Batch: Google Cloud Storage (GCS)

The Banyan data lake is built atop of Google Cloud Storage (GCS). Each merchant brought onto the network will be provisioned their own GCS bucket that only they will be allowed to access. The data within the bucket will be encrypted at rest.

Each bucket will contain 3 folders: Input, Historical, Error. Merchants will write data into the Input folder. Once data is loaded into the folder, an automated ETL process is kicked off by Banyan and the data is moved into the historical folder. This data is not transformed in any way, it is an indicator that it has been processed. If data anomalies are found, those specific records will be written to files and placed in the Error folder for examination. Merchants will have read/write access to the Input folder, read only access to the Error folder and no access to the Historical folder.

Folders and File Naming Formats

Input

Files should be placed within the designated input folder on a regular cadence; our preference is at least daily
If the data is not sent as one file, please create a folder within the Input folder for each file type. Examples:
- Transactions
- Items
- Payments
- Stores
- Product Catalog
Your files may not match the above example, but this is close to how our internal data model stores receipts - a transaction may have multiple items, and it may have multiple payments
If the data is sent in more than one file, the files must contain common columns that make it clear how we will join this data, such as primary keys and foreign keys
Do not exceed an uncompressed size of 5GB per file
Create a subdirectory called "manifest" that will contain a daily manifest file that gets written at the end of your upload process. This file should contain the names of all files that were attempted to be uploaded on that day.
Filenames should at least have the date the type of data they contain in the filename
- example: 2022-03-04-transactions-0001.csv

Error
Files will be created in this folder when caught by our row level data checks. This data should be examined for completeness by the merchant and if there is an issue, revised and resent with a subsequent batch. Examples of data checks are:

Transactions which have no associated items and/or payments
Items or Payments that do not belong to a transaction
Extreme outlier amounts such as -$1,000,000

Updated
Files will be placed in this bucket if there was an issue with records/whole files sent previously that had the wrong date/time or unique transaction id. These types of updates cannot go through our normal pipeline and need to be handled in a more precise manner.

Amazon S3

Introduction

In order to integrate with Banyan's AWS solution, you will be using the AWS CLI sync command.

What we'll need from you

If your data is located in S3

This option is for situations where you have your files already in S3, and would like to move them from your bucket to ours.

Company Name
AWS Account ID
The bucket name of the data that will be copied over
The KMS Key ARN if your data is encrypted at rest

If the location is other then S3

This option is for situations where your data is on a drive or database and can be copied directly from a server.

Company Name
AWS Account ID

Creating a IAM user

Before you can assume the IAM Role created for your company, you have to create an IAM user which will be used by you to assume the set role. Since the role that we make references the IAM username, and the AWS Account ID that you provided us.

Navigate to the IAM console,

create a new user named banyan_input_s3.
Select programmatic access,
Hit next until you are shown your security credentials. Be sure to record them safely.
Once the user is created, attach the following inline policy.

📘
Important
Make sure to replace COMPANY_NAME with your company name.

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": "sts:AssumeRole",
            "Resource": "arn:aws:iam::356687812700:role/banyan_input_s3_merchant_COMPANY_NAME"
        }
    ]
}

Preparation before assuming the role

📘
Note

Install the latest version of the AWS CLI.

The following work needs to be done under a *UNIX system.

First, we need to make sure you have the right environment setup.

If you've used the AWS CLI before

If you have used the AWS CLI in the past, and run the aws configure command, you will have a folder called .aws in the home directory of the user you are logged in as in the OS, with all the files needed in place. You can skip the next step.

If you've never used the AWS CLI before

If you have never used the AWS CLI, following the instructions below to create the folder and files.

mkdir ~/.aws - to create the folder in your home directory.
touch ~/.aws/config - to create an empty file for the AWS CLI configuration.
touch ~/.aws/credentials - to create an empty file where the user credentials will go.

Setup the credentials

With the folder structure and files now in place, add the following content in the ~/.aws/config file, making sure to replace COMPANY_NAME with the one you provided:

[profile banyan]
role_arn = arn:aws:iam::356687812700:role/banyan_input_s3_merchant_COMPANY_NAME
source_profile = banyan_credentials

In the ~/.aws/credentials file, add the following content inside, making sure to replace DATA with the correct values you've saved when making the IAM user:

[banyan_credentials]
aws_access_key_id=DATA
aws_secret_access_key=DATA

Give us access to your resources

At this point all the policies are set for your role on our side, but since we are dealing with a cross account access, we can't just set in our policy that you can copy data from your bucket to ours, if this was the case anyone could access resources in different accounts. For this reason you have to give our account explicit access to your data for our Role that you are going to assume for it to work.

Bucket policy

Go to the bucket that you provided the name to
In the Permission tab scroll down until the Bucket policy section.
Click Edit, and add the following policy

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Principal": {
                "AWS": "arn:aws:iam::356687812700:root"
            },
            "Action": [
                "s3:ListBucket",
                "s3:GetObject",
                "s3:GetObjectTagging"
            ],
            "Resource": [
                "arn:aws:s3:::YOUR-BUCKET-NAME/*",
                "arn:aws:s3:::YOUR-BUCKET-NAME"
            ]
        }
    ]
}

KMS Key Policy

If you also provided us a KMS ARN Key, in this case you also have to update the Key Policy to allow our account to use the key to decrypt your data in the bucket by adding the following Policy Document in the already existing Key Policy

{
    "Effect": "Allow",
    "Principal": {
        "AWS": "arn:aws:iam::356687812700:root"
    },
    "Action": "kms:Decrypt",
    "Resource": "THE-FULL-ARN-OF-THE-KEY"
}

Transferring data to our bucket

📘
Important
Make sure to replace COMPANY_NAME with your company name.

