Python ETL Literacy (Beginner Level)

  • 37m 30s
  • 25 questions
The Python ETL Literacy benchmark will measure your ability to perform data operations using SQLAlchemy, data transformation using petl, and processing of HTTP requests. You will be evaluated on your ability to implement data operations and web techniques in Python applications. A learner who scores high on this benchmark demonstrates that they have the skills to develop interactive Python web applications that perform data operations and process HTTP requests.

Topics covered

  • calculate aggregate statistics using group by
  • combine data from multiple tables into one table
  • create and use a SQL primary key constraint with autoincrement
  • create complex triggers
  • create tables using object relational mapping
  • customize stored procedures using input arguments
  • filter data using the order by, limit, and offset clauses
  • filter data using two tables with a parent-child relationship
  • identify the different forms of responses that may be contained within an HTTPX Response object
  • implement split operations on data stored within petl data tables
  • insert data into views
  • limit the amount of time your app spends waiting to get served a response to an HTTP request using timeouts
  • obtain information about a remote service by examining the data in the headers returned in an HTTP response
  • perform filter operations using SQL operators, such as like, not like, and between
  • perform insert and delete operations
  • perform lookups on data imported from pickle files
  • perform three-way joins in tables
  • perform update operations while preserving the parameter order
  • perform various import and export operations on CSV, TSV, and TXT files
  • recognize the different types of HTTP requests that can be invoked with HTTPX and describe the use cases for each of them
  • slice and dice data stored as records within a petl data table
  • transform data by rows using rowmap() and rowmapmany() functions
  • use petl's facet() function to define filters for specific fields in a table
  • use Python with blocks for transactions
  • use the automap_base function to convert tables to classes