SQL Server Integration Service is a fast and flexible data warehousing tool used for data extraction, transformation, and data loading. It makes it easy to load the data from one database to another database such as SQL Server, Oracle, Excel file, etc.

What is SSIS?

SSIS stands for SQL Server Integration Services.
It is a component available in the Microsoft SQL Server database software used to perform a wide range of integration tasks.
It is a data warehousing tool used for data extraction, loading the data into another database, transformations such as cleaning, aggregating, merging data, etc.
SSIS tool also contains the graphical tools and window wizard’s workflow functions such as sending email messages, ftp operations, data sources.
SSIS is used to perform a wide range of transformation and integration tasks. As a whole, the SSIS tool is used in data migration.

SSIS is a tool mainly used to perform two functionalities:

Data Integration
SSIS performs data integration by combining the data from multiple sources and provides unified data to the users.
Workflow
Workflow can be used to perform several things. Sometimes we need to execute some specific steps or a particular path which is either based on the time period or the parameter passed to the package or the data queried from the database. It can be used to automate the maintenance of SQL Server databases and provides the update to the multidimensional analytical data.

What is Data Integration?

Data Integration is a process that you follow to integrate the data from multiple sources. The data can be either heterogeneous data or homogeneous data. The data can be structured, semi-structured, or unstructured. In Data Integration, the data from different dissimilar data sources integrate to form some meaningful data.

Some methods are used to achieve data integration:

Data Modelling: In Data Modelling, you need first to create the data model and perform operations on it.
Data Profiling: Data Profiling is a process which is used to check the errors, inconsistency, or variations in the available data. Data Profiling ensures the data quality where data quality refers to the accuracy, consistency, and completeness of data.

Advantages of Data Integration:

Reduce data complexity
It reduces data complexity which means that the data can be delivered to any system. Data Integration maintains the complexity, streamlined connections, and making it easy to deliver the data to any system.
Data integrity
Data integrity plays a major role in data integration. It deals with cleansing and validating the data. Everyone wants high quality and robust data, so to achieve this data integration concept is used. Data integration is helpful in removing errors, inconsistency, and duplication.
Easy data collaboration
Accessibility comes under data collaboration. Accessibility means that the data can be easily transformed, and people can easily integrate the data into projects, share their results, and keep the data up-to-date.
Smarter business decisions
It also provides you to make smarter decisions. An integrated data refers to the transmit process within a company so that we can understand the information more easily. An integrated data is much easier and informative.

Why SSIS?

SSIS is used because of the following reasons:

Data can be loaded in parallel to many varied destinations
SSIS is used to combine the data from multiple data sources to generate a single structure in a unified view. Basically, it is responsible for collecting the data, extracting the data from multiple data sources, and merging into a single data source.
Removes the need of hard-core programmers
SSSIS is a platform that has the capability to load a large amount of data from excel to a SQL Server database.
Integration with other products
SSSIS tool provides tight integration with other products of Microsoft.
Cheaper than other ETL tools
SSSIS tool is cheaper than most of the other tools. It can resist with other base products, their manageability, business intelligence, etc.
Complex error handling within dataflows
SSSIS allows you to handle the complex error within a dataflow. You can start and stop the dataflow based on the severity of the error. You can even send an email to admin when some error occurs. When an error is resolved, then you can pick the path in between the workflow.

So, this brings us to the end of blog. This Tecklearn ‘Overview of SSIS and why SSIS is required’ blog helps you with commonly asked questions if you are looking out for a job in Microsoft BI. If you wish to learn Microsoft BI and build a career in Business Intelligence domain, then check out our interactive, Microsoft SSIS Course Training, that comes with 24*7 support to guide you throughout your learning period. Please find the link for course details:

https://www.tecklearn.com/course/microsoft-ssis-course-content/

Microsoft SSIS Course Training

About the Course

Tecklearn’s Microsoft SSIS training equips you with skills needed to work with SQL Server Integration Services (SSIS) for Business Intelligence. SQL Server Integration Services (SSIS) is a component of the Microsoft SQL Server database, it is used for performing a wide range of data integration tasks. This module provides detailed knowledge of data migration techniques, how to work with dataflow transformations, SSIS packages, event handling, implement checklists, deployment procedures, and much more through best practices.

Why Should you take Microsoft SSIS Training?

The Average salary for a SQL SSIS Developer is $104,740 – ZipRecruiter.com
Wells Fargo, United Health Group, Aetna, Conduent and many other MNC’s are using Microsoft SSIS.
Microsoft BI is a Leader in 2018 Gartner Magic Quadrant for Business Intelligence & Analytics Platforms (9th Consecutive Year).

What you will Learn in this Course?

Introduction to MSBI and Data Warehousing

Fundamentals of Data Warehousing
Concepts of Dimensions, Measures, Metadata and Schemas
Data Marts and Design approaches
Normalization and Denormalization and Schema types
Concepts of Online Analytical Processing and Transactional Processing
OLAP Cube
Explain ETL process and various tasks involved in it
Slowly Changing Dimensions Types
Business Intelligence Concepts
Working of BI with data-warehouse

Introduction to SSIS

Understanding of the MSBI Architecture
Import and Export wizard
Understand SSIS Architecture
Control Flow and its Components (Tasks, Containers and Precedence Constraints)
Data Flow and its Components (Source and Destination Connections and types of Transformations)
System Variables and User-defined variables
Scenarios by combining Control Flow and Data Flow components
Hands On

Transformations and Use-Cases

Data Conversion transformation
Multicast transformation
Union all transformation
Conditional Split Transformation
Merge and Merge Join Transformation
Lookup transformation
Cached Lookup transformation
Foreach loop and use-cases
Bulk-insert task
Archival process using dynamic variables and FST
Advancing Execute SQL Task with Object return type
Types of Outputs usage
Hands On

Slowly Changing Dimensions

Understanding data that slowly changes over time
Learning the process of how new data is written over old data
Detail explanation of three types of SCDs –Type1, Type2 and Type3, and their differences
Hands On

Overview of Fuzzy Look-up Transformation and Lookup and Term Extraction

Concept of Fuzzy matching
How Fuzzy Lookup Transformation varies from Lookup Transformation
Hands On

Concepts of Logging & Configuration

Learning about error rows configuration
Package logging
Defining package configuration
Understanding constraints and event handlers
Hands On

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