We\u2019ll lay down a simple scenario from the retail world, but the concepts are applicable in a lot of other situations<\/p>\n
Let us take the following tables.<\/p>\n
These are transactional data for the same vendor that come from different sources & different schemas:<\/p>\n Clean, Transform, and Merge the data\u00a0<\/strong>to look like the following:<\/p>\n \u200d<\/p>\n If we only had these nine rows to deal with, it\u2019s not an issue \u2014 copy and paste within MS Excel or Google Sheets and manually clean it up.<\/p>\n But in the real world, the problems come in various forms:<\/p>\n Let us show you how you can resolve this in a couple of minutes, without writing any code.<\/p>\n For those who don\u2019t know about\u00a0Mammoth Analytics<\/a>, it is a lightweight, code-free data management platform.<\/p>\n It provides powerful tools for the entire data journey, including data retrieval, consolidation, storage, cleanup, reshaping, analysis, insights, alerts and more.<\/p>\n First, bring your data into the Mammoth Data Library.<\/p>\n For this example, we have simple CSV files that we uploaded directly into Mammoth, but\u00a0the platform supports a lot of additional ways to ingest your data.<\/a><\/p>\n With Mammoth\u2019s\u00a0extensive data transformation functions<\/a>, we can shape the data in a variety of ways to get it in the format<\/p>\n We\u2019ll perform a couple of transformations here to get the data in the right shape:<\/p>\n Now that we have transformed the data let\u2019s save it into a Master Dataset.<\/p>\n For this action, we will utilize a powerful function called \u201cSave to Dataset\u201d. This function allows multiple, potentially inconsistent and incompatible datasets to be merged into a single master dataset.<\/p>\n From Dataset 1, we will create a Master Dataset<\/p>\n Now with Dataset 2 and 3, we\u2019ll add the data into the Master Dataset<\/p>\n We can now see the \u201cMaster Dataset\u201d in the Data Library. If we open that up, we\u2019ll see our cleaned up and consolidated data.<\/p>\n We have achieved a code-free solution to combining multiple, incompatible datasets in a couple of minutes.<\/p>\n This a small example of some of the benefits of using the\u00a0Mammoth Analytics<\/a>\u00a0platform.<\/p>\n![\"\"](\"http:\/\/mammoth.enspire.in\/wp-content\/uploads\/2023\/07\/blog1.png\")
<\/figure>\nOur Objective<\/h2>\n
![\"\"](\"http:\/\/mammoth.enspire.in\/wp-content\/uploads\/2023\/07\/blog2.png\")
<\/figure>\nThe Challenges<\/h2>\n
\n
Mammoth\u2019s code-free, time-saving, automated solution<\/h2>\n
Step 1 \u2014 Transform and normalize the three datasets<\/h2>\n
![\"\"](\"http:\/\/mammoth.enspire.in\/wp-content\/uploads\/2023\/07\/blog4.png\")
<\/figure>\n![\"\"](\"http:\/\/mammoth.enspire.in\/wp-content\/uploads\/2023\/07\/blog-step-1.png\")
<\/figure>\n![\"\"](\"http:\/\/mammoth.enspire.in\/wp-content\/uploads\/2023\/07\/blog-step-1-2.png\")
<\/figure>\n![\"\"](\"http:\/\/mammoth.enspire.in\/wp-content\/uploads\/2023\/07\/blog-step-1-3.png\")
<\/figure>\nStep 2 \u2014 Save the Datasets into a Master Dataset<\/h2>\n
![\"\"](\"http:\/\/mammoth.enspire.in\/wp-content\/uploads\/2023\/07\/step-2-main.png\")
<\/figure>\n![\"\"](\"http:\/\/mammoth.enspire.in\/wp-content\/uploads\/2023\/07\/step-2-1.png\")
<\/figure>\n![\"\"](\"http:\/\/mammoth.enspire.in\/wp-content\/uploads\/2023\/07\/step-2-2.png\")
<\/figure>\n![\"\"](\"http:\/\/mammoth.enspire.in\/wp-content\/uploads\/2023\/07\/step-2-3.png\")
<\/figure>\n![\"\"](\"https:\/\/uploads-ssl.webflow.com\/6256cf335240122837e95aff\/631b1958135f70d199f5dbc9_step-2-4.png\")
<\/div>\n<\/figure>\nAnd we\u2019re done<\/h2>\n
![\"\"](\"http:\/\/mammoth.enspire.in\/wp-content\/uploads\/2023\/07\/colclusion-1.png\")
<\/figure>\n![\"\"](\"http:\/\/mammoth.enspire.in\/wp-content\/uploads\/2023\/07\/conclusion-2.png\")
<\/figure>\n