Snowflake Secure Data Sharing
Snowflake is a purpose-built SQL cloud data warehouse for all of your data and all of your users. With Snowflake’s unique and patented architecture it’s easy to amass all of your data, enable rapid analytics, and derive data-driven insights for all of your users.
Braze leverages Snowflake’s Data Exchange to build a presence, find new customers, and expand reach through the ever-growing Snowflake customer base.
Learn more about this partnership here!
What is Data Sharing?
Data Sharing allows Braze to allow you secure access to data on our Snowflake portal without worrying about workflow friction or slowdown, failure points, and unnecessary costs that come with typical data provider relationships.
Integration
If you’re interested in this integration, reach out to your Braze Account or Customer Success Manager and ask them to consult Braze Data Strategy Services on Secure Data Sharing with Snowflake. This will get the cogs going inside Braze and we’ll have your views set up in no time!
You must have an account with Snowflake to use the Data Sharing Services.
Usage & Visualization
Similar to Currents, you can use your Secure Snowflake Data Sharing to…
- Create complex reports,
- Perform attribution modeling,
- Secure sharing within your own company,
- Map raw event or user data to a CRM (like Salesforce)…
And so much more!
After Braze sets up your views on Snowflake and grants you the access you need, we will send you some views. This data is similar to what you’d see in Currents.
You’ll have access to historical, current, and future data dating from the beginning of when that data was sent to Snowflake up until you choose not to utilize Snowflake anymore.
You can do whatever you want with the data, but we recommend using it in conjunction with our Looker Blocks.
Query Samples
Below, you can see query samples for two possible use cases.
You can use this Push Funnel query to aggregate push sends raw event data, through to deliveries raw event data, through to opens raw event data. This query shows how all the tables should be joined, since each raw event typically has a separate table.
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SELECT
COUNT(DISTINCT users_messages_pushnotification_send."ID" ) AS "users_messages_pushnotification_send.push_sent",
COALESCE((COUNT(DISTINCT users_messages_pushnotification_send."ID" )),0)-COALESCE((COUNT(DISTINCT users_messages_pushnotification_bounce."ID" )),0) AS "users_messages_pushnotification_send.push_delivered",
COUNT(DISTINCT users_messages_pushnotification_open."ID" ) AS "users_messages_pushnotification_open.push_opens"
FROM USERS_MESSAGES_PUSHNOTIFICATION_SEND AS users_messages_pushnotification_send
LEFT JOIN USERS_MESSAGES_PUSHNOTIFICATION_OPEN AS users_messages_pushnotification_open ON (users_messages_pushnotification_send."USER_ID")=(users_messages_pushnotification_open."USER_ID")
AND
(users_messages_pushnotification_send."DEVICE_ID")=(users_messages_pushnotification_open."DEVICE_ID")
AND
((users_messages_pushnotification_send."MESSAGE_VARIATION_ID")=(users_messages_pushnotification_open."MESSAGE_VARIATION_ID")
OR
(users_messages_pushnotification_send."CANVAS_STEP_ID")=(users_messages_pushnotification_open."CANVAS_STEP_ID"))
LEFT JOIN USERS_MESSAGES_PUSHNOTIFICATION_BOUNCE AS users_messages_pushnotification_bounce ON (users_messages_pushnotification_send."USER_ID")=(users_messages_pushnotification_bounce."USER_ID")
AND
(users_messages_pushnotification_send."DEVICE_ID")=(users_messages_pushnotification_bounce."DEVICE_ID")
AND
((users_messages_pushnotification_send."MESSAGE_VARIATION_ID")=(users_messages_pushnotification_bounce."MESSAGE_VARIATION_ID")
OR
(users_messages_pushnotification_send."CANVAS_STEP_ID")=(users_messages_pushnotification_bounce."CANVAS_STEP_ID"))
LIMIT 500
You can use this daily Email Messaging Cadence query to analyze the time between emails that a user receives.
For example, if a user received two emails in one day, they would fall under 0 “days since last received”. If they received one email on Monday and one on Tuesday, they would fall into the 1 “days since last received” cohort.
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WITH email_messaging_cadence AS (with deliveries as
(select TO_TIMESTAMP(time) AS delivered_timestamp,
email_address AS delivered_address,
message_variation_id as d_message_variation_id,
canvas_step_id as d_canvas_step_id,
campaign_name as d_campaign_name,
canvas_name as d_canvas_name,
id as delivered_id,
rank() over (partition by delivered_address order by delivered_timestamp asc) as delivery_event,
min(delivered_timestamp) over (partition by delivered_address order by delivered_timestamp asc) as first_delivered,
datediff(day, lag(delivered_timestamp) over (partition by delivered_address order by delivered_timestamp asc), delivered_timestamp) as diff_days,
datediff(week, lag(delivered_timestamp) over (partition by delivered_address order by delivered_timestamp asc), delivered_timestamp) as diff_weeks
from PUBLIC.USERS_MESSAGES_EMAIL_DELIVERY group by 1,2,3,4,5,6,7),
opens as
(select distinct email_address as open_address,
message_variation_id as o_message_variation_id,
canvas_step_id as o_canvas_step_id
FROM PUBLIC.USERS_MESSAGES_EMAIL_OPEN),
clicks as
(select distinct email_address as click_address,
message_variation_id as c_message_variation_id,
canvas_step_id as c_canvas_step_id
FROM PUBLIC.USERS_MESSAGES_EMAIL_CLICK)
SELECT * FROM deliveries
LEFT JOIN opens
ON (deliveries.delivered_address)=(opens.open_address)
AND ((deliveries.d_message_variation_id)=(opens.o_message_variation_id) OR (deliveries.d_canvas_step_id)=(opens.o_canvas_step_id))
LEFT JOIN clicks
ON (deliveries.delivered_address)=(clicks.click_address)
AND ((deliveries.d_message_variation_id)=(clicks.c_message_variation_id) OR (deliveries.d_canvas_step_id)=(clicks.c_canvas_step_id))
)
SELECT
email_messaging_cadence."DIFF_DAYS" AS "email_messaging_cadence.days_since_last_received",
(count(distinct email_messaging_cadence."OPEN_ADDRESS", email_messaging_cadence."O_MESSAGE_VARIATION_ID")
+count(distinct email_messaging_cadence."OPEN_ADDRESS", email_messaging_cadence."O_CANVAS_STEP_ID"))/(COUNT(DISTINCT email_messaging_cadence."DELIVERED_ID" )) AS "email_messaging_cadence.unique_open_rate"
FROM email_messaging_cadence
GROUP BY 1
ORDER BY 1
LIMIT 500
Braze Benchmarks
Braze Benchmarks will allow Braze prospects and customers alike to see how they compare to top players in their industry by comparing their metrics against Braze’s industry benchmarks.
We created a Data Tool for Braze Benchmarks so you can view a selection of the data, outside the Snowflake interface.
The initial industries are: Delivery Services, Ecommerce, Education, Entertainment, Finance, Gaming, Health, Lifestyle, Restaurants, Retail, Technology, Transportation, and Travel.
Our benchmarking data will also be available directly in the Snowflake Data Exchange.