7 Best ETL Tools for Snowflake in 2026: Marketing Analyst's Guide

Marketing teams pour millions into Google Ads, Meta, LinkedIn, TikTok, and dozens of other platforms. Yet when it's time to report on performance, most analysts still waste hours manually exporting CSVs, reconciling mismatched schemas, and troubleshooting broken API connections.

Snowflake solves the storage and compute side of this equation — but without a purpose-built ETL layer, your data warehouse becomes another silo. The right ETL tool doesn't just move rows. It preserves marketing attribution logic, handles schema drift when platforms change APIs overnight, and transforms raw event streams into reportable metrics your CMO can trust.

This guide breaks down the 7 best ETL tools for Snowflake in 2025, evaluated specifically for marketing analytics workloads. You'll see how each handles connector breadth, transformation flexibility, cost predictability, and the schema chaos that kills most marketing data projects.

Key Takeaways

✓ Marketing ETL tools must support 200+ advertising, CRM, and analytics platforms — generic connectors miss critical attribution fields like UTM parameters, conversion windows, and cross-device identifiers.

✓ Schema preservation matters more than raw speed: when Google Ads deprecates a metric, your ETL should backfill historical data automatically, not break every dashboard downstream.

✓ Transformation logic belongs close to the source — pre-built marketing data models (cost per channel, ROAS by campaign, multi-touch attribution) eliminate months of SQL work and reduce join errors.

✓ Total cost of ownership includes hidden charges: per-row pricing, API overage fees, and the engineering time required to build custom connectors when your tool doesn't support a niche platform.

✓ Real-time sync is overrated for most marketing use cases — hourly incremental loads provide sufficient freshness for campaign optimization while controlling compute costs in Snowflake.

✓ Vendor lock-in risk is real: choose tools that output to standard Snowflake tables (not proprietary formats) so you can switch ETL providers without rebuilding your entire warehouse schema.

What Is ETL for Snowflake?

ETL (Extract, Transform, Load) for Snowflake refers to the process of pulling data from external sources, applying business logic and schema transformations, and writing the results into Snowflake tables for analysis. Unlike traditional ETL tools built for on-premise databases, Snowflake-native ETL leverages cloud-scale compute separation, automatic concurrency scaling, and semi-structured data support (JSON, Avro, Parquet).

For marketing teams, this means consolidating Google Ads spend data, Salesforce opportunity records, and HubSpot email engagement metrics into a single source of truth — then joining them on shared dimensions like campaign ID or customer email to calculate metrics like cost per qualified lead or multi-touch revenue attribution. The ETL layer handles API pagination, rate limiting, incremental updates, and the transformation logic required to make disparate schemas compatible.

How to Choose the Best ETL Tools for Snowflake: 6 Critical Criteria

Not all ETL tools are built for marketing data. Before evaluating vendors, define your requirements across these six dimensions:

1. Connector Coverage for Marketing Platforms

Generic ETL tools offer 100–200 connectors covering databases, SaaS apps, and file storage. Marketing-specific tools provide 500+ pre-built integrations for advertising platforms (Google Ads, Meta Ads, LinkedIn Ads, TikTok, Snapchat, Pinterest, Reddit, Quora), affiliate networks (Impact, CJ Affiliate, Rakuten), influencer platforms (CreatorIQ, AspireIQ), and regional ad exchanges.

More importantly, marketing connectors must extract granular fields: ad creative IDs, placement breakdowns, audience segment attributes, and conversion tracking parameters. A connector that only pulls top-line spend and impressions is useless for performance analysis.

2. Transformation Flexibility and Data Modeling

You have three architectural choices: ELT (extract-load-transform, where raw data lands in Snowflake and you transform via dbt), ETL (transform before loading), or hybrid approaches. Marketing teams benefit from pre-built data models that map platform-specific schemas to standardized dimensions: channel, campaign, ad group, creative, keyword, and conversion event.

Without this layer, you'll spend months writing SQL to reconcile Google Ads' camelCase field names, Meta's nested JSON structures, and LinkedIn's hyphenated column headers into a queryable format.

3. Schema Change Handling and Historical Backfills

Ad platforms deprecate metrics, rename columns, and change data types with minimal warning. Google Ads removed average position in 2019. Meta retired 28-day attribution windows in 2021. TikTok changes conversion event schemas quarterly.

The best ETL tools detect schema drift automatically, backfill historical data to maintain continuity, and alert you before breaking changes hit production dashboards. Inferior tools simply stop syncing, leaving you with incomplete datasets and no audit trail.

