ETL Testing with ADF-ADB-Pyspark - Mastering Cloud Data Quality Assurance
One-Q Soft offers you complete practical and Real-time ETL Testing training with basic to advanced concepts along with DWH Concepts.
Here in One-Q Soft– along with this course we also include concept wise documentation, Real-time scenario, Project solution, Project use case & interview preparation guidance for your job interviews.
Why only One-Q Soft
- 1400+ professionals trained on different technologies.
- Splendid Certified and Experienced Trainers.
- Highly Interactive & Classroom training
- Customised complete course notes
- Job guidance
- Guidance on official certification
ETL Testing with ADF-ADB-Pyspark Highlights
- 1400+ professionals trained on different technologies.
- Splendid Certified and Experienced Trainers.
- Highly Interactive & Classroom training
- Customised complete course notes
- Job guidance
- Guidance on official certification
Course Overview
ETL testing is done to ensure that the data that has been loaded from a source to the destination after transformation is performed. It also involves the validation of data in different stages used between source and destination using ADF ,ADB and Pyspark. ETL stands for Extract-Transform-Load.
Data Warehouse Testing is a testing to check for data integrity, reliability, accuracy and consistency in order to comply with the company’s data is accurate. The main purpose of data warehouse testing is to ensure that the data loaded into the data warehouse is accurate and satisfies the business logics so that the end users can make decisions on.
Apart from delivering you the best ETL testing training, you will also get many other extra learning concepts which include, advanced material for ETL testing interview questions and Job Guidance.
ETL Testing with ADF-ADB-Pyspark Course Curriculum
1 | Introduction to ETL testing |
2 | Importance of ETL testing |
3 | Difference between ETL and Application Testing. |
Oracle | |
4 | SQL -DDL, DML, DCL, TCL & DQL |
5 | SQL-Sub Queries & Case Statement |
6 | SQL -SET Operators |
7 | SQL -Window Functions |
8 | SQL -Joins |
9 | SQL -Views |
10 | SQL -Predefined Functions |
11 | SQL -Temporary Tables and CTE |
12 | SQL – Clauses |
13 | SQL- Aggregate Functions |
14 | SQL- Indexes |
15 | SQL -Procedures |
16 | SQL -User Defined Functions |
DATAWAREHOUSE | |
17 | What is Data Warehouse |
18 | Purpose of Data Warehouse |
19 | OLTP and OLAP |
20 | Data Marts and ODS [Operational Data Store] |
21 | Dimensional Modelling |
22 | Types of Dimensional Modelling |
23 | Dimension and Fact Tables |
24 | Normalization and Denormalization |
ETL Tool | |
25 | Introduction to Azure Data Factory |
26 | About Azure Components |
27 | Azure Data Lake Store |
28 | Azure Portal UI |
29 | Azure Data factory Sources and Targets |
30 | Azure Data Factory data flow Transformations |
31 | Integration Run time and Types |
32 | Creating Pipeline and Types of Loads |
33 | Deployment using GIT hub |
34 | Scheduling Pipelines |
35 | Audit logs Monitor |
36 | Triggers |
37 | Monitor Pipelines |
38 | Mail Configuration |
39 | CICD Pipelines |
40 | About Blob Storage and ADLS |
41 | Introduction to Databricks |
42 | Configuring Databricks |
43 | Clusters and configuring |
44 | Notebooks [Python, Scala, SQL, R, spark SQL] |
45 | Creating Data frames |
46 | Writing and Executing code in Notebook |
47 | Implementation of Transformation Logics |
48 | All ETL testing Validations in Notebook |
ETL testing Validations | |
54 | Source Files check |
55 | Metadata Check |
56 | Integrity check |
57 | Data Transformation check |
58 | Data difference check |
59 | Data quality check |
60 | Performance check |
61 | SQL queries for Testing |
62 | Creating SQL queries based on requirements |
63 | ETL Pipeline Checks |
64 | Practice-1 |
65 | Practice-2 |
66 | Practice-3 |
67 | Practice-4 |
68 | Practice-5 |