This blog series assumes you are working in your own developer account for learning purposes and not working with anything production-related. Important: You should always follow the principle of least privilege when it comes to security. If you've done other exercises in the Hello, Cloud blog series, you may have already set up this permission. ![]() ![]() In this step, you'll give that user the built-in PowerUserAccess policy, intended for developers. The IAM user for your AWS toolkit user / AWS default profile needs a policy with the necessary permissions to interact with AWS services programmatically. Configure the toolkit to access your AWS account and create an IAM user. If you're using an older version of Visual Studio you won't be able to use. An AWS account, and an understanding of what is included in the AWS Free Tier.NET console program that queries the data with Athena via the AWS SDK for. Next, we'll teach Athena to understand the data and query the data from the AWS console. We’re going to create a data lake and upload expense report CSV files to it. You define table schema for one of more subsets of your data lake, for example server log TSV files or expense report CSV files, and after that you can query those tables. Amazon Athena is for querying your data lake. ![]() Multiple AWS services perform those functions. With a data lake, you can query, analyze, visualize, and run machine learning against your data as-is. We need different tools and approaches to work in this maelstrom, where data is rushing in at a furious pace and needs to be rapidly analyzed. As organizations cast an ever-wider net to increase their awareness, much of that data is going to be loose, mixed, and voluminous. If you're used to having your information in well-structured databases, a data lake might seem like a very messy thing in comparison. The data will grow, because a data lake is a living thing. In AWS, an S3 bucket is the store for a data lake, and the data can be in the form of JSON files, Parquet files, and delimited files such as comma-separated values (CSV) and tab-delimited values (TSV). The data in a data lake can come from people, mobile devices, applications, server logs, and/or IoT devices such as factory sensors. Data lakes are increasingly popular in the era of big data, and are particularly suited to organizations that need to mine their data and make decisions from it in its present form. A data lake is a place you can put your structured and unstructured data, without having to change it. To understand Athena, we first need to talk about data lakes. Amazon Athena: What is it, and why use It?Īmazon Athena is "an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL." Let that sink in: you get to use SQL to query data, but here the data is not in a database, it's in S3-in other words, you're querying against files. We'll do this step-by-step, making no assumptions other than familiarity with C# and Visual Studio. In this post we'll introduce Amazon Athena and the concept of data lakes, create a "Hello, Cloud" use of Athena, and use it in a. If you love C# but are new to AWS, or to this particular service, this should give you a jumpstart. In this Hello, Cloud blog series, we're covering the basics of AWS cloud services for newcomers who are.
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