AWS S3 Storage
Amazon S3 or Amazon Simple Storage Service is a service offered by Amazon Web Services (AWS) that provides object storage through a web service interface. Amazon S3 uses the same scalable storage infrastructure that Amazon.com uses to run its e-commerce network. Amazon S3 can store any type of object, which allows uses like storage for Internet applications, backups, disaster recovery, data archives, data lakes for analytics, and hybrid cloud storage.
Our AWS S3 Storage DataLakeHouse.io integration:
- replicates AWS S3 Storage data to your Cloud Data Warehouse target
- synchronizes to your target destination at a scheduled frequency
It allows you to replicate/synchronize your S3 data files, including capturing snapshots of data at any point int time, and keep it up-to-date with little to no configuration efforts. You don’t even need to prepare the target schema — DataLakeHouse.io will automatically handle all the heavy lifting for you.
All you need is to specify the connection to your S3, point to your target system, or use a DataLakeHouse.io managed Data Warehouse and DataLakeHouse.io does the rest. Our support team can even help you set it up for you during a short technical on-boarding session.
DataLakeHouse.io securely connects to your AWS S3 Storage. Using the form in the DataLakeHouse.io portal please complete the following basic steps.
- Enter a Name or Alias for this connection, in the 'Name/Alias' field, that is unique from other connectors
- Enter a 'Target Schema Prefix', which will be the prefix for the schema at the target you will sync your data files into
- Enter a 'Bucket' name, where your files are stored
- Typically starts with s3:// or https://, so enter just the name without the prefix.
- Select your 'Region'
- Enter your 'Access Key', credentials to access the bucket
- Enter your 'Secret Key', credentials to access the bucket
- Enter any other optional details in the available fields (See the setup video if you need help or contact support)
- Folder Path, is a path on the root bucket from where desired files will be retrieved
- For JSON/GZ files, that are stored within nested folders, each file(s) in the subfolder(s) will be inserted into the same Target Connection table as the parent folder. The presumption is that the file structure is the same across all the files within the nested folders.
- File Pattern, is a regular expression (RegEx) used to isolated only certain files to be retrieved
- File Type, allows for a pre-determined type of file extension to be retreived
- JSON files stored in .gz compressed files will get ingested in the same manner as JSON files not stored in a .gz file
- Click the Save & Test button. Once your credentials are accepted you should be able to see a successful connection.
In the option for compression there are several options:
- GZIP
- Can contain multiple files.
- It doesn't matter if the file types are all of a kind or different formats for loading as long as they are compatible structures to JSON, CSV, etc. and this depends on the selected file type and will parse only the file type selected (JSON, CSV, etc.)
- GZ
- Compressed Version of JSON (a single JSON file)
- If file type is JSON then it will be an unzipped JSON File only
- ZIP
- Similar to how GZIP performs as described above.
SQL Transformations allow logic to be executed against a target connection based on a scheduled frequency or triggered event of new data on tables updated via DataLakeHouse.io (DLH.io). This especially helps when you want to control the data type set in your Target Connection since all columns are set as VARCHAR(16777216).
If any issues occur with the authorization simply return to the sources page in DataLakeHouse.io, edit the source details and click the 'Save & Test' button to confirm connectivity. If any issues persist please contact our support team via the DataLakeHouse.io Support Portal.
