Best data warehouses in 2025

Contributors

Written by Ishan Shekhar
Data Engineer at Funnel. Passionate about data, and enabling companies to become more data-driven.

Research conducted by IBM and Gartner reveals that poor-quality data can lead to businesses losing an estimated 20 to 35% of their annual revenue.

Fragmented across platforms offline and online, most data comes in varying formats with inconsistent naming and metrics. For data engineers working with marketing data, getting standardized data ready for analysis into a single source of truth is an ongoing battle. There are also rate limits, transient errors and retroactive updates that derail efficiency to contend with.

The wrong data warehouse makes it worse, creating even more bottlenecks and incomplete insights. But the right one can streamline the process of managing and analyzing large amounts of data.

So, what are the best data warehouses in 2025? Here are five of the top solutions for data engineers working with marketing data:

Amazon Redshift: Delivers fast, cost-efficient analytics for large-scale data processing.
Google BigQuery: Provides rapid querying and insights with easy access to advanced analytics.
Microsoft Azure Synapse: Unifies data for seamless analysis and decision-making in one platform.
Snowflake: Offers flexibility to work with data across platforms and enables secure collaboration.
Oracle Autonomous: Handles performance tuning and maintenance automatically for efficient data management.

Before deciding what data warehouse is best for your needs, let’s look at data storage solutions in depth and how to simplify the process of sending structured marketing data to its destination with an automation tool.

What are data warehouses, and how can they help with marketing data?

A data warehouse is a home for marketing and other types of business data, such as sales, finance and product data. Data warehouses are designed to be scalable and handle huge amounts of information, so they’re useful when you need a reliable, cost-efficient storage solution for business intelligence (BI).

Marketing teams often send data directly to visualization tools. However, data warehouses can be more beneficial for companies that have a data engineering team that wants to analyze marketing data along with other types of business intelligence. A warehouse can become your marketing data’s main BI destination. However, marketing data might be coming from hundreds of sources and have different formats, so you’ll want to make that data ready for analysis before sending it along to a data warehouse.

That’s where a marketing data hub that integrates and prepares data for a warehouse can help. You can build a pipeline that automates the flow and transformation of marketing data, freeing up more time for higher-level tasks.

Instead of spending hours wrangling data, a marketing intelligence solution that simplifies your job by organizing and normalizing your data lets you send it to your warehouse and dive straight into analysis. This allows you to quickly spot trends, identify opportunities and enable your marketing team to make faster, more confident decisions.

What’s the difference between a data lake and a data warehouse?

Deloitte found that 59% of C-suite executives see data and analytics as key to having a competitive edge, yet only 9% of organizations fully understand their performance drivers. Being able to efficiently store, organize and understand your data will help ensure all departments, including marketing, focus on growth-driving initiatives.

Here’s how data warehouses and data lakes fit into the equation:

Data format

Data warehouses: Store cleaned, structured and semi-structured data ready for analysis.
Data lakes: Store raw, unprocessed data in native format, perfect for diverse formats like clickstreams and social media logs.

Purpose

Data warehouses: Ideal for analytics, reporting, tracking performance and calculating ROI.
Data lakes: Designed for storage and exploration, supporting projects like audience segmentation and predictive analytics.

Data preparation

Data warehouses: Require transformation for consistency and accuracy before loading.
Data lakes: Ingest raw data quickly, but require extra effort for later analysis.

As a data analyst working with marketing data, a data warehouse is a great solution if you have a tool to prepare the data before sending it to its destination.

Types of data storage solutions

When it comes to data warehouses, there are a few different types designed to meet the specific needs of data analysts:

Enterprise data warehouse (EDW): This is the most common type. It centralizes all data from across the organization, integrating information from various departments like marketing, sales and finance. EDWs allow for detailed reporting and business analysis, supporting decision-making at all levels.

Operational data warehouse (ODW): Focused on handling real-time, transactional data, an ODW is perfect for operational reporting. For marketers, this type is great for tracking ongoing campaigns and monitoring real-time performance.

Data mart: A smaller, more specialized version of a data warehouse, a data mart focuses on a single business area. It’s useful for targeting specific data analysis needs without the complexity of a full-scale EDW.

Cloud data warehouse: Hosted on the cloud, this type offers scalability and flexibility without the need for maintaining physical infrastructure. Cloud data warehouses are increasingly popular for marketing teams as they allow for easy integration with external platforms like Google Ads or Facebook. Most data storage solutions today are cloud-based, as traditional on-premises solutions are very expensive and require a lot of resources to manage.

Each type serves a different purpose, but all make it easier for data analysts to access and analyze data efficiently.

For example, if your main objective is to answer “How many website visits did we get last week?” A tool like Google Analytics can quickly provide this data with just a few clicks.

But if you want to dive deeper into “What factors led to a 25% increase in website traffic last week?” you can’t get this answer from Google Analytics alone. You’d need to pull data from additional sources like social media ad campaigns on Facebook or LinkedIn, email open rates or influencer collaborations to see what triggered the spike in traffic.

