Amazon S3

Trying to establish a data lake(or maybe puddle) for my org's Data Sharing project. The idea is that outside partners would send cuts of their PHI data, regardless of format/variables/systems, to our Data Team who would then harmonize the data, create data marts, and eventually use it for something. End-to-end, I'm envisioning:

1. Ingestion->Secure, role-based, self service portal for users to upload data (1a. bonus points if it can preform basic validations/masking)
2. Storage->Amazon S3 seems like the cheapest. We probably won't need very big, even at full capacity. Our current storage is a secure Box folder that has ~4GB with several batches of test data, code, presentations, and planning docs.
3. Data Catalog-> AWS Glue? Azure Data Factory? Snowplow? is the main difference basically based on the vendor? We also will have Data Dictionaries/Codebooks from submitters. Where would they fit in?
4. Partitions-> I've seen Cassandra and YARN mentioned, but have no experience with either
5. Processing-> We want to use SAS if at all possible. What will work with SAS code?
6. Pipeline/Automation->The check-in and verification processes that have been outlined are rather involved. Some sort of automated messaging or approval workflow would be nice
7. I have very little guidance on what a "Data Mart" should look like, so I'm going with the idea that it would be another "experimental" partition. Unless there's an actual mart-building paradigm I've missed?
8. An end user might use the catalog to pull certain de-identified data sets from the marts. Again, role-based access and self-service gui would be preferable. I'm the only full-time tech person on this project, but I'm mostly an OOP, HTML, JavaScript, and some SQL programmer. Most of this is out of my repertoire. I've done a lot of research, but I can't be an effective evangelist without hands-on experience. Since we're starting a new year of our grant, they've finally decided to let me try some stuff out. Any pointers would be appreciated!

Hi, I'm working on a project to integrate dat from Shopify (e-commerce platform) to Amazon Quicksight. I'm thinking about which database to use, either Amazon S3 or MySQL.

Hi team, we are building up a ecommerce marketplace with both sides (sellers and buyers), now, we have made the forntend dev, but we are facing the choice of database selection. we would use Amazon S3 as cloud storage, but hard to choose the database between No-SQL (Heroku Postgres and MongoDB) For the tech stack; we are using Next.js and Node.js for the front and backend. so please share your professional input. thanks in advance

So, I have data in Amazon S3 as parquet files and I have it available in the Glue data catalog too. I want to build an AppSync API on top of this data. Now the two options that I am considering are:

1. Bring the data to Amazon DynamoDB and then build my API on top of this Database.

2. Add a Lambda function that resolves Amazon Athena queries made by AppSync.

Which of the two approaches will be cost effective?

I would really appreciate some back of the envelope estimates too.

Note: I only expect to make read queries. Thanks.

I have been building a website with Gatsby (for a small group of volunteers). I track it in GitHub and push it to Amazon S3.

I am satisfied with it as a single user; however, I would like to get non-technical teammates to be able to post Markdown blog posts. I tried to teach them to add mdx files, git push, gastby build, and publish with gatsby-plugin-s3, but I am getting a fair amount of resistance :).

So I wonder if there are tools, preferably using Node.js, that allow multi-user blog authors a la wordpress, i.e. with an interface for non technical bloggers, but producing static/pre-rendered web pages.

(PS: I am considering having a node/express.js server where they could upload their mdx file and the server would re-build push and publish for them, without having them install anything, but I'd like to know if something already exists before jumping into this endeavor)

I need a replacement for Amazon S3 storage, private storage replacement for s3, which one would you choose?

We are developing a system in which we have to collect 10 Million records every day. We need a database solution, NoSQL. data is simple logs. We are using AWS for now. I want to know the cheaper solution from both available techs. Amazon S3 or MongoDB.

We have 30 Tables that are collecting these logs.

Amazon S3 or CloudFlare as a CDN? Anyone like one over the other? My devs are switching to Amazon S3 but I would like other opinions - thank you in advance! Read a little how they differ, but would love an expert opinion. Thanks!

I have data stored in Amazon S3 bucket in parquet file format.

I want this data to be copied from S3 to Amazon Redshift, so I use copy commands to achieve this. But, I need to do this manually. I want to achieve this with some sort of automation such that if any new file comes into S3, it should be copied to the required table in redshift. Can you suggest what different approaches I can use?

Hi,

We are currently storing the data in Amazon S3 using Apache Parquet format. We are using Presto to query the data from S3 and catalog it using AWS Glue catalog. We have Metabase sitting on top of Presto, where our reports are present. Currently, Presto is becoming too costly for us, and we are looking for alternatives for it but want to use the remaining setup (S3, Metabase) as much as possible. Please suggest alternative approaches.