Key Takeaways

Schedule Smart Ingest syncs from Snowflake, Databricks, BigQuery, or Redshift as often as every 15 minutes.

Schedule Smart Ingest syncs from Snowflake, Databricks, BigQuery, or Redshift as often as every 15 minutes.

Send engagement data out through Data Sync to Databricks, BigQuery, Redshift, AWS S3, and SFTP.

Send engagement data out through Data Sync to Databricks, BigQuery, Redshift, AWS S3, and SFTP.

Zero-copy means Snowflake queries a read-only share of our data; synced-in data lands on Iterable profiles.

Zero-copy means Snowflake queries a read-only share of our data; synced-in data lands on Iterable profiles.

Per our June 2026 announcement, Snowflake named us a Leader; its report analyzed 11,500+ customers’ usage.

Per our June 2026 announcement, Snowflake named us a Leader; its report .

Iterable integrations bring customer data in from Snowflake, Databricks, BigQuery, Redshift, and 20+ other sources through Smart Ingest . We send engagement data back out through Data Sync (Databricks, BigQuery, Redshift, AWS S3, and SFTP) and Snowflake Secure Data Sharing. Snowflake’s Leader recognition reflects .

What Connects to Iterable

Use this table to see which Iterable integrations move data into Iterable, out of Iterable, or both. Smart Ingest handles inbound warehouse and file syncs, and Data Sync handles outbound exports.

System

Direction

Method

What Marketers Get

Snowflake

Both

Smart Ingest (in); Snowflake Secure Data Sharing (out)

Schedule audience and event syncs into Iterable, and query Iterable engagement and profile data inside your own Snowflake account

Databricks

Both

Smart Ingest (in); Data Sync (out)

Bring Databricks tables into Iterable and export engagement data back to Databricks. We announced Databricks Validated Partner status in June 2026

BigQuery

Both

Smart Ingest (in); Data Sync (out)

Sync BigQuery data into Iterable and export system events, campaign data, and profile fields back to BigQuery

Redshift

Both

Smart Ingest (in); Data Sync (out)

Sync Redshift data into Iterable and return engagement data to Redshift for analysis

AWS S3

Both

Smart Ingest (in, CSV or JSON files); Data Sync (out)

Import CSV or JSON files from an S3 bucket and export Iterable data to S3

SFTP

Both

Smart Ingest (in, CSV files only); Data Sync (out)

Import CSV files from your SFTP server and export Iterable data back to it

Census

Into Iterable

Partner reverse extract, transform, load (ETL)

Sync warehouse data from Snowflake, Databricks, Redshift, BigQuery, and more into Iterable

Hightouch

Into Iterable

Partner reverse ETL

Send up-to-date warehouse data into Iterable for email, push, and SMS campaigns. Smart Ingest is co-developed with Hightouch

Segment

Both

Customer data platform (CDP)

Send Segment data into Iterable through the Iterable (Actions) destination, and send Iterable data to Segment as a source

RudderStack

Into Iterable

CDP

Connect RudderStack’s data collection and validation to Iterable through our RudderStack integration

Fivetran

Out of Iterable

Partner extract, load, transform (ELT) connector

Write Iterable event data to your warehouse through Fivetran’s connector

Each path runs on a schedule, so plan campaigns around these timing limits:

Iterable’s Data Sync documentation puts typical delivery at about 1–3 hours after a transfer begins, and up to 6–12 hours in some cases.

Iterable’s Data Sync documentation puts typical delivery at about 1–3 hours after a transfer begins, and up to 6–12 hours in some cases.

Iterable’s Data Sync documentation confirms no historical backfill: the first export is a current-state snapshot.

Iterable’s Data Sync documentation confirms no historical backfill: the first export is a current-state snapshot.

Iterable’s Smart Ingest documentation lists the most frequent sync schedule as once every 15 minutes.

Iterable’s Smart Ingest documentation lists the most frequent sync schedule as once every 15 minutes.

What Zero-Copy Means Here

Zero-copy access describes how your analysts reach Iterable data inside Snowflake. Iterable’s Snowflake Secure Data Sharing documentation describes a one-way, read-only share of profile data (including AI insights) and system event data. New events appear in your Snowflake account within 12 hours.

Stays in your warehouse: Your source tables and models, plus read-only access to Iterable profile and event data through Snowflake Secure Data Sharing.

Stays in your warehouse: Your source tables and models, plus read-only access to Iterable profile and event data through Snowflake Secure Data Sharing.

Lands in Iterable: The profile and event data you sync in through Smart Ingest, stored on the Iterable profile.

