Training Data Integration in Microsoft SSIS is designed to enable data integration, such as the Extract-Transform-Load (ETL) process, which plays an important role in the data storage process.
- Data Analytics and Visualization
Data Integration in Microsoft SSIS
- Duration: 24 hours / 3 days
- Knowledge: Intermediate
- Trainer: Tomislav Hlupić
- Language: Croatian, English
- Location: Krste Pavletića 1, Zagreb
Date 1
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Date 2
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Price
3.600,00 Kn
*per attendee, price without VAT 25%
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Overview
Training Data Integration in Microsoft SSIS is designed to enable data integration, such as the Extract-Transform-Load (ETL) process, which plays an important role in the data warehousing process. In training, the principles of data integration, basic data transfer and transformation models, data flow management, error management, testing and the principle of extracting data from sources will be presented. Complete exercises will take place on the Microsoft SQL Server database, the market leader in the database market for the last ten years.
Audience
MISSING – Edukacija je namijenjena korisnicima koji posjeduju minimalno osnovna znanja SQL-a (preporučljivo napredna znanja) i koriste ili uvode sustave za integraciju podataka. Poželjno je poznavati osnove skladištenja podataka, no nije nužni uvjet.
What you will learn
Training is for intended who use the minimum basic SQL knowledge (preferably advanced knowledge) and use or introduce data integration tools in company. It is desirable to know the basics of data warehousing, but it is not a necessary condition.
Syllabus
1. Introduction to ETL process
In this module, you will learn the basics of the ETL process, explain each step in the processes and their role in data warehousing.
2. Data flow
Through the module the participants will master the basics of data flow, data collection and transfer principles and data transformation within the data flow. An introduction to the module is an overview of SSIS architecture, how to use SSIS, and the importance of reviewing source data.
- Workshop
3. Control flow
In the module student will learn data flow management using knowledge from the past module, creating dynamic packages using parameters and variables, and using different containers for advanced data flow management.
- Workshop
4. Error handling and debugging
In this module students will master the knowledge of packet debugging, ways of tracking performance, changing data, and writing down different events during packet execution. In addition, the module includes an upgrade over data by adding bug management.
- Workshop
5. Implementation of data extraction
This module covers the data extraction area as a part of the ETL process, the data collection principle in the data warehouse, incremental ETL process models, and architecture. Data extraction models include data values, Change Data Capture Process and Change Tracking Processes.
- Workshop
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