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Start by visiting the US Census Bureau's website and identify the datasets you need. Use the Data.Census.gov tool to search for and download the relevant data. Download the datasets in CSV format, as this is a widely accepted and easy-to-handle format for data processing.
Set up your local environment to handle data manipulation tasks. Install necessary tools like a text editor or code editor (such as Visual Studio Code), and ensure you have Python installed on your machine. Python is ideal for data manipulation due to its extensive library support.
Use Python (or your preferred programming language) to clean and transform the downloaded CSV data. Libraries like Pandas can be helpful for this task. Ensure that the data is in the appropriate format and structure required by Starburst Galaxy, such as ensuring column names are standardized and data types are consistent.
If you haven’t already, create an account on Starburst Galaxy. Once your account is set up, log in to access the platform. Familiarize yourself with the interface, focusing on how to create and manage catalogs and schemas which will store your data.
Within Starburst Galaxy, navigate to the interface where you can manage catalogs and schemas. Create a new schema that will house your US Census data. Define the schema structure such as tables and columns that match the data format prepared in step 3.
Convert your cleaned and transformed data into SQL INSERT statements. This can be done through a custom script in Python. Iterate over each row of your CSV data, generating an INSERT SQL statement that corresponds to the schema and table structure created in Starburst Galaxy.
Use the Starburst Galaxy SQL interface to execute the SQL INSERT statements. You can do this by copying and pasting the SQL commands into the query editor and running them. Alternatively, if the dataset is large, consider automating the execution using the command line tools provided by Starburst Galaxy for batch processing. Ensure that the data is correctly loaded by running SELECT queries to verify the integrity and accuracy of the data in your Starburst Galaxy instance.
By following these steps, you can successfully move data from the US Census to Starburst Galaxy without relying on third-party connectors or integrations.
FAQs
What is ETL?
ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.
The U.S. Census Bureau takes the pulse of the country’s economy. Producing economic data monthly, quarterly, yearly, and at five-year intervals requires high-tech solutions. The U.S. Census Bureau, in response to this need, has built an its first ever iPhone application, aimed at providing users with important economic statistics quickly and easily directly from an iPhone: the America’s Economy application.
The US Census Bureau's API provides access to a wide range of data related to the United States population and economy. The following are the categories of data that can be accessed through the API:
1. Demographic data: This includes information on population size, age, gender, race, ethnicity, and household characteristics.
2. Economic data: This includes data on employment, income, poverty, and industry.
3. Housing data: This includes data on housing units, occupancy, and characteristics of housing units.
4. Education data: This includes data on educational attainment, enrollment, and school districts.
5. Geographic data: This includes data on boundaries, locations, and maps.
6. Health data: This includes data on health insurance coverage, disability, and healthcare facilities.
7. Transportation data: This includes data on commuting patterns, modes of transportation, and traffic.
8. Business data: This includes data on businesses, employment, and revenue.
9. Agriculture data: This includes data on crops, livestock, and farms.
10. International data: This includes data on international trade, migration, and foreign-born population.
What is ELT?
ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.
Difference between ETL and ELT?
ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
