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Discover the nuanced dissimilarities between Data Lakes and DataWarehouses. Data management in the digital age has become a crucial aspect of businesses, and two prominent concepts in this realm are Data Lakes and DataWarehouses. It acts as a repository for storing all the data.
In this article we’re going to check what is an Azure function and how we can employ it to create a basic extract, transform and load (ETL) pipeline with minimal code. Extract, transform and Load Before we begin, let’s shed some light on what an ETL pipeline essentially is. ELT stands for extract, load and transform.
Machine learning and AI analytics: Machine learning and AI analytics leverage advanced algorithms to automate the analysis of data, discover hidden patterns, and make predictions. IoT analytics: IoT (Internet of Things) analytics deals with data generated by IoT devices, such as sensors, connected appliances, and industrial equipment.
A rigid data model such as Kimball or Data Vault would ruin this flexibility and essentially transform your data lake into a datawarehouse. However, some flexible data modeling techniques can be used to allow for some organization while maintaining the ease of new data additions.
It can ingest data in real-time or batch mode, making it an ideal solution for organizations looking to centralize their data collection processes. Its visual interface allows users to design complex ETL workflows with ease. Apache NiFi is used for automating the flow of data between systems.
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