0 ×

04_​AmazonS3-MSBlobStorage_​Census_​Data

Workflow

Will They Blend? Amazon S3 meets MS Blob Storage ... and as usual also a few Excel files.
The challenge here is to blend S3 formatted data from the Amazon Cloud with Blob Storage formatted data from the MS Azure Cloud. As a secondary challenge we want to blend the cloud data with Excel data. Will they blend? Data is the new CENSUS data downloadable from: xxx. We report the average travel time to work vs. the English proficiency and we discover that, if you are a woman, the less you speak English the closer to home your work place is. If we then analyze the size of the statstical sample, we also discover that the average is based on only 31 not English speaker females. Probably not enough to consider the results as reliable. Always maintain a healthy degree of skepticism towards your results! By the way ... yes they blend! BLOG: Amazon S3 meets MS Azure Blob Storage. A match made in the clouds. https://www.knime.org/blog/S3_meets_BlobStorage
data blending Amaozon S3 Microsoft BlobStorage Cloud service cloud KNIME connectors
Will They Blend? Amazon S3 meets MS Blob Storage ... and as usual also a few Excel files.The challenge here is to blend S3 formatted data from the Amazon Cloud with Blob Storage formatted data from the MS Azure Cloud. As a secondary challenge we want to blend the clouddata with Excel data. Will they blend?Data is the new CENSUS data downloadable from: http://www.census.gov/programs-surveys/acs/data/pums.html. We report the average travel time to work vs. the English proficiency and wediscover that, if you are a woman, the less you speak English the closer to home your work place is. If we then analyze the size of the statstical sample, we also discover that the average is based on only 31 not English speaker females. Probably not enough to consider the results as reliable. Always maintain a healthy degree of skepticism towards your results!Blog post available at https://www.knime.org/blog/S3_meets_BlobStorageBy the way ... yes they blend! From the Clouds ... From Excel ... In Maine women whospeak no English havethe shortest commute.Should you stoplearning English? But then again womenwho speak no Englishin this dataset are only31. Keep learningEnglish. connect to Amazon CloudS3 datass13hme.csvreadingselected fileinto KNIMEconnect toMS Azure CloudBlob Store datass13pme.csvreadingselected file into KNIMEjoin on SERIAL NOENG vs.SEX_textmean(Travel Time)& COUNTavg travel timevs. English proficiencyby sexENG_MapValuesSEX_MapValuesjoin on SERIAL NOjoin on SERIAL NOMISSING -> 99ENG & SEXJWMNP as"Travel Timeto Work"remove rowswith missing ENGvalueremove COUNTMale& Femaleby ascendingENGremove meanMale& FemaleCOUNTvs. English proficiencyby sex Amazon S3Connection Amazon S3File Picker CSV Reader Azure Blob StoreConnection Azure Blob StoreFile Picker CSV Reader Joiner Pivoting JavaScriptBar Chart Excel Reader (XLS) Excel Reader (XLS) Joiner Joiner Missing Value Column Rename Row Filter Column Filter Column Rename Sorter Column Filter Column Rename JavaScriptBar Chart Will They Blend? Amazon S3 meets MS Blob Storage ... and as usual also a few Excel files.The challenge here is to blend S3 formatted data from the Amazon Cloud with Blob Storage formatted data from the MS Azure Cloud. As a secondary challenge we want to blend the clouddata with Excel data. Will they blend?Data is the new CENSUS data downloadable from: http://www.census.gov/programs-surveys/acs/data/pums.html. We report the average travel time to work vs. the English proficiency and wediscover that, if you are a woman, the less you speak English the closer to home your work place is. If we then analyze the size of the statstical sample, we also discover that the average is based on only 31 not English speaker females. Probably not enough to consider the results as reliable. Always maintain a healthy degree of skepticism towards your results!Blog post available at https://www.knime.org/blog/S3_meets_BlobStorageBy the way ... yes they blend! From the Clouds ... From Excel ... In Maine women whospeak no English havethe shortest commute.Should you stoplearning English? But then again womenwho speak no Englishin this dataset are only31. Keep learningEnglish. connect to Amazon CloudS3 datass13hme.csvreadingselected fileinto KNIMEconnect toMS Azure CloudBlob Store datass13pme.csvreadingselected file into KNIMEjoin on SERIAL NOENG vs.SEX_textmean(Travel Time)& COUNTavg travel timevs. English proficiencyby sexENG_MapValuesSEX_MapValuesjoin on SERIAL NOjoin on SERIAL NOMISSING -> 99ENG & SEXJWMNP as"Travel Timeto Work"remove rowswith missing ENGvalueremove COUNTMale& Femaleby ascendingENGremove meanMale& FemaleCOUNTvs. English proficiencyby sex Amazon S3Connection Amazon S3File Picker CSV Reader Azure Blob StoreConnection Azure Blob StoreFile Picker CSV Reader Joiner Pivoting JavaScriptBar Chart Excel Reader (XLS) Excel Reader (XLS) Joiner Joiner Missing Value Column Rename Row Filter Column Filter Column Rename Sorter Column Filter Column Rename JavaScriptBar Chart

Download

Get this workflow from the following link: Download

Nodes

04_​AmazonS3-MSBlobStorage_​Census_​Data consists of the following 22 nodes(s):

Plugins

04_​AmazonS3-MSBlobStorage_​Census_​Data contains nodes provided by the following 5 plugin(s):