Microsoft Azure Data Factory
Microsoft Azure Data Factory (ADF) is an integrated cloud data processing tool to build, program and manage data pipelines in a big data environment. It does support both ETL and ELT uses, so the raw data can be ingested and then transformed and loaded from numerous data sources. ADF also enables you to connect to virtually any on-premise or cloud data source and therefore can be effectively used as the foundation for building data integration solutions.
Use Case:
Hybrid data integration is one of the more typical scenarios for creating and using Azure Data Factory. Data are usually located in on-premises facilities and clouds to enhance functionality and efficiency in organizations. ADF can initiate these quite diverse sources, process them if needed, and load data into a single data warehouse or a data lake. This allows consolidation and convergence of analysis and reporting systems within the organisation.
Case Study:
To pinpoint data differences a global retail company Azure Data Factory to extract, transform and load data between their on-premises ERP system and cloud-based e-commerce platform. The company required a way to integrate sales, stock, and customer lists as a way of getting a concrete understanding of their activities. ADF was used to build pipelines that pulled data from both the ERP system and e-commerce platform, then converting the data into the required structure, and subsequently loading the data into Azure SQL Data Warehouse. The sales data from different channels were fed into this integrated data warehouse, thus presenting a real-time view of their sales performance, inventory stock and general customer behaviour for the company to adjust its supply chain and marketing strategies.
Top Data Ingestion Tools for 2024
To capture data for utilising the informational value in today’s environment, the ingestion of data is of high importance to organisations. Data ingestion tools are especially helpful in this process and are responsible for transferring data from origin to storage and/or processing environments. As enterprises deliver more diverse data, the importance of the right ingestion tools becomes even more pronounced.
This guide focuses on the top data ingestion tools 2024 detailing the features, components, and fit for organization applications to help organizations make the right choice for their data architecture plan.
Table of Content
- Apache NiFi
- Apache Kafka
- AWS Glue
- Google Cloud Dataflow
- Microsoft Azure Data Factory
- StreamSets Data Collector
- Talend Data Integration
- Informatica Intelligent Cloud Services
- Matillion ETL
- Snowflake Data Cloud
- MongoDB Atlas Data Lake
- Talend Data Integration
- Azure Synapse Analytics
- IBM DataStage
- Alteryx
Contact Us