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Name: Liliana Tang
Type: User
Company: Databricks
Bio: Data Scientist | Software Developer | Life-long learner
Location: Canada
Name: Liliana Tang
Type: User
Company: Databricks
Bio: Data Scientist | Software Developer | Life-long learner
Location: Canada
Building high grade data pipelines that are dynamic and built from reusable tasks, can be monitored, and allow easy backfills.
A music streaming startup has grown their user base and song database and want to move their processes and data onto the cloud. 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. As their data engineer, I am 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.
Building an ETL pipeline for a data lake hosted on S3
Writing Python code that reads in data, cleans the data, and then uses the data to make Plotly visualizations.
Modelling event data to create a non-relational database and ETL pipeline for a music streaming app - defining queries & tables for a database built using Apache Cassandra.
Modelling user activity data to create a database and ETL pipeline in Postgres for a music streaming app - defining Fact & Dimension tables as well as inserting data into new tables.
An exploratory data analysis to understand the real estate market in Edmonton.
Demo of using the Nutter for testing of Databricks notebooks in the CI/CD pipeline
Building a machine learning pipeline to categorize emergency messages based on the needs communicated by the sender
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