Talk to the fridge! (using Alexa, Salesforce and Electric Imp)

Long time no blog post, sorry. I have meant to write this post forever but I have managed to avoid it.

Anyways, consider the scenario when you sit in your couch and you wonder:
– “What’s the temperature in my fridge?”
– “Did I close the door?”
– “What’s the humidity?”

You have already installed your Electric Imp hardware in the Fridge (Best Trailhead Badge Ever) and it’s speaking to Salesforce via platform events, you even get a case when the temperature or humidity reaches a threshold or the door is open for too long.

But what if you just want to know the temperature? And you don’t have time to log into Salesforce to find out.

Alexa Skills to the rescue!

Thanks to this awesome blog post:

And this GitHub repository:

And example Flows from here:

I’ll walk you through what’s needed to speak to your fridge.

I will only show the small pieces you need for setting this up, for details please read the original blog posts.

First of all you need an Alexa Skill, I have created one called Salesforce.

This is the interaction model:

  "intents": [
      "intent": "FridgeStatus"

And the Sample Utterances

FridgeStatus How is my fridge

I’ll not go into details about Lambda and the connected app needed, please refer to this documentation:

The important thing here is the FridgeStatus in the Sample Utterances, you’ll need a flow called FridgeStatus.

Here’s mine:

Going into details:

And creating the response:

The Value is:

Your fridge temperature is {!Temperature} degrees celcius, the humidity is {!Humidity} percent, and the door is {!DoorStatus}

The result sounds like this:

So the next time you wonder about the temperature in the fridge you won’t have to move from the couch, awesome right?

The next step would be to ask Alexa about “What’s the average temperature during the last day?” and calculate the average from the BigObjects holding my temperature reading.


Uploading CSV data to Einstein Analytics with AWS Lambda (Python)

I have been playing around with Einstein Analytics (the thing they used to call Wave) and I wanted to automate the upload of data since there’s no reason on having dashboards and lenses if the data is stale.

After using Lambda functions against the Bulk API I wanted to have something similar and I found another nice project over at Heroku’s GitHub account called pyAnalyticsCloud

I don’t have a Postgres Database so I ended up using only the file and wrote this Lambda function to use it:

from __future__ import print_function

import json
from base64 import b64decode
import boto3
import uuid
import os
import logging
import unicodecsv
from uploader import AnalyticsCloudUploader

logger = logging.getLogger()

s3_client = boto3.client('s3')
username = os.environ['SF_USERNAME']
encrypted_password = os.environ['SF_PASSWORD']
encrypted_security_token = os.environ['SF_SECURITYTOKEN']
password = boto3.client('kms').decrypt(CiphertextBlob=b64decode(encrypted_password))['Plaintext'].decode('ascii')
security_token = boto3.client('kms').decrypt(CiphertextBlob=b64decode(encrypted_security_token))['Plaintext'].decode('ascii')
file_bucket = os.environ['FILE_BUCKET']
wsdl_file_key = os.environ['WSDL_FILE_KEY']
metadata_file_key = os.environ['METADATA_FILE_KEY']

def bulk_upload(csv_path, wsdl_file_path, metadata_file_path):
    with open(csv_path, mode='r') as csv_file:'Initiating Wave Data upload.')
        logger.debug('Loading metadata')
        metadata = json.loads(open(metadata_file_path, 'r').read())

        logger.debug('Loading CSV data')
        data = unicodecsv.reader(csv_file)
        edgemart = metadata['objects'][0]['name']

        logger.debug('Creating uploader')
        uploader = AnalyticsCloudUploader(metadata, data)
        logger.debug('Logging in to Wave')
        uploader.login(wsdl_file_path, username, password, security_token)
        logger.debug('Uploading data')
        uploader.upload(edgemart)'Wave Data uploaded.')
        return 'OK'

def handler(event, context):
    for record in event['Records']:
        # Incoming CSV file
        bucket = record['s3']['bucket']['name']
        key = record['s3']['object']['key']
        csv_path = '/tmp/{}{}'.format(uuid.uuid4(), key)
        s3_client.download_file(bucket, key, csv_path)

        # WSDL file
        wsdl_file_path = '/tmp/{}{}'.format(uuid.uuid4(), wsdl_file_key)
        s3_client.download_file(file_bucket, wsdl_file_key, wsdl_file_path)

        # Metadata file
        metadata_file_path = '/tmp/{}{}'.format(uuid.uuid4(), metadata_file_key)
        s3_client.download_file(file_bucket, metadata_file_key, metadata_file_path)
        return bulk_upload(csv_path, wsdl_file_path, metadata_file_path)

Yes the logging is a bit on the extensive side and make sure to add these environment variables in AWS Lambda:

SF_USERNAME - your SF username
SF_PASSWORD - your SF password (encrypted)
SF_SECURITYTOKEN - your SF security token (encrypted)
FILE_BUCKET- the bucket in where to find the mapping file
METADATA_FILE_KEY- the path to the metadata file in that bucket (you get this from Einstein Analytics)
WSDL_FILE_KEY - the path to the wsdl partner file in the bucket

I added an S3 trigger that runs this function as soon as a new file is uploaded. It has some issues (crashing with parenthesis in the file name for example) so please don’t use this for a production workload before making it enterprise grade.

Note: The code above only works in Python 2.7