DataWarehouseGroupTrainingRequirements.pdf
PDF 126 KB Posted
- Attached to
- Data Warehouse Training - USPS OIG
- Solicitation number
- 6HQOIG-24-A-00XX
- Issued by
-
United States Postal Service
About this file
The provided document is a set of training requirements for a data warehouse group training program, related to a pre-solicitation for "Data Warehouse Training - USPS OIG" issued by the United States Postal Service.
The key details are:
- The training will be conducted in-person or virtually, at the OIG's discretion, with class sizes capped at 15 students.
- The target audience is data analysts, data scientists, and data specialists.
- The instructor must have at least 8 years of practitioner experience and 3 years of instructor training experience.
- The training curriculum must be technology-agnostic and include hands-on lab exercises, preferably using Databricks or an open-source database/data warehouse, and SQL or PySpark.
- The training topics may include data warehouse concepts, data modeling, and best practices for naming conventions.
- The training must include pre- and post-assessments to measure efficacy.
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Other files
| File | Type | Posted |
|---|---|---|
| RFI - Data Warehouse Training Course - SAM.pdf |
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Training Requirements
a. Conduct Data Warehouse Group Training
i. Training will be held in-person or virtually, at the OIG’s discretion. If training is held in-person, additional travel costs will be incurred.
ii. Course materials will be provided to training participants electronically.
iii. Class size will be capped at 15 students per class. The Supplier may advise the OIG on the ideal class size, but the OIG has final discretion on the number of attendees assuming the number of participants is under the cap.
iv. The target audience are data analysts, data scientists, and data specialists
v. Curriculum Instructor must have background as practitioner for at least 8 years, and instructor training experience of at least 3 years
vi. Both the Instructor & training materials must be agnostic to technology, and not promote proprietary technology.
vii. Training must have hands-on lab exercises. Lab environment must be provisioned by the vendor for students to log in.
viii. Training must have pre and post assessments designed by the instructor to measure training efficacy.
ix. Prefer to have lab exercises held within Databricks. Using open-source database or data warehouse for lab exercises is ok. Having training attendees work with proprietary data warehouse for lab exercises is not ok.
x. Prefer to have lab exercises use SQL programming language. PySpark API is ok as an alternative.
xi. Data Warehouse topics may include, but are not limited to:
1. Data Warehouse basic and fundamental concepts
2. Database vs Data warehouse (OLTP vs OLAP)
3. Row vs Columnar storage
4. Data structures – common data types and its important role in data warehouse (e.g. integer, float, double, string, varchar(), date, datetime, binary). Complex data types (e.g. blobs, clobs, arrays, structs)
5. Data Modeling
a. Logical Data Models
b. Cardinality of relationships – one-to-one, one-to-many, many-to-many
c. Primary, foreign and compound keys
d. Star schema; fact & dimension table modeling
e. Normalization – First, second, third normal form
f. Slowly changing dimension tables (SCD)
g. Data Model validation & testing
6. Best practices for naming conventions for tables and views.
File details come from the government source that posted it. Updated .