4. Incremental Sync Performance and API Rate Limits

Full table reloads waste Snowflake compute credits and hit API rate limits on high-volume platforms like Google Ads (where enterprise accounts generate millions of rows per day). Incremental sync based on modification timestamps or append-only logs reduces data transfer by 90%+.

However, not all platforms support reliable incremental extraction. Facebook's Marketing API doesn't guarantee stable IDs for deleted campaigns. Salesforce's getUpdated() method misses records when users bulk-delete and restore data. Your ETL tool must handle these edge cases without silently dropping rows.

5. Cost Structure and Pricing Transparency

ETL pricing models vary wildly: per-row metering (Fivetran), monthly active rows (Stitch), flat-rate tiers (Matillion), or usage-based compute (Airbyte Cloud). For marketing teams syncing billions of ad impressions, per-row pricing becomes prohibitively expensive.

Watch for hidden charges: custom connector development fees ($10K–$50K per source), professional services for data modeling, API overage penalties, and Snowflake compute costs triggered by inefficient transformations that scan entire tables instead of using incremental merge logic.

6. Data Governance and Compliance Certifications

Marketing data includes PII (customer emails, phone numbers, IP addresses) subject to GDPR, CCPA, and HIPAA regulations depending on your industry. Your ETL tool must support field-level encryption, PII masking, audit logging, and SOC 2 Type II certification at minimum.

Budget validation rules prevent overspend: if your tool detects Google Ads daily spend exceeding $50K (when your approved budget is $30K), it should block the sync and alert your team before the data hits Snowflake and triggers automated bidding scripts.

1. Improvado: Marketing-First ETL with AI-Powered Analytics

Improvado is a marketing analytics platform built specifically for multi-channel campaign data. It combines 500+ pre-built connectors, marketing-specific transformation logic, and an AI Agent that answers natural-language questions over your entire Snowflake dataset.

Marketing Data Governance Engine with Pre-Launch Validation

Improvado's standout feature is its governance layer: 250+ pre-built validation rules that check data quality before it reaches Snowflake. Budget pacing rules flag when daily spend deviates 20%+ from planned allocation. Schema consistency checks ensure UTM parameters follow your taxonomy (campaign names match regex patterns, source/medium pairs conform to approved values). Duplicate detection prevents the same conversion event from being counted twice when a user clicks an ad, visits via organic search, then converts.

For enterprise marketing teams managing $50M+ annual ad budgets, this prevents the catastrophic failures that occur when bad data triggers automated bidding algorithms. One Improvado customer (a Fortune 500 retailer) detected a $2.4M budget overrun in Google Ads 18 hours before month-end close — early enough to pause campaigns and reallocate spend, avoiding a financial restatement.

The platform stores 2 years of historical data snapshots, so when Google Ads deprecates a metric, you can backfill using Improvado's archive rather than losing trend analysis. Custom connector builds take 2–4 weeks under SLA (compared to 3–6 months with in-house development), and the service includes a dedicated Customer Success Manager plus professional services for data modeling — not sold as an add-on.

When Improvado Is Not the Right Fit

Improvado is purpose-built for marketing analytics. If your primary use case is syncing ERP data, IoT sensor streams, or application database replication, general-purpose ETL tools like Fivetran or Airbyte offer broader connector libraries for non-marketing sources. Pricing is transparent but reflects the platform's enterprise feature set — startups spending under $100K annually on advertising may find better ROI with lighter-weight tools.

The AI Agent requires structured data models to deliver accurate insights. If your Snowflake schema is highly customized with non-standard naming conventions, you'll need to invest time mapping your tables to Improvado's semantic layer before conversational analytics works reliably.

2. Fivetran: High-Volume Replication for Data Engineers

Fivetran is a fully managed ELT platform optimized for replicating large datasets into Snowflake with minimal configuration. It supports 300+ connectors spanning databases, SaaS applications, event streams, and file storage, with enhanced reverse ETL capabilities for syncing Snowflake data back to operational systems.

Proven Throughput for Enterprise-Scale Workloads

Fivetran excels at raw data movement speed. According to the company's published benchmarks, the platform handles high-volume replication exceeding 500 GB per hour for historical syncs and 250+ GB per hour for incremental syncs. For marketing teams analyzing clickstream data from millions of web sessions, this throughput ensures overnight batch jobs complete before business hours.