Lands in Iterable: The profile and event data you sync in through Smart Ingest, stored on the Iterable profile.

Data on the profile is what lets sends and journey decisions act at send time without querying your warehouse. Journeys Live Data, part of Journeys, keeps live data in memory for the journey path and writes it to the profile only when you choose. For example, a retailer can confirm stock before sending a product promotion.

Validated by Reality: The Snowflake Methodology

Snowflake’s annual report, The Modern Marketing Data Stack 2026, 5th Edition, highlights the technologies, solutions, and platforms its customers adopt. It helps organizations navigate the constantly changing data landscape. As we announced in June 2026, Snowflake named us a Leader in the Activation and Delivery category for the fourth consecutive year.

What makes this placement so significant is that it reflects real-world customer adoption. Rather than leaning on qualitative vendor surveys or subjective questionnaires, this report focuses on the actual platforms that enterprise organizations deploy to run daily marketing operations.

What it measures: Snowflake tracked , covering its fiscal year 2026.

What it measures: Snowflake tracked , covering its fiscal year 2026.

Sample: Snowflake’s Modern Marketing Data Stack, 5th Edition , Feb. 1, 2025–Jan. 31, 2026.

Sample: Snowflake’s Modern Marketing Data Stack, 5th Edition , Feb. 1, 2025–Jan. 31, 2026.

What “Leader” means: Leaders are the established technologies with the highest adoption and usage density in each category.

What “Leader” means: Leaders are the established technologies with the highest adoption and usage density in each category.

The result shows that teams already running Snowflake run our activation layer on top of it.

The Goal: Turning Marketing Into a Profit Center Through AI-Driven Messaging Decisions

Chief marketing officers (CMOs) are under intense pressure to transform marketing from a cost center into a high-performance profit center. Historically, this has been an uphill battle because legacy marketing clouds quietly absorb massive amounts of manual work while causing steep operational drag.

According to the Iterable 2026 Customer Engagement Report,

60% of marketers take 2–4 weeks to act on campaign learnings

60% of marketers take 2–4 weeks to act on campaign learnings

54% require 2–3 different teams just to make a change to a customer journey

54% require 2–3 different teams just to make a change to a customer journey

In a world plagued by this level of decision latency, human-driven, rules-based campaign infrastructure simply cannot scale. Snowflake’s The Modern Marketing Data Stack, 5th Edition frames the shift as “From tools that assist to agents that act.” When you replace rigid triggers with a system that reasons, the operational reality changes, and messaging starts producing revenue instead of absorbing manual work.

This is where pairing Iterable and Snowflake becomes a growth driver. When Snowflake’s centralized data architecture and our cross-channel activation engine stay aligned, marketers can change audiences and journeys with fewer engineering requests. Teams protect sender reputation with personalized messaging and grow customer lifetime value, which compounds into high-margin revenue.

The Foundation: Why Strong Data Integration Powers the AI Future

Achieving this level of marketing velocity requires more than a conceptual partnership. It demands deep architectural alignment, because you cannot build an adaptive, AI-driven messaging system on fragmented, delayed, or siloed data.

To feed a system that continuously reasons and adapts, the data underneath has to be complete and current.

A Partnership Built for Enterprise Scale

This is where our position in the Snowflake ecosystem becomes particularly important. Our , the highest tier in that category, as we announced in June 2026, marks another milestone.

This designation indicates that a partner has demonstrated significant customer success and adoption with its Data Cloud Product offerings on Snowflake.

How the Iterable + Snowflake Data Loop Works

Instead of relying on fragile custom integrations or manual syncs, our combined infrastructure relies on a highly efficient two-way data flow.

Customer data flows from Snowflake into Iterable: Marketers use Smart Ingest with a no-code visual setup after a one-time connection by your data admin.

Customer data flows from Snowflake into Iterable: Marketers use Smart Ingest with a no-code visual setup after a one-time connection by your data admin.

Engagement data flows back into Snowflake: We share engagement data back into your Snowflake account through Snowflake Secure Data Sharing, with read-only, zero-copy access.

Engagement data flows back into Snowflake: We share engagement data back into your Snowflake account through Snowflake Secure Data Sharing, with read-only, zero-copy access.

Why Closed-Loop Data Makes AI Smarter

This closed-loop system is precisely what makes AI messaging decisions smarter over time. As engagement data lands in Snowflake (new events appear within 12 hours), your teams can retrain centralized data models with signals such as:

Exact click behaviors

Exact click behaviors

Channel preferences

Channel preferences

Message fatigue signals

Message fatigue signals

Snowflake users can process these engagement signals to update risk profiles, lifetime value predictions, and attribution models, feeding refined insights back into Iterable for subsequent customer interactions. Rather than relying on static rules or one-time audience definitions, the system continuously learns from customer behavior and improves future engagement decisions.

