Start_time, hour, day, week, month, year, weekdayĬonfig = configparser. Time - timestamps of records in songplays broken down into specific units Song_id, title, artist_id, year, durationĪrtist_id, name, location, lattitude, longitude User_id, first_name, last_name, gender, level Songplay_id, start_time, user_id, level, song_id, artist_id, session_id, location, user_agent Dimension Tables Songplays - records in event data associated with song plays i.e. The log files in the dataset you’ll be working with are partitioned by year and month. These simulate app activity logs from an imaginary music streaming app based on configuration settings. The second dataset consists of log files in JSON format generated by this event simulator based on the songs in the dataset above. The files are partitioned by the first three letters of each song’s track ID. Each file is in JSON format and contains metadata about a song and the artist of that song. The first dataset is a subset of real data from the Million Song Dataset. Using the song and event datasets, we create a star schema optimized for queries on song play analysis. Write a SQL CREATE statement for each of these tables in sql_queries.py Sql_queries.py is where we define you SQL statements, which will be imported into the two other files above.Ĭreate_cluster_redshift.ipynb is where we create the AWS Redshift Cluster by using SDK.ĭwh.cfg is the info about the personal account of AWS Step 1 :Create Table Schemasĭesign schemas for your fact and dimension tables You’ll be able to test your database and ETL pipeline by running queries given to you by the analytics team from Sparkify and compare your results with their expected results.Ĭreate_table.py is where we create the fact and dimension tables for the star schema in Redshift.Įtl.py is where we koad data from S3 into staging tables on Redshift and then process that data into your analytics tables on Redshift. Their data resides in S3, in a directory of JSON logs on user activity on the app, as well as a directory with JSON metadata on the songs in their app.Īs their data engineer, you are tasked with building an ETL pipeline that extracts their data from S3, stages them in Redshift, and transforms data into a set of dimensional tables for their analytics team to continue finding insights in what songs their users are listening to. IntroductionĪ music streaming startup, Sparkify, has grown their user base and song database and want to move their processes and data onto the cloud. We will need to load data from S3 to staging tables on Redshift and execute SQL statements that create the analytics tables from these staging tables. In this project, we will acreate a data warehouse by using AWS and build an ETL pipeline for a database hosted on Redshift. > redshift_tool.How to create Data Warehouse with Redshift > upsertkey=('upsertkey1','upsertkey2')Ĥ.Examples Append or Copy data without primarykey, sortkey, distributionkey Eg. upsertkey:- During the upsert method of data loading, we need to pass upsert key by which key old record will get updated & new will be added.
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