The connector architecture prioritizes reliability over customization. Fivetran maintains each integration in-house, pushing updates automatically when source APIs change. This reduces maintenance burden but limits flexibility — you cannot modify transformation logic within Fivetran's pipeline. All transformations happen downstream in Snowflake using dbt or SQL-based models.

Cost Predictability Challenges at Scale

Fivetran's per-row pricing model (Monthly Active Rows, or MAR) becomes expensive for high-volume marketing datasets. If you sync Google Ads search query reports (one row per keyword × impression), Facebook ad delivery data (one row per ad × hourly interval), and web analytics events, you can easily exceed 100M MAR within a single month. At Fivetran's published rates, this translates to $20K–$40K monthly spend before Snowflake compute costs.

The platform lacks marketing-specific governance features. There's no built-in budget validation, UTM taxonomy enforcement, or pre-built attribution models. Data engineers appreciate Fivetran's simplicity, but marketing analysts must build every transformation themselves or rely on dbt packages maintained by the community.

3. Matillion: Cloud-Native ETL with Visual SQL Transformations

Matillion is a Snowflake-native ETL tool designed for users who prefer visual drag-and-drop pipelines over writing raw SQL. It runs entirely within your Snowflake environment, using Snowflake's compute resources for transformations rather than requiring separate infrastructure.

SQL Pushdown Efficiency for Complex Joins

Matillion's key advantage is ELT architecture: extract raw data into Snowflake staging tables, then transform using Snowflake's compute engine via generated SQL. This avoids the data egress costs and latency penalties of tools that pull data out of Snowflake, transform in a separate environment, then load results back.

For marketing teams joining Google Ads campaign data with Salesforce opportunity records and HubSpot email engagement metrics, Matillion's visual pipeline builder simplifies multi-table joins. You define relationships graphically, and Matillion generates optimized SQL with proper indexing hints and partition pruning.

Limited Marketing Platform Coverage

Matillion's connector library skews toward databases, cloud storage, and enterprise SaaS apps (Salesforce, NetSuite, Workday). Coverage of advertising platforms is sparse — you'll find Google Ads and Facebook Ads, but specialized connectors for TikTok, Snapchat, Pinterest, Reddit, or affiliate networks require custom development using Matillion's API component (which assumes your team has engineering resources to maintain it).

The visual interface appeals to analysts comfortable with SQL but unfamiliar with Python or Java. However, version control and CI/CD workflows are cumbersome compared to code-first tools. Matillion stores pipeline definitions in XML, making Git diffs unreadable and peer review impractical for teams practicing infrastructure-as-code.

4. Stitch: Developer-Friendly Open-Source Framework

Stitch (owned by Talend) is an ELT platform built on the Singer open-source specification. It appeals to engineering teams that want to contribute custom connectors to a shared ecosystem while benefiting from managed infrastructure for core pipelines.

Open-Source Extensibility for Niche Platforms

Stitch's Singer taps (extractors) and targets (loaders) are Python scripts published on GitHub. If you need to sync data from a regional ad network not supported by commercial ETL vendors, you can write a Singer tap yourself or hire a contractor — the specification is well-documented and includes helper libraries for OAuth, pagination, and state management.

This extensibility comes at a cost: you're responsible for maintaining any custom taps you deploy. When the ad network changes its API, you must update your code and redeploy. For marketing teams without dedicated data engineering headcount, this maintenance burden quickly outweighs the upfront cost savings.

No Transformation Layer for Marketing Metrics

Stitch is purely ELT — it loads raw JSON responses from APIs into Snowflake, then stops. Calculating derived metrics like cost per acquisition, return on ad spend, or multi-touch attribution requires writing SQL transformations yourself. There are no pre-built marketing data models, no schema mapping for platform-specific fields, and no governance rules to validate UTM consistency or budget pacing.

For small teams running 5–10 ad platforms, this DIY approach is manageable. For enterprise marketing organizations with 50+ data sources and complex attribution requirements, the engineering investment required to operationalize Stitch exceeds the cost of a purpose-built marketing analytics platform.

5. Talend: Enterprise Data Quality Suite

Talend is an enterprise integration platform combining ETL, data quality, master data management, and API services. It targets organizations with complex data governance requirements across multiple departments — not just marketing, but also finance, supply chain, and customer service.

