BryteFlow Ingest leverages the columnar Redshift database by capturing only the deltas (changes in data) to Redshift keeping data in the Redshift database synced with data at source. To test and finish the project in time, I decided to filter the songs directory to only get data from 's3://udacity-dend/song-data/A'.Change Data Capture your data to Redshift with history of every transactionīryteFlow continually replicates data to Redshift in real-time, with history intact, through log based Change Data Capture. After the staging tables are loaded, the ETL is filling the necessary tables that can be used to run queries against. The command COPY is used to read all JSON files in the bucket and to load the staging tables. Python create_table.py creates all necessary table to load the data in Once the sql is loaded and you are able to connect to the database, the run two Python scripts from a normal terminal window Load the SQL and connect to the databaseīlock 11. Open the incoming TCP port to access the cluster endpointīlock 10. Once the cluster becomes available, get the cluster endpoint and role ARN.Įnter the endpoint as the host in the param fileĮnter the correct role (ARN) in the param fileīlock 9.
Check the availability of the cluster.īlock 8. Finally the create the actual Redshift Clusterīlock 7. create the IAM role that provided the required access to the S3 bucketīlock 6. Now that the S3 bucket is defined, check the data (resources) that will be used to load our tablesīlock 5. create the clients or definitions from IAM, EC2, S3 and Redshiftīlock 4. In order to use the parameter file use your own AWS valuesīlock 3. Load all Data ware house parameters which are stored in a configuration (param) file. > make sure you run the blocks in sequenceīlock 2. > Open the file Redshift-Cluster, and execute each block by pressing ctrl + Enter Open terminal and execute jupyter notebook this is the python file that actually performs the ETL and therefore loads the staging data and further loads the actual tables used the perform queries on. is a python file that calls all required functions to create the tables as described in the sql_queries.pyĮtl.py. is a python file that holds all DDL aand DML Statements to create and load the data.Ĭreate_tables.py. The file is mainly empty and can be substituted with your own values. This is the configuratiuon file that contains all parameters required to build and run the cluster. The jupyter note is the scafolding that describes the build of a DWH cluster and is suplemented with some queries to test the database.ĭwh.cfg.
The project consists of the followling files: For example, here are filepaths to two files in this dataset.Īnd below is an example of what the data in a log file, -events.json, looks like.Ī star schema is used for queries on song play analysis.
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.
#Aws million song dataset import to redshift simulator
The second dataset consists of log files in JSON format generated by this event simulator based on the songs in the dataset above. And below is an example of what a single song file, TRAABJL12903CDCF1A.json, looks like.
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