The common problem in organizations is how to collect data from different sources, in a different format and move it to the data store. The destination data store and data format may also be different than the source systems. To load the data into the destination from the source, the data needs to be cleaned and structured. Also, it is important to know, how to perform predictive analysis over the data?
Nowadays, many companies are expressing interest in cloud-based data warehouses, ETL tools and Azure services are continuing to play a significant role in Data Integration and Data Standardization. ETL tools and Azure Data Factory exceeds this need by adding Mapping Data Flows, which allows visual and code-free data transformation. The data flow architecture consists of the following components which illustrate the mapping flow of data.
Fig. Data Flow System
A data source is a location where data is being used where it originates from. There are two types of data sources: machine data sources and file data sources. The machine data sources are stored on the system that is ingesting data and cannot be easily shared. The file data sources are stored in a file that is used by a single user or shared among several users. The most common data sources are Databases, Flat files, and Web Services. So, there are three shapes of data such as Structured, Semi-Structured, and Unstructured. Structured data is tabular data. Often such data is stored in databases like Teradata, Oracle DB, MS SQL, and IBM Dash DB. Semi-structured data is anywhere between Structured and Unstructured such as JSON or XML files. Unstructured data is the rawest form of data such as flat files (text files and CSV files). It is required to get all these structured, semi-structured, and unstructured data in one place for better decision making and analytics.
Extract, transform, and load (ETL) is a data pipeline used to collect data from multiple sources, transform the data under business rules, and load it into a destination data store. There are various ETL tools for data transformation such as Azure Data Factory, Talend, and Informatica that are compatible with Azure cloud services. The most common tools are: –
- Azure Data Factory – Azure cloud-based ETL service provides Azure Data Factory for serverless data integration, data movement, data transformation at scale, and security. It offers a code-less UI. It allows creating data-driven workflows for orchestrating data movement and transforming data.
- Talend – Talend is an open-source ETL platform that offers data integration and data transformation solutions. The tools provide features like cloud integration, big data, data integration, data preparation, and data quality. Talend offers 900+ drag-n-drop components and generates optimized code.
- Informatica – Informatica is an ETL tool that offers data integration, data quality, big data, cloud integration, data preparation, data masking, and data transformation. The tool can connect and fetch data from different sources and processing of data.
I would highly recommend using Azure Data Factory because it is a part of Azure Cloud Services and works well with Data Lake, HD Insight, SQL Server, Data Warehouse. Those ETL tools extract data from a various source system, transforms it in Azure Data Lake Storage and loads into Azure SQL Data Warehouse.
In such an architecture, Azure Data lake Storage Gen2 is one of the effective ways to store the data. ADLS Gen2 is very secure and scalable. It supports file-level security and hierarchical namespace with granular ACLs. Further, Azure ML Studio and Azure Databricks platform perform predictive analytics over the data. Azure ML Studio allows the implementation of Machine Learning algorithms which is a GUI-based integrated Machine Learning workflow. Azure Databricks provides machine learning with a fast and collaborative Spark-based platform where we can use Python, R, Scala with Spark. After the data transformation, all the data is stored in the Data Warehouse. Azure SQL Data Warehouse is a secure place to store organization data. The platform provides storage and instant scalability for huge amounts of data.
This architecture is mostly useful in areas like Healthcare organization, Retail & e-commerce area, Banking sector, and Service providers. Thus, Azure cloud services and ETL tools provide a method of transforming the data from various sources into a data warehouse for faster and easier access. They can perform a complex transformation on data and helps to migrate the data into Data Warehouse. Also, Azure Cloud service provides robust services for analyzing Big data. It provides Azure Data Lake Storage Gen2 to store data and then processes it using Spark using Azure Databricks.