Built-In Data Profiling and Cleansing Rules

Talend's data quality module scans incoming data for anomalies: duplicate records, null values in required fields, invalid email formats, or outlier values (e.g., a Google Ads click-through rate of 847% indicates a tracking error). You define quality thresholds, and Talend quarantines rows that fail validation rather than loading bad data into Snowflake.

For regulated industries (financial services, healthcare, pharmaceuticals), this built-in governance satisfies audit requirements. Marketing teams benefit when syncing customer PII from multiple sources — Talend can deduplicate email addresses, standardize phone number formats, and geocode mailing addresses using integrated third-party enrichment services.

Steep Learning Curve and Deployment Complexity

Talend's feature breadth creates operational overhead. The platform requires Java runtime environments, dedicated application servers, and database storage for its metadata repository. Cloud-managed offerings (Talend Cloud) reduce infrastructure burden but inherit the same complex UI designed for enterprise IT teams, not marketing analysts.

Connector coverage for modern advertising platforms is limited. Talend prioritizes database replication (Oracle, SQL Server, MySQL) and legacy enterprise apps (SAP, Siebel) over digital marketing tools. Syncing data from TikTok Ads, Snapchat, or influencer platforms requires custom development using Talend's tREST or tHTTP components — a multi-week project even for experienced developers.

6. Informatica: Legacy Enterprise Integration Platform

Informatica Intelligent Cloud Services (IICS) is a cloud-based evolution of the company's PowerCenter ETL tool, which dominated on-premise data warehousing for two decades. It remains popular in Fortune 500 enterprises with existing Informatica investments and strict procurement requirements.

Centralized Metadata Catalog for Lineage Tracking

Informatica's Enterprise Data Catalog tracks data lineage across every pipeline: when you query a Snowflake table showing revenue by marketing channel, the catalog reveals which Google Ads API endpoints, Salesforce objects, and transformation logic contributed to each row. For compliance audits or troubleshooting attribution discrepancies, this visibility is invaluable.

The platform also supports complex orchestration scenarios: triggering Snowflake transformations only after upstream CRM data loads complete, running data quality checks before promoting staging tables to production schemas, and coordinating cross-system workflows (e.g., syncing Snowflake aggregates back to Salesforce for territory planning).

Prohibitive Licensing Costs for Mid-Market Companies

Informatica's pricing model reflects its enterprise heritage: per-connector licensing, per-user fees for administrators, and separate SKUs for data quality, master data management, and API management. A typical deployment for a mid-market company costs $100K–$300K annually before professional services.

Connector development for new advertising platforms is slow. Informatica releases quarterly update bundles rather than pushing changes continuously, so support for emerging platforms (TikTok, Reddit Ads, new Amazon Advertising APIs) lags 6–12 months behind specialized marketing ETL vendors. The platform excels at database replication but lacks pre-built marketing data models or attribution logic.

7. Airbyte: Open-Source Connector Framework

Airbyte is an open-source ELT platform founded in 2020, offering both self-hosted and cloud-managed deployment options. It competes with Fivetran and Stitch by providing a no-code connector builder and a community-contributed connector library with 300+ sources.

Rapid Custom Connector Development

Airbyte's Connector Development Kit (CDK) lets developers build new connectors in Python with minimal boilerplate. The framework handles OAuth flows, pagination, rate limiting, and incremental sync state management automatically. A developer familiar with the CDK can create a basic API connector in 4–8 hours, compared to weeks of work using lower-level HTTP libraries.

This speed benefits marketing teams integrating niche platforms: regional ad exchanges, white-label influencer networks, or proprietary attribution tools built in-house. Airbyte's community actively maintains connectors for Google Ads, Facebook Ads, LinkedIn Ads, and Salesforce, with most updates merged within days of API changes.

Production Readiness and Support Limitations

Airbyte's open-source model means connector quality varies. Community-maintained sources may lack comprehensive error handling, retry logic, or support for incremental sync modes. The Google Ads connector, for example, works well for standard reports but struggles with custom columns or conversion tracking edge cases that require deep platform expertise.

Self-hosted deployments require DevOps resources to manage Kubernetes clusters, monitor pipeline health, and upgrade versions. Airbyte Cloud eliminates infrastructure overhead but charges per-connector pricing similar to Fivetran, reducing the cost advantage. For marketing teams without data engineering support, the operational burden of maintaining Airbyte in production exceeds the licensing cost of fully managed alternatives.

