A marketing data lake is essential in 2025 because it lets teams store, access, and analyze large volumes of marketing data from one place. It handles raw files, structured reports, and unstructured content like social media feeds or logs.

With more platforms, more customer data, and the growing need for real-time analytics, traditional analysis tools can’t keep up. A data lake supports better decisions by combining flexibility, scale, and speed.

In this article, you’ll learn what a marketing data lake is, how it compares to a data warehouse, what benefits it offers, and how tools like TapClicks help make it practical for marketers.

Connect, store, and report on all your marketing data in one place. Try TapClicks now!

What Is the Difference Between Lakes and Data Warehouses?

Marketers often compare data lakes vs. data warehouses, but each serves a different purpose. A data lake, a centralized repository, stores raw information in its native format, while a data warehouse keeps structured data ready for fast reporting.

To choose the right system, you need to understand how they differ in data type, performance, and cost.

Data Type

A data lake accepts all types of data: structured, semi-structured, and unstructured.

It includes everything from clean, labeled spreadsheets and tables to raw server logs, emails, video transcripts, and social media feeds. Because it stores information in its raw form, there’s no need to convert or format it before storage.

A data warehouse stores structured data only. It uses a fixed schema, meaning all information must be cleaned, labeled, and organized before it can be stored.

This approach improves speed and consistency when running reports but doesn’t support unstructured data well. Teams often lose context or skip valuable insights when they can’t include data that doesn’t fit into predefined rows and columns.

When comparing lakes and data warehouses, the biggest difference lies in how flexible they are with data formats. A lake keeps everything as it is, while a warehouse reshapes the data before saving it.

Data Volume

A data lake handles big data processing without much effort. Since it stores data in its original state, it doesn’t need extra processing before saving.

This makes it easier to collect marketing data from many tools and keep it all in one place, even if the files are huge or come in different formats. Cloud-based lakes also scale automatically, so storage capacity keeps growing as your data grows.

A data warehouse is also built to manage large datasets but does so with more structure. The system is optimized for repeated queries and fast analysis.

However, as data grows, warehouses may need more resources to maintain performance. That can lead to higher operational costs, especially when you’re storing information you don’t use often.

If your team works with growing campaign data, creative assets, logs, and customer touchpoints across many platforms, a data lake offers more flexibility. It keeps all your information in one place, no matter the size or format.

Purpose

A marketing data lake stores raw data, structured reports, and unstructured inputs like customer feedback or social media posts. Teams can collect data from many platforms and keep it in its native format.

It makes it easier to experiment, test advanced analytics, or feed machine learning models. Since the lake doesn’t require a fixed schema, marketers can work with different formats and explore new insights as needed.

A data warehouse supports predefined queries and repeatable analysis, like campaign performance reports or sales dashboards. Because the data is cleaned and organized before it’s stored, the system responds faster to known queries.

While both systems support marketing teams, a data lake provides more room to test, analyze, and adapt. Warehouses excel when the goal is speed and consistency. Many teams now use both to balance exploration and reporting.

Data Processing Time

Data lakes delay processing until it’s time to read or analyze. This approach, known as schema-on-read, gives marketers more flexibility. Teams can gather raw data from multiple sources and store it without transforming it right away.

The information stays in its original format until someone queries it or pulls it into a reporting tool. That setup is useful when dealing with diverse data, especially if the format or use case isn’t clear at the start.

Data warehouses use schema-on-write. Before storing anything, the system runs the data through an ETL process that filters, organizes, and reshapes it. Once loaded, the data is ready for fast and repeatable queries.

For projects that involve constant exploration, shifting campaign inputs, or evolving business questions, a marketing data lake makes it easier to store and access unprocessed data without slowing down the collection process.

Advantages of Marketing Data Lakes

Marketing teams deal with information from dozens of platforms. A marketing data lake helps organize it all by storing raw, structured, and unstructured inputs in one place. Beyond storage, it supports deeper analysis, quicker insights, and better decision-making across channels.

Centralized Data Storage

A marketing data lake creates one place for storing large volumes of data from different tools. Instead of scattering files across platforms, you can keep everything together.

Key benefits of centralized storage in a marketing data lake:

• All your data in one place - Marketing campaign results, website logs, and customer data stored in one system.

• Supports structured and unstructured data - Unlike a data warehouse, the lake stores data from spreadsheets, ad platforms, or raw clickstream files.

Enhanced Data Exploration and Analysis

When everything is stored in one place, marketing teams can ask better questions and test new ideas without waiting for IT support. Data lakes support tools that work with raw inputs and help teams find patterns that weren’t clear in isolated dashboards.

