is Dr. Alita Poojar, Professor in Computer Science and Engineering Department from Walchin Institute of Technology, SolarPore. At the end of this session, learners are able to create an extract, add new data to extracts, apply field operations and generate custom data view. What is an extract? An extract is a subset of a data from the data source created by applying filters and limits. Extracts are stored in local drive for offline access and can be used for analysis by Tableau any time to improve the performance. Data extraction is done by going to the menu, then choosing data option from that menu and then data extract data. Now let us see the screenshot of it. So you see that in the menu, there is a data tab, when you click on that data tab, a window pops up where there is an option for extract data. Now to extract a subset of data from the data source, you have to create filters that return the relevant rows. Now let us consider the sample dataset, superstore.xls that comes with Tableau. Create an extract by choosing the filter option and in filter you have to select from the list. Now when you click on the select from the list, various fields are displayed, a checklist of the fields are displayed, from that checklist you have to select the fields that are to be extracted from the data source. So check the values to pull the data from the data source, the screenshot is like this. There is a list of values is displayed, you have to check on the check boxes. So in this particular example, 5 values have been selected for the extract. So below there is a summary shown that 5 out of 17 values have been extracted for the creation of an extract. So to add more data for an already created extract, you can choose the options, extract data, then append the data from the file, browse the file and then click OK, which means that you are choosing the file from which you want to add the data to the extract. So before doing this, you need to check whether the number and the data type of the columns in the file syncs with the existing data in the extract. The history of data extracts can also be checked here, that is you can check how many times the extract has been created by using the following options, that is go to menu. In the menu you choose the data type and when you click on the data type, you can choose an option extract history. So when you click on this option, it will immediately display how many times the extract has been created till now. Now let us see how field operations are handled in W. These are the field operations which are frequently required for analysis in W, that is adding fields to the worksheet, combining two fields, grouping two fields, searching fields, reordering fields and also renaming the fields. So any field can be added to the worksheet by right click and pick the option add to sheet. So this is a screenshot. So you can see in the pop-up window there is an add to sheet option. Now when you want to combine two fields, two fields can be combined to create one field. The values in the dimension field get combined to a single value by joining two strings into one string separated by comma. The combined field has a name which is combination of individual fields. It is but obvious. The default name can be changed to a new name by using the rename field operation. So you can see the screenshot here for combined fields. Now in this particular example of Superstore we have chosen country and state as the two fields to be combined. So when you right click on these two fields you get an option for create and in the create you have an option for combined field. Now next is the searching the fields. You can search the fields by using search box option where you can type the first three letters of the field name and this brings out the results showing the fields whose name contains these letters. Next comes reordering the fields. Reordering of fields is done to bring similar fields together which are frequently used in analysis and also grouping of fields is used which helps to create group of data which lies under the same category. So first let's see the screenshot for reordering the fields. So we will first go to the right click option. Then we can see the list of the dimensions and the measures. We drag the field which is to be reordered. So we are dragging here the customer name to be reordered between the two fields state and city. So it will put the customer name between state and city. Similarly we can go to the grouping of fields by the options right click. When you right click we can see the create option and in create option we can go to the group to group the fields. So let's see the screenshot for grouping of fields. So this is the screenshot. Here I have used segment. I have dragged the segment measure to the field name in grouping of fields dialogue box. And there we have three types of groups called as consumer, corporate and home office. So all the analysis will be shown for these three groups. Similarly we can create set of two fields. It is used when we have common data in two fields and when we want to combine and sort out. So the options for this are right click on the worksheet. You have create option and in create option you select set. And when you click on set you can choose the field names to be combined as one set. So this is the screenshot here. So when you right click on the sheet there is a create option and in create there is an option called as set. Now let's see how to create the custom data view. A custom data view is created by extending the normal data views with some additional features so that the view can give different types of charts for the same underlying data. That is when you want to perform different types of analysis for the same underlying data then we use custom data view. It is used to enhance the normal data views. Some of the frequently used custom data views that Tabu offers are drill up drill down and swapping dimensions. So let's see what is drill up and drill down. Drill down and drill up view it is used when we want to have results at different granularity level of a dimension field which is a part of predefined hierarchy. So you can see the values of the measures at different granularity levels that is either at the finer granularity levels or at the coarser granularity levels. For example if your analysis creates measures for a quarter then you can drill down the dimension to create measures for every month or even every week in that quarter to view at a finer level of granularity. Similarly, if your analysis creates measures for an year then you can drill up to measure it the analysis for every five years and so on or if your analysis measures for a month then you can drill up to view the measures for quarter half yearly or for every year. In this way drill down and drill up they are used for weaving the analysis either at the finer level or at the coarser level. So to drill up and drill down the view for individual dimensions in an hierarchy right click a table header and select the drill down from the context menu. Suppose a bar chart is created with the dimension category in the column shelf and the major cells in the row shelf then right click on the bar representing the dimension and select the drill down. So this is a screenshot for that. So you can click on that bar chart that is on the column immediately it shows the option drill down. Now we will see what do you mean by swapping dimensions. A new view can be created from an existing view by swapping the positions of dimensions. Now swapping changes the position of measures but not the values of the measures. Consider view to analyze profit for each year that to for each segment and category of products. So click on the vertical line at the end of the category column and drag it to the segment column. So you will find that profit changes for each category and segment and not its value. So let's see the screenshot for it. So here it shows the analysis for every category and in every category it shows for subcategory and the column wise it shows for sales. So I have dragged the category subcategory column to view at the segment level. So it has shown all the analysis for this. Now let's pause the video for a while think and write. What is used to view the results of analysis at coarser granularity level? Is it drill up, drill down or swapping dimensions? So as per our earlier discussion we see that drill up is used to view the results of analysis at coarser granularity level. That is I have already told you that if your results are already created showing the analysis for a month then using drill up option we can show the analysis for every quarter, half year or yearly that is you are viewing the analysis at higher level in the hierarchy that is called as coarser granularity level. Now these are some of the references that are being used for creating this video that is tutorialspoint.com and data flare training. Thank you.