ETL Tools for Snowflake: Feature Comparison

How to Get Started with ETL for Snowflake

Implementing ETL for Snowflake follows a predictable path regardless of which tool you choose. The steps below assume you're migrating from manual CSV exports or fragmented point-to-point integrations to a centralized marketing data warehouse.

Step 1: Audit your current data sources and define must-have connectors. List every platform generating marketing data: ad platforms (Google, Meta, LinkedIn, TikTok), analytics tools (Google Analytics, Adobe Analytics, Amplitude), CRM systems (Salesforce, HubSpot, Marketo), and attribution providers (Rockerbox, Neustar, Google Analytics 360). Prioritize sources by data volume and business criticality — if Google Ads represents 60% of your paid media spend, that connector is non-negotiable.

Step 2: Define your target schema in Snowflake before connecting sources. Decide whether you'll use a star schema (fact tables for events, dimension tables for campaigns/creatives/audiences) or a wide denormalized format. Pre-build staging, transformation, and presentation layers. This upfront design prevents the "data swamp" anti-pattern where raw API responses pile up in Snowflake with no governance or documentation.

Step 3: Pilot with 3–5 high-volume sources and validate data quality. Connect your most important platforms first. Run parallel loads for 2–4 weeks, comparing ETL output against manual exports to catch discrepancies. Check for missing rows (API rate limits causing incomplete syncs), duplicate records (retry logic creating duplicate inserts), and schema drift (new columns appearing without warning).

Step 4: Build transformation logic and test attribution accuracy. Write SQL to join ad platform data with CRM conversion events and web analytics sessions. Calculate test metrics (cost per lead, return on ad spend, customer acquisition cost) and compare against source platform dashboards. Discrepancies often stem from timezone mismatches, conversion window differences, or attribution model conflicts (last-click vs. multi-touch).

Step 5: Automate governance checks and alerting before going production. Implement budget pacing rules, schema validation tests, and anomaly detection (e.g., alert when daily Google Ads spend drops below $1K, indicating a tracking failure). Configure Slack or email notifications for pipeline failures so your team can respond before stakeholders notice missing data in dashboards.

Step 6: Train end users and establish a cadence for connector additions. Marketing analysts need SQL training or access to a BI tool that abstracts query complexity. Schedule monthly reviews to evaluate new platform requests — as your team experiments with emerging channels (Reddit Ads, Nextdoor, streaming TV), add connectors proactively rather than scrambling during campaign launches.

Conclusion

Choosing the best ETL tool for Snowflake depends on your team's technical depth, connector requirements, and tolerance for DIY engineering work. Data engineering teams comfortable writing Python and managing infrastructure will find Airbyte or Stitch cost-effective for general-purpose replication. Enterprise IT organizations with existing Informatica or Talend investments can extend those platforms to Snowflake, though at significant licensing cost.

For marketing teams specifically, the calculus is different. You need 200+ pre-built connectors for advertising platforms, not just databases and SaaS apps. You need transformation logic that understands marketing attribution, not generic SQL generators. You need governance rules that prevent budget overruns and schema chaos, not just data movement at scale.

Improvado delivers these marketing-specific capabilities out of the box: 500+ connectors maintained by domain experts, pre-built data models that eliminate months of SQL work, schema change preservation with 2-year historical backfills, and an AI Agent that answers natural-language questions over your entire Snowflake dataset. It's purpose-built for the use case where Fivetran, Matillion, and open-source tools require extensive customization.

The ROI math is straightforward. If your marketing analysts currently spend 10 hours per week manually exporting CSVs, reconciling discrepancies, and troubleshooting broken API connections, that's 520 hours annually — roughly $52K in fully loaded salary cost for a mid-level analyst. A purpose-built marketing ETL platform eliminates this waste while improving data accuracy, enabling faster campaign optimizations, and reducing the risk of catastrophic errors (like the $2.4M budget overrun one Improvado customer caught 18 hours before month-end close).

Frequently Asked Questions

What is the difference between ETL and ELT for Snowflake?

ETL (Extract-Transform-Load) applies business logic and schema transformations before data reaches Snowflake, often using a separate processing engine. ELT (Extract-Load-Transform) loads raw data into Snowflake first, then transforms it using Snowflake's compute via SQL or dbt models. ELT is generally preferred for Snowflake because it leverages the platform's elastic compute scaling and eliminates data egress costs. However, marketing teams benefit from ETL's pre-load transformations when dealing with complex API responses (nested JSON from Facebook Ads, paginated results from Google Ads) that are easier to normalize outside Snowflake than with pure SQL.

