Skip to main content

Importing Excel files to SQL server

In this I post I will try to demonstrate how we import a Excel files to SQL Server.We use the table we import to do some other SQL manipulations in the next topics.
We start by creating a database called 'Nobel_Laureates'. Open SQL Server Management Studio and create a new query window by clicking on 'New Query' menu or by hitting 'Ctrl + N' on your keyboard.
Lets create a database called 'Nobel_Laureates' using the following code.

CREATE DATABASE Nobel_Laureates
USE Nobel_Laureates

Now lets download the list of all the Nobel Laureates from online using this link, or by copying and pasting 'http://www.downloadexcelfiles.com/sites/default/files/docs/list_of_nobel_laureates-944j.xlsx' on your browser.
The link downloads an Excel file with a name 'list_of_nobel_laureates-944j.xlsx' on your default downloads folder.
Once the list_of_nobel_laureates-944j.xlsx file is downloaded, We import it to our Database as follows. Right click on the 'Nobel_Laureates' database.



 And go to 'task' and then click on  'import data'.The 'SQL Server Import and Export Wizard' dialogue box pops up.
Read the content and Click Next.


From the Data Source list Select Flat File Sources,click Browse button to select the nobel.csv file from your downloads folder. Leave everything else as it is and click Next.


Leave everything as it is and click Next.

 Leave everything as it is and click Next.


Leave everything as it is and click Next.


Leave everything as it is and click Next.


Leave everything as it is and click Finish.


Leave everything as it is and click Finish.

Now You have the 'nobel' table in your database.




Popular posts from this blog

Intelligent Agents and Their Application to Businesses

Intelligent agents have moved from a research topic to something most businesses now interact with directly. An agent is software that perceives its environment (a database, a webpage, a codebase, a set of sensors), decides what to do next, and takes action toward a goal, largely on its own, adjusting as conditions change. What used to be a narrow academic definition now describes tools millions of people use daily: coding assistants that read a codebase and ship a fix, browsing agents that complete a multi-step task on a website, and customer-facing bots that resolve a support ticket end to end instead of just answering one question. What Makes Something an "Agent," Not Just a Model A language model on its own answers a question. An agent does something with the answer: it calls a tool, queries a database, sends an email, or hands a result to another agent, then observes what happened and decides on the next step. That loop, perceive, reason, act, observe, repeat, is wha...
Read more

Exploring Sentiment Analysis Using Support Vector Machines

Sentiment analysis, a powerful application of Natural Language Processing (NLP), involves extracting opinions, attitudes, and emotions from textual data. It enables businesses to make data-driven decisions by analyzing customer feedback, social media posts, and other text-based interactions. Modern sentiment analysis has evolved from simple rule-based methods to advanced machine learning and deep learning approaches that detect subtle nuances in language. As text communication continues to dominate digital interactions, sentiment analysis is an essential tool for understanding public opinion and driving actionable insights. The GoEmotions Dataset The GoEmotions dataset, developed by Google Research, is a benchmark in emotion recognition. It consists of over 67,000 text entries labeled across 27 emotion categories, such as joy, anger, admiration, and sadness. For practical applications, these emotions can be grouped into broader categories like positive and negati...
Read more

Role of Fourier Transform in Speech Recognition

Speech recognition has become an integral part of modern technology, from voice assistants to transcription services. A key mathematical tool enabling these advancements is the Fourier Transform (FT), particularly its variant, the Short-Time Fourier Transform (STFT). The Fourier Transform provides a way to convert speech signals from the time domain to the frequency domain, allowing us to extract meaningful features for analysis and recognition. Why Use Fourier Transform in Speech Recognition? Speech signals are inherently time-domain signals, with varying amplitude over time. However, speech carries crucial information in its frequency content, such as phonemes, tones, and pitch. The Fourier Transform enables us to analyze these characteristics by breaking the signal into its constituent frequencies. The Fourier Transform is widely used in speech recognition for: Spectrogram Generation: Converting speech signals into visual representations of frequency over time. Fea...
Read more