Python Tutorial: Uploading and retrieving files

Want to learn more? Take the full course at https://learn.datacamp.com/courses/introduction-to-aws-boto-in-python at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- In the last lesson, we learned how to list, create and delete buckets. Now, it's time to put stuff in them. Let's take a look at how objects work. The files in S3 buckets are called objects. An object can be anything - an image, a video file, CSV or a log file. Managing objects is a key component of many data pipelines. Objects and buckets in S3 work somewhat like files and folders on our desktop. Each bucket has a name. Objects' names are called keys. A bucket name is just a name. An object's key is the full path of the object from the bucket's root. A bucket's name is unique in all of S3. An object's key is unique in the bucket. A bucket contains many objects. But an object can only belong to one bucket. First, we create the client and assign it to the s3 variable. Now we can perform operations on our objects and buckets. Let's upload an object into a Bucket. We upload the file using the client's upload_file method. The Filename is the local file path. Bucket parameter takes the name of the bucket we are uploading to. Key is what we want to name the object in S3. We are not capturing the return from this method in a variable. The method doesn't return anything. If there is an error, it will throw an exception. Whoo! Our file is now on S3! I've uploaded a few more objects for us to play with. Let's list them with boto3. Call the client's list_objects method, passing gid-requests for Bucket Name. Optionally, we can limit the response to two objects with the MaxKeys argument. If we omit it, S3 will return up to 1000 objects in our bucket if they exist. Another way to limit the response is to use the optional Prefix argument. Passing it will limit the response to objects that start with the string we provide. The response