| Cloud Engineer With Azure Synaps Exp. : San Francisco CA Hybrid at San Francisco, California, USA |
| Email: [email protected] |
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http://bit.ly/4ey8w48 https://jobs.nvoids.com/job_details.jsp?id=531907&uid= From: Pritam Singh, KPG99 [email protected] Reply to: [email protected] Job Title : Cloud Engineer - Azure Synaps/Terraform Location : San Francisco, CA - Hybrid 3 days a week Duration : 4+ Months Contract Visa: USC ONLY Must Have LinkedIn Azure Synapse Analytics and Terraform can intersect in the context of provisioning and managing resources for your data analytics and processing workflows on the Microsoft Azure cloud platform. Let me break down each concept and explain how they can work together: Azure Synapse Analytics (formerly SQL Data Warehouse): Azure Synapse Analytics is a cloud-based analytics service provided by Microsoft Azure. It's designed to process and analyze large volumes of data using both on-demand and provisioned resources. It integrates various data storage and processing capabilities, including data warehousing, data lakes, data integration, and big data analytics. Key features of Azure Synapse Analytics include: Data warehousing: You can store and manage large volumes of structured data for analytics and reporting. Data lakes: Integration with Azure Data Lake Storage allows you to analyze both structured and unstructured data. Data integration: You can ingest, transform, and prepare data from various sources for analysis. Big data analytics: It supports running complex analytical queries on large datasets. Terraform: Terraform is an open-source infrastructure as code (IaC) tool that allows you to define and manage your cloud resources and infrastructure using code. With Terraform, you can define your infrastructure in a declarative manner, and then use Terraform commands to create, update, and manage those resources. Terraform supports multiple cloud providers, including Azure. How Azure Synapse Analytics and Terraform Intersect: Terraform can be used to provision and manage the resources needed for your Azure Synapse Analytics environment. This includes creating and configuring various components such as Azure Synapse workspaces, SQL pools, serverless SQL endpoints, data integration pipelines, and more. Here's how the intersection between Azure Synapse Analytics and Terraform can work: Infrastructure Provisioning: You can use Terraform to define the infrastructure components required for Azure Synapse Analytics, such as virtual networks, storage accounts, Azure Data Lake Storage, and dedicated SQL pools. Resource Configuration: Terraform allows you to specify configurations for your Azure Synapse Analytics resources, such as specifying the number of data warehouse units (DWUs) for your SQL pools, setting up firewall rules, and defining authentication settings. Deployment Automation: By defining your Synapse Analytics infrastructure as code using Terraform, you can automate the deployment and management of your environment. This helps ensure consistency and repeatability in your deployment process. Version Control and Collaboration: Storing your infrastructure code in version control systems (e.g., Git) enables collaboration among team members and provides a history of changes made to the environment. Scalability and Flexibility: As your data analytics needs evolve, you can use Terraform to easily scale up or down the resources in your Azure Synapse Analytics environment to meet changing demands. To work with Azure and Terraform, you'll need to install the Azure provider for Terraform, which allows Terraform to interact with Azure APIs to create and manage resources. By combining the capabilities of Azure Synapse Analytics and Terraform, you can create a more streamlined and automated process for deploying and managing your data analytics infrastructure on Azure Thanks and Regards, Pritam Singh Sr. Technical Recruiter Keywords: information technology California http://bit.ly/4ey8w48 https://jobs.nvoids.com/job_details.jsp?id=531907&uid= |
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| 01:18 AM 16-Aug-23 |