Now that the data is in the right place, you can run the following command in the terminal which will copy the evaluation data in the folder where you'll run the command using the profile that you just made above. The CLI will take care of the IAM Role assumption.

Bucket to Bucket

In the terminal, run the following command to copy data from your bucket to ours.

aws s3 sync s3://YOUR-BUCKET-NAME s3://by-production-us-east-1-input-s3-COMPANY_NAME --delete --profile banyan

Drive to Bucket

In the terminal, run the following command to copy data from from the local drive to our bucket.

aws s3 sync . s3://by-production-us-east-1-input-s3-COMPANY_NAME --delete --profile banyan

Additionally you can copy, list and delete

Snowflake Data Onboarding with Banyan

Banyan integrates with Snowflake to allow seamless data exchange. This guide covers how to send data to Banyan (ingress) and receive data from Banyan (egress) using Snowflake's secure data sharing functionality.

Overview:

You can send data to Banyan via a secure view and direct share in Snowflake. Banyan will access this data securely.

Steps:

Prepare Data:
- Ensure your table has a timestamp column for efficient processing of new records.
Create a Secure View:
- Create a secure view encapsulating the data to share:
```
CREATE SECURE VIEW <view_name> AS
SELECT * FROM <your_table>;
```

Create and Share Data:

Create a share and grant Banyan access:

CREATE SHARE <share_name>;
GRANT USAGE ON DATABASE <your_database> TO SHARE <share_name>;
GRANT SELECT ON VIEW <view_name> TO SHARE <share_name>;
ALTER SHARE <share_name> ADD ACCOUNT GTB18971;

Schedule:
- Append data daily to the table to keep Banyan updated.
- If you need to do any data restatements, you can send the data with the same merchant_receipt_id with a new timestamp so that we reprocess the data.

Supported Formats:

Snowflake supports formats like JSON, Parquet, AVRO, CSV, etc.

SFTP

Introduction

Our SFTP solution allows you to send us data to ingest. When you send us the files, we process them in real time and subsequently delete the files from the server.

Connection requirements

You can use any SFTP client (for example, FileZilla) that supports a SSH Private Key for authorization.

Credentials

Once the contract is signed, you will generate an SSH public/private key pair, and share the public key with us. Banyan will then provide you with the hostname and user name for your SFTP server, which you will access with your private SSH key.

In general:

Server address: YOUR_COMPANY_NAME.sftp.getbanyan.com
User: sftpuser
Authentication: SSH Private Key

Where to upload

Once you are logged in to the server, please use the input folder under the data folder to upload the files. Within this folder, create subdirectories for each "type" of file you are uploading. If you have flattened your data to all be included into one file you do not have to do this step. Examples of file types:

Transactions
Items
Product Catalog
Tenders
Stores

Within the /data directory, also include a subdirectory called "manifest" that will contain a daily manifest file that gets written at the end of your upload process. This file should contain the names of all files that were attempted to be uploaded on that day. This will ensure the Banyan ingestion process can check for any missing files and delay the ingestion.

Schedule

Depending on your infrastructure we would prefer to receive the file at least once per day or as otherwise mentioned in your agreement with Banyan.

File format

We recommend files sent should be no bigger than 2GB in size.
Files should have the date of upload in the name, file type, as well as a part number if broken into multiple files within a single day.
- example: 2022-03-04-transactions-0001.csv

Caveats

When you’ll write your custom implementation to upload data to our SFTP server, make sure to take the following scenarios in to account:

The server can become unavailable for a short period of time. Make sure to have in place a retry mechanism.
The signature of the server might change due to hardware failure or changes in the hardware configuration. Make sure to take this into account (the URI won’t change).
Due to a limitation of the cloud storage backend, files cannot be updated in-place: they must first be deleted, and then re-uploaded.

Batch Onboarding Options

Batch: Google Cloud Storage (GCS)

Folders and File Naming Formats

Amazon S3

Introduction

What we'll need from you

If your data is located in S3

If the location is other then S3

Creating a IAM user

📘
Important

Preparation before assuming the role

📘
Note

If you've used the AWS CLI before

If you've never used the AWS CLI before

Setup the credentials

Give us access to your resources

Bucket policy

KMS Key Policy

Transferring data to our bucket

📘
Important

Bucket to Bucket

Drive to Bucket

Snowflake Data Onboarding with Banyan

Overview:

Steps:

Supported Formats:

SFTP

Introduction

Connection requirements

Credentials

In general:

Where to upload

Schedule

File format

Caveats

Batch: Google Cloud Storage (GCS)

Folders and File Naming Formats

Amazon S3

Introduction

What we'll need from you

If your data is located in S3

If the location is other then S3

Creating a IAM user

📘Important

Preparation before assuming the role

📘Note

If you've used the AWS CLI before

If you've never used the AWS CLI before

Setup the credentials

Give us access to your resources

Bucket policy

KMS Key Policy

Transferring data to our bucket

📘Important

Bucket to Bucket

Drive to Bucket

Snowflake Data Onboarding with Banyan

Overview:

Steps:

Supported Formats:

SFTP

Introduction

Connection requirements

Credentials

In general:

Where to upload

Schedule

File format

Caveats

📘
Important

📘
Note

📘
Important