Getting Started with Data Management

Master data management is a data governance discipline maintaining a single source of truth for an organization's core data elements by aligning processes and technology for cross-system consistency.

Explore how to measure name popularity using the US baby names data set since 2010, creating name and year series and visualizing frequency with a horizontal bar plot.

Break data into smaller components with descriptive columns to enable flexible queries in a relational database, avoiding multipart fields and enabling easy reports on customers by state.

Restricting and sorting data using filters such as in, not, between, and like, and ordering by salary in descending order and by hire date to show most recently hired.

Learn to display data from multiple tables with inner joins and aliases to pull department names, job titles, and locations, and generate payroll reports by country.

Connect to a Postgres database named github warehouse and set up a movies table with a serial id primary key; then implement ratings table to illustrate one-to-many and many-to-many relationships.

Finish ETL process by loading transformed data into the database with load.py, wrapping routines in functions, creating an average ratings table that overwrites if it exists, then verify in Postgres.

Explore the three data types: structured, semi-structured, and unstructured, and learn how each uses organization, markers, and metadata to influence management, search, and analysis.

Explore Hadoop, the first open source big data project built as an implementation of MapReduce and the Google File System; learn its MapReduce and HDFS components and cloud service options.

Interact with big data on Hadoop using Hive to issue SQL-like queries, import CSV to HDFS, and create an external table that points to the file for quick data access.