![]() When we then check how this object’s metadata has been stored, we find out that it was labeled as binary/octet-stream. Starting from line 9, we first upload a CSV file without explicitly specifying the content type. Howeve r, doing it explicitly has some advantages. Often when we upload files to S3, we don’t think about the metadata behind that object. ![]() Specifying content type when uploading files Therefore, downloading and processing files, and then opening a single database connection for the Load part of ETL, can make the process more robust and efficient.īy using a temporary directory, you can be sure that no state is left behind if your script crashes in between ( Gist). Many analytical databases can process larger batches of data more efficiently than performing lots of tiny loads. ![]() near real-time streaming data), concatenate all this data together, and then load it to a data warehouse or database in one go. This can be useful when you have to extract a large number of small files from a specific S3 directory ( ex. Downloading files to a temporary directoryĪs an alternative to reading files directly, you could download all files that you need to process into a temporary directory. Similarly, if you want to upload and read small pieces of textual data such as quotes, tweets, or news articles, you can do that using the S3 resource method put(), as demonstrated in the example below ( Gist). Here is how you can directly read the object’s body directly as a Pandas dataframe ( Gist): Imagine that you want to read a CSV file into a Pandas dataframe without downloading it. Note: each code snippet below includes a link to a GitHub Gist shown as: ( Gist).ġ. In this article, we’ll look at various ways to leverage the power of S3 in Python. It became the simplest solution for event-driven processing of images, video, and audio files, and even matured to a de-facto replacement of Hadoop for big data processing. The simplicity and scalability of S3 made it a go-to platform not only for storing objects, but also to host them as static websites, serve ML models, provide backup functionality, and so much more. ![]() AWS Simple Storage Service (S3) is by far the most popular service on AWS. ![]()
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