How data lakes improve analysis and exploration:

• Custom queries - Marketers can explore performance across different segments without rigid templates.

• Better use of machine learning models - Lakes store the diverse data types needed to build and test predictive tools.

• Supports advanced analytics - From real-time analytics to historical trend tracking, teams gain deeper insights faster.

Speed and Real-time Analysis

Fast access to data is a major benefit of using a marketing data lake. With the right tools, you can run near-instant queries and respond to changes in customer behavior or campaign performance.

Marketing data lakes offer real-time analytics, allowing teams to track how ads or content perform as it happens, not hours or days later. You don’t have to wait for processing as data is available immediately after collection.

Data-Driven Decision Making

A marketing data lake improves visibility by pulling all marketing data into one place—structured, unstructured, and raw. Teams can evaluate the whole picture instead of working with partial snapshots.

How data lakes support better decisions:

• Complete view of customer interactions - From email clicks to web visits, all touchpoints are visible in one system.

• Custom reporting options - Teams can create dashboards tailored to their needs without relying on fixed warehouse schemas.

• Backed by real results - With access to full datasets, marketers can base their choices on real outcomes instead of assumptions.

Streamlined Data Integration

Marketing teams use a wide range of tools, like analytics platforms, ad networks, CRMs, and social channels. A marketing data lake simplifies integration by pulling data from all sources, regardless of format or structure.

Key advantages of simplified data integration:

• Supports large volumes from many sources - Marketers don’t need to clean or organize files before loading.

• Reduces the risk of data silos - Everyone works from the same source of truth.

What Are the Challenges of Data Lakes?

Enterprise marketing teams often rely on data lakes and data warehouses to store large volumes of marketing data. Both centralize information, but they differ in flexibility.

A data lake stores raw, unstructured data from any source. A data warehouse, by contrast, requires data to be cleaned and structured before storage.

While both systems are powerful, marketers face two major drawbacks:

• Accessing the data often requires coding skills

• Analysis and reporting must be done in separate tools

Not Marketer-Friendly

When you store your marketing data in a data lake or a data warehouse, you’ll be reliant on IT expertise to access that data.

Typically, a data team with SQL and data science skills will do the work for you, extracting the data sets you need and doing the advanced analytics on that data, usually with a sophisticated business intelligence tool. 

This means that if you receive an ad hoc request from a client for a particular data set or report, you cannot easily collect and aggregate that data yourself. You have to fit it in with the workload and timeframe of the data scientists. 

You Need to Use Separate Data Analytics and Reporting Tools

The second challenge of using data lakes and data warehouses is that they help with only one part of the unified data management process. To report and analyze data, you need to add separate solutions to the data architecture. 

For example, data is often imported into a data visualization tool (e.g., Tableau) or even a spreadsheet, where it can be plotted into a presentation to share with others. That makes the process unnecessarily complicated and time-consuming.

How TapClicks Meets the Common Limitations of Data Lakes

In our experience, we believe there are four essential factors that a good marketing data management process should have, one of which is storing data in a data lake. Specifically, an effective marketing data management solution should:

1. Easily collect data from any sales and marketing source, automatically.

2. Store all your data in one fully managed data lake, accessible to marketers with no coding experience.

3. Transform unstructured data and do advanced calculations on that data, so you can use it for meaningful analytics.

4. Distribute your data wherever you want, via a powerful reporting function or to another platform.

TapClicks makes cross-channel data analysis easy for marketers. Try now!

TapClicks Automatically Manages Data From Almost All Marketing Data Sources

TapClicks acts like a marketing data lake, connecting to more than 6,000 data sources, including CRMs like Salesforce, offline systems, and niche platforms like Genius Monkey and Tiger Pistol. You can pull in data as-is using our Smart Connector, with no need for separate ETL tools.

Hundreds of pre-built API integrations cover major ad networks such as Facebook Ads and X Ads. For many platforms, TapClicks also fetches up to 12 months of historical data, which updates daily or on your schedule.

Our team handles the API maintenance, so if something breaks, you don’t need to call your developers. We fix it for you.

TapClicks Acts as Your Marketing Data Lake

With TapClicks, you don’t need technical skills to work with your marketing data lakehouses. It stores all your data, structured, unstructured, or raw, just as it comes in. You can organize, transform, and create custom metrics right inside the platform.

The TapClicks system is fully managed, so there’s no need for data engineers or coding. Everything lives in one central hub, and any marketer can access it anytime. You can also push your data to other tools without leaving TapClicks.

Marketers Can Transform Data and Do Complex Calculations

A data warehouse usually requires data to be cleaned and formatted first, often with an ETL tool. In contrast, most data lakes let you keep the raw data as-is and decide how to work with it later.