Building the Infrastructure for Agentic Marketing

By securing this ecosystem alignment, enterprise brands get the data foundation that agentic marketing needs. We activate data from your warehouse and share engagement data back under one set of controls, preserving granular access governance.

We see the same pattern in our customers’ stacks: the fewer copies a team maintains, the easier it is to govern what AI agents can see and do.

Key Insights From The Modern Marketing Data Stack, 5th Edition

A closed-loop foundation prepares your data infrastructure for the next major shift in enterprise marketing. Snowflake maps out the market realities driving this change in The Modern Marketing Data Stack, 5th Edition.

The report centers on a critical new operational mandate: governing the agentic enterprise. That means building a marketing organization where AI agents operate within clear boundaries, with the right data, permissions, context, and human oversight. Here’s how we read the core shifts in the report:

From Copilots to Orchestrators: Owned channel execution is shifting from rule-based journey builders to goal-based orchestration. Large language model (LLM) adoption has progressed from task-level assistance toward systems that can reason, plan, and execute multi-step workflows across channels and applications.

From Copilots to Orchestrators: Owned channel execution is shifting from rule-based journey builders to goal-based orchestration. Large language model (LLM) adoption has progressed from task-level assistance toward systems that can reason, plan, and execute multi-step workflows across channels and applications.

Data Gravity Pulls the Stack Together: As customer, campaign, and operational data move toward a single governed source of truth, more work happens on the data platform itself rather than in dozens of downstream copies.

Data Gravity Pulls the Stack Together: As customer, campaign, and operational data move toward a single governed source of truth, more work happens on the data platform itself rather than in dozens of downstream copies.

The Ecosystem Matures Together: Composable architectures are replacing or joining monolithic designs, per Snowflake’s report. Individual tools now matter less than the systems connecting them, and vendors that once competed on features now compete on how well each operates within a governed data ecosystem.

The Ecosystem Matures Together: Composable architectures are replacing or joining monolithic designs, per Snowflake’s report. Individual tools now matter less than the systems connecting them, and vendors that once competed on features now compete on how well each operates within a governed data ecosystem.

Frequently Asked Questions (FAQ)

1. What Is the Snowflake Modern Marketing Data Stack Report?

Snowflake’s The Modern Marketing Data Stack, 5th Edition shows which marketing tools Snowflake customers actually run. For a buyer, it works as a shortlist of tools in each category that already operate alongside Snowflake at enterprise scale.

2. What Category Was Iterable Recognized For?

Snowflake named us a Leader in the Activation and Delivery category. This category highlights technologies that enable marketers to seamlessly mobilize data out of the Snowflake AI Data Cloud and deliver optimized customer experiences across owned channels (like email, push, and SMS) and paid media channels.

3. How Does Iterable Integrate With Snowflake?

Marketers schedule Snowflake syncs into Iterable themselves, so new audiences reach campaigns without an engineering ticket. Analysts query Iterable engagement data in your Snowflake account, right where your models already live. New engagement events show up in Snowflake within 12 hours, ready for your next model run.

4. What Does the AI Data Cloud Products Elite Partner Tier Mean?

As we announced in June 2026, Snowflake elevated us to its AI Data Cloud Products Elite Partner Tier, the highest tier in that category.

5. What Data Infrastructure Does Iterable Support?

We support the warehouses most enterprise teams already run: Snowflake, Databricks, BigQuery, and Redshift. We also work with storage and file paths (AWS S3 and SFTP) and partner tools, including Census, Hightouch, Segment, RudderStack, and Fivetran. Before you plan a migration, check our partner and integrations directory to confirm the path your stack needs.

6. Does Iterable Support Zero-Copy Data Activation?

Yes, for outbound engagement data: Snowflake Secure Data Sharing gives your Snowflake account read-only access to Iterable data without a file transfer. For inbound activation, the data you bring into Iterable lands on the customer profile, so sends can act on it without delay. If you’re weighing platforms on this point, see how we compare with Braze.

7. Which Data Warehouses Can Iterable Export To?

Data Sync exports to three warehouses (Databricks, BigQuery, and Redshift) and two storage destinations (AWS S3 and SFTP). Snowflake receives Iterable data through Snowflake Secure Data Sharing rather than Data Sync. If your analysts work in Snowflake, plan for Snowflake Secure Data Sharing; for other warehouses, plan for Data Sync. To see how the inbound and outbound paths fit together, read our Ingest Toolkit overview.