
Explore enterprise data governance, permissions, and toolchains for data at scale, covering structured and unstructured data, speed of access, and cross-database joins using Toad, Teradata, Vertica, PySpark, and SAS.
Raise permissions across dev, UAT, and prod environments, manage access requests, and monitor permission levels to resolve errors such as table not found or database not found.
Explore configuring ODBC native connections to Teradata, Oracle, and Vertica, locating drivers and Windows LDAP authentication, and selecting authentication methods, with Vertica presenting the most challenges.
Diagnose and resolve common connectivity issues in enterprise data engineering by examining permissions, access requests, and drivers for teradata, vertica, and oracle using toad and odbc.
Compare Teradata, Vertica, and Oracle across EDW, analytics, and real-time processing to reveal their strengths in big data, fast analytics, and regulatory reporting.
Extract data from Oracle Exadata and Teradata, join with Vertica or Hive, build a feature store, and model credit risk using score, income, balances, and history.
Analyze and optimize cross-database queries by converting SQL and PySpark, orchestrate etl, leverage left joins, and integrate Teradata, Vertica, and Hive data within the enterprise data warehouse.
Course Title: Enterprise Data Fundamentals (Permissions, Sources, Tools, Connections)
Course Description
In today's data-driven world, understanding how data is accessed, managed, and connected is essential for any professional working in enterprise environments. This course is designed to introduce you to the core concepts of data handling, focusing on permissions, data sources, tools, connections, and common errors.
What You Will Learn
Section 1: Data Permissions
How data permissions work
Why access control is critical for security
Maintaining data integrity through proper permissions
Section 2: Data Sources
Different types of data sources
Structured and large-scale datasets
Enterprise systems and their data storage
Section 3: Tools and Connections
Tools used to interact with data
How different systems connect to share information
Managing connections across platforms
Section 4: Working with Large Datasets
Managing millions of records efficiently
Accessing data across various platforms
Beyond Hive – multiple systems for big data
Section 5: Real-World Challenges
Performance issues and bottlenecks
Common data errors
Basic troubleshooting techniques
Course Goals
By the end of this course, you will have:
A solid foundation in enterprise data workflows
Understanding of data permissions and access control
Knowledge of different data sources and tools
Ability to troubleshoot common connection errors
Readiness to work with large-scale data systems
Who This Course Is For
Data analysts
Data engineers
Business intelligence professionals
Database administrators
Anyone working with enterprise data systems
Prerequisites
Basic SQL knowledge recommended
No prior enterprise experience required