The Microsoft Azure cloud platform can host virtually any sort of computing task, from simple web applications to full-scale enterprise systems. With many pre-built services for everything from data storage to advanced machine learning, Azure offers all the building blocks for scalable big data analysis systems including ingestion, processing, querying, and migration.
Azure consists of several components: the cloud operating system itself; SQL Azure, which provides database services in the cloud; and .NET services. Azure runs on computers that are physically located in Microsoft data centers.
Azure Databricks, a fully managed cloud service that provides powerful ETL, analytics, and machine learning capabilities. A blaringly new feature, it provides first party service which integrates seamlessly with other Azure services such as event hubs and Cosmos DB.
Clusters can be created in a blink of the eye, with capabilities of auto-scale and auto-terminate, pay only for run-time based on the number and size of the VM nodes. Unlock a true ETL and Data Warehousing feature with Spark Structured Streaming. This feature creates opportunities for (batch and streaming) ETL and analytics pipelines.
Integrate Data Factory, create and automate ELT pipelines. Provisioned for choosing from a wide range of sources, to name a few SQL Server, AWS S3, Oracle DB and 63 others, you can lift and shift your entire process and use Azure services for a complete and dynamic experience. Query and analyze data with U-SQL and Azure Data Lake services.
Our professionals, with expert knowledge and experience in Azure tools and technologies will provide custom made business solutions to fit perfectly with the customer’s business needs. To know more about the services listed below, contact Crossroad Elf. We are the Best Azure Data Engineering Services & Azure Data Engineering Company in Bangalore
1. Azure cloud services architecture
2. Building a data warehouse in Azure
3. How to choose the right Azure technology for your task
4. Calculating fixed and variable costs
5. Hot and cold path analytics
6. Stream processing with Azure Stream Analytics and Event Hub integration
7. Giving structure to distributed storage