I was curious about disaster events occurred in the United States because I did a similar program on Canadian natural disaster. This time I am making my data more graphical so that we can understand the data better. There are two datasets and they are both from the Federal Emergency Management Agency (FEMA). One consists of historical disaster data records and the other one consists of the grants awarded under the Public Assistance Program. There are three relief programs in the dataset: Individual Assistance Program, Public Assistance Program, and Hazard Mitigation Program. Out of the three, Public Assistance Program is the most used program.
We are a company that sells furniture, technology, and office supplies. Our database for our transactions and customers can be messy because of our high sales volume. This requires some data specialist to extract certain data we need in the database and visualize our data. To extract data, we use Microsoft SQL Server. To visualize our data, we use Tableau. The two tools can help us to understand our customers and our overall business. Below is a quick overview of our large dataset:
Yes, I admit that I am new to Hadoop, but I am making a lot of process in the past few days. I learned how to run my job both locally and on Hadoop cluster. Python was not new to me, so it was easier for me to navigate through my Hadoop journey. The first dataset I used is all about movie ratings because my instructor is part of the IMDB gang. For the first job, I want to count the total number of movies rated under the 5-star rating system. I first needed to use a mapper function to extract the ratings from the data set and the number 1s. The rating is the key and 1 is the value. After some shuffling and sorting we proceed to the next step which is to use a reducer function to sort out the key which is the rating, and the sum of 1s (number of times the movie has been rated, say, 4 stars) that are associated with the rating. For the second job, I am interested in sorting the movies with their associated times they have been rated in the database. The more times they are rated, the more popular they are. Again, the mapper extracts the movieIDs as the key and set 1 as the value. In the reducer, it then adds up the 1s to see the number of times rated. This time, however, the sum of movie counts becomes the key and movieID becomes the value. This way when we pass through a second step the shuffle and sort phase will automatically sort things by the movie count for us. Then we write another function to get a unique count with the movie associated to the count. The result is that the movie column will appear on the left and the count column will be on the right.
My boyfriend and I love roadtripin' across America. However, our F150 consumes a lot of gas, and costs a lot more than a small car. This program is based on the fuel efficiency standard of the United States of America.
Use Google Maps to find the miles between your starting point and your destination
Find the fuel efficiency of your car model
Find the local gas price
Now use the data from above, type in the corresponding data
I watched the latest Chucky movie called "Cult of Chucky". Well, this movie didn't disappoint me at all. I really like the last part of the movie because it left me hanging...I really want to find out what Kyle is going to do with Chucky! Ah, this means I have to patiently wait for the next movie to come out. Anyhow, the Good Guys bookstore did some database updates, and this time is all about refining our selection. In the following file, it will show all the different ways of selecting the particular information you want from the database.