Do I need real-time data sync for marketing analytics?

Real-time sync (sub-minute latency) is overkill for most marketing use cases. Campaign performance metrics stabilize over hours, not seconds — ad platforms batch conversion data, attribution windows span days, and intraday optimizations rarely require minute-by-minute updates. Hourly incremental sync provides sufficient freshness for dashboards and automated bidding while controlling Snowflake compute costs. Reserve real-time streaming (via Kafka, Kinesis, or Snowpipe) for high-value scenarios like fraud detection or live event promotion where immediate action matters.

How long does it take to build a custom connector for a niche ad platform?

Timeline varies by platform complexity and your ETL tool's framework. Using Airbyte's Connector Development Kit, an experienced Python developer can build a basic REST API connector in 4–8 hours. Adding incremental sync, OAuth authentication, and comprehensive error handling extends this to 2–4 weeks. Managed ETL vendors like Improvado offer custom connector builds with 2–4 week SLAs, including ongoing maintenance when the platform's API changes. In-house development without a framework typically requires 6–12 weeks for a production-ready connector plus ongoing maintenance overhead.

What happens when Google Ads or Facebook deprecates a metric I'm using?

Platform API changes fall into three categories: additive (new columns appear), renaming (existing columns get new names), and breaking (columns disappear entirely). The best ETL tools detect these changes automatically and backfill historical data using archived schemas so your dashboards maintain trend continuity. Without this capability, you face a choice: accept broken dashboards until you manually rewrite queries, or lose historical comparisons by switching to the new metric with no backfill. Improvado preserves 2 years of schema snapshots specifically to handle this scenario — when Google Ads retired "average position" in 2019, customers retained historical data for year-over-year analysis.

How can I control Snowflake compute costs when running marketing ETL pipelines?

Snowflake charges for compute (queries, transformations) and storage separately. ETL workloads drive compute costs through: full table scans during joins, inefficient SQL generated by transformation tools, and overlapping pipelines that prevent warehouse auto-suspend. Reduce costs by: using incremental merge logic instead of full reloads (90% reduction in rows scanned), scheduling heavy transformation jobs during off-peak hours when Snowflake credits are cheaper, partitioning large fact tables by date so queries only scan relevant months, and choosing ETL tools that generate optimized SQL with proper join ordering and predicate pushdown. Monitor your Snowflake query history to identify expensive patterns — a single poorly written transformation can cost thousands monthly.

What governance rules should I implement for marketing data pipelines?

Marketing data governance spans five areas: budget validation (alert when daily spend exceeds approved limits), taxonomy enforcement (UTM parameters follow naming conventions, campaign IDs match approved values), PII protection (mask or encrypt customer emails and phone numbers), schema validation (required fields are never null, numeric columns don't contain text), and attribution consistency (conversion events aren't double-counted across platforms). Implement these as pre-load checks that quarantine invalid rows rather than blocking entire pipelines — this prevents downstream dashboards from breaking while alerting your team to data quality issues requiring investigation.

Can I use my existing BI tool (Tableau, Looker, Power BI) with Snowflake ETL data?

Yes — all major BI tools connect natively to Snowflake via ODBC, JDBC, or platform-specific drivers. Your ETL pipeline writes data to Snowflake tables following your chosen schema (star, snowflake, or denormalized), then your BI tool queries those tables like any other database. However, BI tool performance depends on your Snowflake schema design: wide denormalized tables are fast for simple queries but expensive for complex aggregations; star schemas require more joins but enable flexible slicing by dimension. Some ETL platforms (including Improvado) offer pre-built BI templates and semantic layers that map marketing dimensions (channel, campaign, creative) to BI tool concepts (filters, hierarchies, drill-paths), reducing the setup time from weeks to hours.

How long should I retain raw marketing data in Snowflake?

Retention requirements depend on attribution window lengths and regulatory compliance. For performance marketing, retain at least 90 days of raw event data to support multi-touch attribution models that track customer journeys across weeks. For brand campaigns and annual planning, 2–3 years of historical data enables year-over-year comparisons and seasonal trend analysis. Snowflake's storage costs ($23–$40 per TB per month depending on region) make long-term retention affordable — archiving 5 years of marketing data typically costs less than $500 monthly. Implement tiered storage: keep recent data (0–90 days) in hot tables for fast queries, archive older data (90+ days) in separate schemas that use lower-cost Snowflake storage tiers, and document retention policies so your team knows when data will be purged.