Similarly, if you're asking, “How much revenue did we generate from ads in February?” a tool like Google Ads or Facebook Ads Manager can give you a straightforward answer.

But if you ask, “Why did our ROI from ads double in February compared to January?” it’s not just about ad performance. You'll need data from your CRM, customer behavior data and even seasonal trends, all of which give context to the numbers and help you pinpoint what made the difference.

So what are the best data warehouse solutions in 2025?

Unique challenges of data warehousing for marketers

Marketing data is unique because it comes from so many places — ad platforms, social media, CRMs and web analytics. If you're a data analyst working with marketing, you know the pain of dealing with fragmented data. It comes from different sources, and each one has its own format and naming system. This makes it tough to bring everything together and get a clear, unified view of your performance without a reliable normalization and aggregation process.

Then, there’s the problem of retroactive updates. Marketing data changes all the time, whether it’s new campaign results, updated metrics or even last-minute tweaks to ad performance. It’s hard to keep everything consistent and accurate when things are constantly shifting.

Real-time analysis is another hurdle. Different rate limits and constant API errors from every platform just add to the chaos, making it even harder to get reliable data when you need it most because you’re too busy patching issues.

On top of all that, many marketers don’t have the technical expertise to handle complex data pipelines or work directly with data warehouses. So, you’re stuck trying to juggle these technical challenges on top of everything else.

To make it easier, you need tools that can optimize workflows, standardize metrics and integrate everything in one place. Then, you can focus on the insights and stop getting bogged down by the process.

Evaluating data warehouse solutions for marketing teams

Data engineers managing large-scale marketing workflows must prioritize solutions that support performance, scalability and reliability. Key criteria include:

Scalability and performance

Ability to handle large data volumes and high-concurrency workloads
Support for distributed computing and massively parallel processing (MPP)
Elasticity to scale resources up or down based on business needs

Data integration and management

Seamless integration with marketing platforms and existing infrastructures
Robust support for structured and unstructured data
Automation of repetitive workflows, including retries for transient errors

Data security and governance

Compliance with data protection regulations
Tools for auditing, user access control and role-based permissions

Cost-efficiency and pricing structure

Transparent pricing models that align with data volume and usage patterns
Cost-optimized features like pre-aggregation and partitioning to reduce storage costs

User experience and usability

Intuitive interfaces for pipeline setup and monitoring
Support for customizable dashboards and reports

Key features to factor in during evaluation

When assessing a data warehouse, data engineers should focus on features that simplify pipeline management and support seamless data flows:

BI and analytics integration

Native support for data visualization and business intelligence tools
Capabilities to generate real-time, actionable insights from large datasets

Scalability and elasticity

Ability to accommodate growing organizational needs without performance degradation
Support for dynamic workloads and peak processing demands

Choosing a solution that integrates and prepares marketing data for warehouses

To overcome the marketing data challenge, a data automation tool that can streamline data flows, handle large amounts of data efficiently and simplify pipeline management is key. A marketing data hub with ETL (extract, transform, load) capabilities can clean and move data to your warehouse for you. Your tool should offer the following:

Streamlined data integration for marketing sources

Look for a solution that goes beyond basic ETL functions by automating extraction, normalization and delivery of structured marketing data into any data warehouse.

It should standardize fields like currencies, dates and metrics, optimize storage and processing with pre-aggregation and table partitioning and ensure efficient updates without redundant edits.

After all, your goal should be to eliminate manual data wrangling, freeing up time for strategic initiatives.

Optimized storage and processing for marketing analytics

Look for tools that save you time right down to the most granular level:

Groups data in advance and organizes it for faster queries
Avoids storing duplicate data, making updates quicker and saving storage space
Provides clean, organized data that's ready to use for faster insights

Comprehensive connectivity for diverse marketing platforms

You want a solution with hundreds of connectors and custom integrations. Funnel scales effortlessly to meet any organization’s needs, saving time, reducing costs and ensuring reliable data workflows. It automates the extraction, normalization and delivery of marketing data into any data warehouse and consolidates data from ad platforms, CRMs and analytics tools into a unified view.

Cost and time efficiency for marketing data teams

The big win here — efficiency. Automating repetitive reporting tasks reduces costs and resource needs. You can also count on reliable, real-time data pipelines for accuracy in reporting and decision-making, which means less time sifting through data sources and checking for errors and more time actioning results.

Ultimately, a solution that prepares data so it’s analysis-ready when it reaches its destination

enables marketers and data teams to focus on optimization and strategy, rather than data wrangling and logistics.

A central hub for actionable marketing intelligence

The best data warehouses offer the reliability, flexibility and scalability you need for streamlined, fruitful data reporting and analysis. But you also require a solution to handle your marketing data before it goes to the warehouse to avoid creating some of the same headaches you’re trying to solve.

Funnel automates marketing data flows and scales with you. It gathers, transforms and then moves marketing data to its destination and gives marketers the power to make faster, smarter decisions. It also integrates with all the leading data warehouse solutions, so you can build reliable pipelines and create the breathing room you need to focus on higher-level tasks.

Ready to streamline your marketing data pipeline? Discover how Funnel can help — contact us today!