Azure Synapse Analytics
This is an integrated analytics service that is a part of the larger Azure data services family and was previously called SQL Data Warehouse. It offers one place to consume, clean, store process, and serve data to meet immediate business insights and machine learning requirements. This state-of-the-art tool helps data engineers and Data Scientists work together in order to design and implement efficient and scalable end-to-end analytics solutions.
Use Case:
An example use case is the utilization of Azure Synapse Analytics in terms of analytics and reporting. Several tools are provided directly through Azure Synapse to ingest data from a wide array of sources and perform analysis on the data. This helps in analyzing data and making decisions that are informed by data and this can occur throughout the organization.
Case Study:
In its real-life business, a manufacturing firm used Azure Synapse Analytics to improve the process of supply chain management. It would be ideal if they could make use of data from several resources: ERP systems, IoT sensors, and logistics platforms. In this case, by moving this data into Azure Synapse, it was possible to perform analytics of the supply chain performance in real time. They were also able to forecast usage and shortages and optimize pending inventory requirements through integrated machine-learning models. This led to overall betterment in operational efficiency, cost control and amelioration of customer satisfaction levels.
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
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