Data Wrangling using Azure Data Factory: 1-Wk POC

Data-Core Systems, Inc.

Explore Data Wrangling features and capabilities with our experts

The term Data Wrangling has sometimes been misinterpreted as a process to cleaning and preparing the data from its raw format. But clean data may not always add value or establish the purpose of the project.

Data Wrangling is the process of mapping and transforming raw data into an acceptable format the would not only clean, organize and prepare the data but also add value to the data and make it appropriate to use for Data Analytics and Machine Learning activities.

Data-Core will provide a POC with any of your sample data with the objective to ensure: "* " The data should provide precise and valuable outcomes.
"* " Reduce the time spent is collecting and preparing the data.
"* " Help focus on other data analysis than preparing the data.
"* " Improve the data driven decisions in shorter time duration.

Implementation process to Wrangle the sample data using Azure Data Factory are as follows:

1. Collect Data: We will collect a sample dataset that can be of various types like structured, unstructured or semi-structured and stored in different data sources like on-premises or on the cloud. However, for this Proof-of-Concept the sample data will be collected and stored on an Azure Blob Storage.

2. Mapping Data Flow: A pipeline will be created, and the sample data will be then added as a source in the Azure Data Factory. The data will then go through a Mapping Data Flow where the data will be transformed to the desired schema as per the requirements.

3. Wrangling Data Flow: We will then prepare the data using the Wrangling Data Flow using various supported functions provided by Azure Data Factory to ensure that your data is wrangled and is valuable to be used for future Data Analytics or Machine Learning/AutoML purposes.

Data-Core's experienced team of Data Wranglers can build a state-of-the-art solution tending to your requirements by using Azure Data Factory or Power BI.