Azure Data Factory Training
Introduction to Azure Data Factory Training:
Our Azure Data Factory training helps as the Azure’s cloud ETL service for scale-out serverless data integration and data transformation. It also offers a code-free UI for intuitive authoring and single-pane-of-glass monitoring and management. You can also lift and shift existing SSIS packages to Azure and run them with full compatibility in ADF. For more information register with us or dial our helpline to find best training guides for Azure Data Factory Corporate Training and Azure Data Factory Classroom Training and become a better executive. At Global Online Training we also provide Azure Factory Online Training from our real-time experts. Global Online Trainings is one of the best IT Training delivering Partners, we can gather up profound trainers for all the possible latest technologies at Hyderabad, Bangalore, Pune, Gurgaon and other such IT hubs.
Prerequisites for Azure Data Factory training:
- Should have knowledge of Business Intelligence.
- Should have sound knowledge of PowerShell and Microsoft Azure
- Knowledge of Hive / Pig / JSON Scripting is desirable
- A basic understanding of data flows and their uses
Azure Data Factory Corporate Training Course Outline:
- Course Name: Azure Data Factory Training
- Duration of the Course: 40 Hours (It can also be optimized as per required period).
- Mode of Training: Classroom and Corporate Training
- Timings: According to one’s Feasibility
- Materials: Yes, We are providing Materials for Azure Data Factory Corporate Training (We will get the soft copy material)
- Sessions will be conducted through WEBEX, GOTOMETTING or SKYPE
- Basic Requirements: Good Internet Speed, Headset.
- Trainer Experience: 10+Years
- Course Fee: Please register in our website, so that one of our agents will assist you.
What is Azure Data Factory?
Azure Data Factory is a cloud-based integration service that orchestrates and automates the movement and transformation of data. It works heavily on the data that you store.
This represents the collection of data within the data stores. The data passes through a pipeline for processing.
A pipeline consists of a group of activities, such as:
- Data movement activity
- Data transformation activity using:
- Stored procedures
After the data is transformed into the pipeline, we get an output dataset. Here, we get a structured form of data.
Linked Services :
The data from output datasets passes to linked services, such as:
- Azure Data Lake
- Block storage
Linked services contain information needed to connect to external sources. This is similar to the concept of a connection string in an SQL Server, where you define the source and destination of your data.
This connects your on-premises data to the cloud. It consists of a client agent that is installed on the on-premises data system, which then connects to the Azure data.
The data is analyzed and visualized using a number of analytical frameworks, like Apache Spark, R, Hadoop, and so on.