Redshift reads CSV files without a header line. I have chosen CSV files because they are easy to upload to Redshift through Amazon’s popular S3 service. Postgres allows us to export data in many formats. One of the first steps required to migrate one analytical system to another is to export the data gathered. This result will be used to verify if the migration succeeded. User_profile_id | total_time_spent | session_avg_duration The view is then used in the final query, where the SUM() of the total time spent and the AVG() session time are calculated for each user. First, we select users' sessions as a time between the last action max(action_time) and the first action min(action_time) as the scoped view user_sessions. Sum(user_ssion_duration) as total_time_spent,Īvg(user_ssion_duration) as session_avg_duration For example, the following SQL query returns the overall time spent by a user and the average session length:Įxtract(epoch from max(action_time)) - extract(epoch from min(action_time)) as session_duration This information allows analytics operators to track the time spent by users. failed_exercise_code_execution – Indicates if the user failed to finish the exercise.successful_exercise_code_execution – Indicates if the user finished the exercise.execute_exercise_code – When the user executed some SQL code.exercise_page_open – When an exercise’s page was opened.course_page_open – When a course’s page was opened.course_finish – When all the exercises in the course were completed. ![]() course_start – When the first exercise in the course was completed.there has been no action for more than a few minutes. logout_session_expired – The user’s session expired, i.e.logout – The user logged out of the system.login – The user logged into the system.It stores many kinds of possible user actions, the most important of which are: action_type – A dictionary table containing three unchanging columns ( id, code, and name).query – The SQL query which the user entered to complete the exercise.is_async – Not important for the purpose of this article.is_exercise_checked – A true/false value that indicates if the exercise required user interaction.exercise_id – The exercise being completed by the user.course_id – The course being taken by the user when the action occurred.action_time – The timestamp when this action occurred.session_id – An identifier for each unique learning experience.user_profile_id – A unique identifier for each user.The source implementation is built on a PostgreSQL database and contains two main tables: The solution we want to reengineer is a tracking system for an online SQL learning platform like. In this article, we will show how to reimplement an existing Postgres database to a more complex analytics database like Amazon Redshift. ![]() Gathering information about users’ behavior can increase the quality of their experience, which can lead to increased business income. Tools such as DB dumps will not work with Amazon Redshift.Online systems tend to track user’s actions. Thus, you might encounter an error while using the tool with your Amazon Redshift database. However, note that Redshift does not support all features of the latest PostgreSQL versions. As Amazon uses PostgreSQL 8.x, you can use psql command line tool to connect Redshift.
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