TapClicks takes this flexibility even further. It gives marketers direct access to transformation and calculation tools. No need for SQL or a team of data analysts.

Transforming Structured and Unstructured Data

With TapClicks, you can:

• Store raw form data - TapClicks accepts raw data from every source, just like a standard data lake.

• Preserve data hierarchies - Multi-level ad data (e.g., campaign, ad group, and ad level from Facebook Ads) is stored exactly as it was collected.

• Standardize metric names - If different platforms use “imp”, “hit”, or “view”, TapClicks lets you label them under a single metric like “impression” for consistent reporting.

• Work across multiple servers and clients - You can build clean, cross-channel views from diverse inputs without reformatting each time.

Doing Advanced Calculations Without Manual Work

TapClicks also supports built-in calculations across channels and data types. For example, if you’re managing a campaign that spans PPC, SEO, and display ads, TapClicks can automatically group performance and sales metrics under one campaign name.

You can:

• Normalize campaign performance across platforms

• Calculate total sales, cost per sale, and average spend by channel

• Avoid manual spreadsheets or repetitive data exports

• Re-use custom metrics across reports with no rework

Traditional data lakes or data warehouses don’t offer this level of reporting without third-party tools. With TapClicks, the full process, such as data collection, transformation, and advanced reporting, happens in one platform, and no coding is required.

Distribute Data to Stakeholders via TapClicks Reports or to Third-Party Platforms

Many marketing data lake workflows require reporting from third-party tools like Tableau or Google Sheets. TapClicks gives you that option, but also includes built-in tools for creating and distributing reports at scale.

Create Visualizations via TapClicks Dashboards

With TapClicks, you can:

• Choose from ready-made dashboard templates

• Customize metrics (widgets) to match client needs

• Visualize data using graphs, charts, or tables

• Scale dashboards across hundreds of clients using filters

Each widget and dashboard is fully customizable. You can tailor views per client or campaign without rebuilding from scratch.

TapClicks Automatically Populates PowerPoint-Style Reports With Up-to-Date Data

TapClicks ReportStudio makes it easy to:

• Create PowerPoint-style reports with live marketing data

• Use white-label templates to match your brand

• Set user permissions for different audiences

• Schedule delivery (weekly, monthly, etc.)

Reports update with real-time analytics, so you never have to manually refresh charts or export files. No outside tools or coding needed.

Distribute Data to Any Other Platform

If you prefer other reporting tools, TapClicks still works with them:

• Push data to Google Sheets using built-in connectors

• Distribute data to Tableau and other BI platforms via open API

• Export data to your existing data lake or data warehouse on a schedule

You can send structured or unstructured data in whatever format your team needs.

Upgrade Your Data Lake Workflow for Marketing With TapClicks

Data lakes are becoming an essential initiative for marketers to store increasing volumes of data.

However, it’s significantly beneficial for marketers to use a solution that can handle the entire data management process, including data collection, data transformation, visualization, and reporting, as well as act as a data lake.

Using TapClicks, which can do everything for you automatically, without the need for data programming skills, provides a flexible solution that saves a significant amount of time.

Centralize structured and unstructured data from 6,000+ sources. Schedule a demo today!

FAQs About Marketing Data Lake

What is a marketing data lake?

A marketing data lake is a centralized system that stores structured and unstructured data from various sources such as ads, CRM, and social media analytics platforms. It supports data ingestion at scale, allowing marketers to utilize data in its raw form for advanced reporting, predictive modeling, and personalizing customer experiences. It enables cost efficiency, preserves data integrity, and avoids data swamps through strong data governance, access controls, and data quality standards.

What is the difference between a data lake and a CDP?

A data lake stores all types of data in a flexible format, with minimal structure, suitable for data analysis and transformation later. A CDP (Customer Data Platform) is built for marketers, combining customer data from different systems to create unified customer profiles. CDPs focus on activation and engagement, while data lakes focus on scalable storage and processing, with greater flexibility for integrating data and applying data security policies.

What is the difference between a data lake and a data mart?

A data lake holds raw, unprocessed information from multiple sources, including sensitive data, and supports diverse analytics use cases. A data mart is a subset of a data warehouse, designed for a specific team or purpose, like sales. Data marts are structured, query-optimized, and curated. Data lakes offer broader flexibility, while data marts deliver faster access to domain-specific, structured data.

What are the main principles of data lake architecture?

Core principles include scalable data ingestion, support for diverse data types, and the ability to store data in its native format. A strong data lake architecture also requires metadata management, lineage tracking, data security, and access controls to maintain order and trust. Other principles include schema-on-read, predictive maintenance potential, and alignment with data governance to prevent the formation of a data swamp.