Data Warehouses and Data Lakes FAQ

Welcome to Climber’s FAQ on Data Warehouses and Data Lakes. This page answers common questions about modern data platforms and how organisations use them to store, manage, and analyse data effectively.

We explain the differences between data warehouses and data lakes, outline their benefits, and explore how they work together to support reporting, analytics, AI, and machine learning.

Whether you are building a new platform or modernising an existing environment, understanding how data is structured and governed is essential to delivering trusted insights and better decision-making.

1. What is a Data Warehouse?

A data warehouse is a centralised data platform designed to support business intelligence, reporting, and analytics. It brings together structured data from systems such as ERP, CRM, and HR platforms into a single, consistent environment.

Data is cleaned, organised, and structured before it is used. This makes it suitable for complex queries and historical analysis. By consolidating information from across the organisation, a data warehouse provides a reliable foundation for trend analysis, performance reporting, and informed decision-making. Modern cloud data warehouses also support advanced analytics, machine learning, and AI use cases, helping organisations scale insight as data volumes grow.

2. What is a Data Lake?

A data lake is a centralised storage environment designed to hold large volumes of data in its raw or native format. Unlike a data warehouse, it can store structured, semi-structured, and unstructured data without requiring transformation before it is stored.

Data lakes use a “schema-on-read” approach, meaning structure is applied only when the data is accessed for analysis. This flexibility makes them well suited to exploratory analytics, data science, machine learning, and AI use cases where teams need access to diverse and evolving datasets.

3. What is the difference between a Data Warehouse and a Data Lake?

The key difference lies in how data is structured and processed. A data warehouse uses a schema-on-write approach, meaning data is cleaned, transformed, and structured before it is loaded. A data lake uses a schema-on-read approach, storing data in its raw format and applying structure only when it is analysed.

Data warehouses are typically used for business intelligence, reporting, and performance analysis where consistency and trusted metrics are essential. Data lakes are more flexible. They are often used for exploratory analytics, data science, machine learning, and AI, where teams need access to diverse and evolving datasets.

4. What are the benefits of a data warehouse?

A data warehouse improves data quality and consistency by consolidating information from multiple systems into an environment that is structured and governed. Data is cleaned and transformed before use, enabling organisations to run complex queries and generate reliable reports with confidence.

It also preserves historical data, making it easier to analyse trends, measure performance over time, and support informed decision-making. Modern cloud data warehouses provide scalable performance for reporting, advanced analytics, and AI use cases, helping businesses turn data into practical insight.

5. Can a data lake replace a data warehouse?

Although a data lake can support some analytical workloads, it does not replace the structured role of a data warehouse. Data lakes are designed to store raw and diverse datasets for exploration, data science, machine learning, and AI. Data warehouses provide structured and validated data for reliable reporting and business intelligence.

Many organisations now adopt a hybrid or “Lakehouse” approach, combining the flexibility of a data lake with the governance and performance of a data warehouse. This allows raw data to be retained for exploration, while curated, analysis-ready data supports trusted reporting and decision-making.

6. What are the components of a data warehouse?

A typical data warehouse consists of several core components. At its centre is a database that stores structured data optimised for reporting and analytics. Data integration processes such as ETL (Extract, Transform, Load) or ELT (Extract, Load, Transform) move and transform data from source systems into the warehouse and prepare it for analysis.

Metadata provides context about data origin, structure, and lineage, supporting governance and data discovery. Access tools, including business intelligence and reporting platforms, allow users to explore data, build dashboards, and generate insight.

7. How do you build a data warehouse?

Building a data warehouse begins with defining clear business requirements and identifying the key metrics or KPIs the organisation needs to track. From there, a data model and architecture are designed to support reporting, analytics, and long-term scalability.

Data is then sourced from operational systems such as ERP and CRM platforms, transformed into a structured format, and loaded into the warehouse using ETL or ELT processes. Business intelligence tools connect to the warehouse to enable dashboards, reporting, and advanced analytics.

At Climber, our approach is centered on your business outcomes. We work with you to:

• Source: We identify and extract data from its original location, whether it’s in your ERP, CRM, or other systems.
• Transform: We create a structured, organised, and scalable dataset within the data warehouse.
• Connect: We link the structured data to your BI solution to enable powerful visualisations and reporting.

8. What are the main cloud platforms for data warehousing?

The main cloud providers offering data warehouse solutions are Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). Each provides a managed cloud data warehouse service, including Amazon Redshift, Azure Synapse Analytics, and Google BigQuery.

In addition, cloud-native platforms such as Snowflake are available on multiple cloud providers, including AWS, Microsoft Azure, and Google Cloud Platform. These platforms deliver fully managed, scalable data warehouse services that support modern analytics, machine learning, and AI workloads, with flexible, consumption-based pricing models.

9. What is ETL?

In modern cloud data architectures, ELT (Extract, Load, Transform) is increasingly common.

With ELT:

• Raw data is loaded first into a data lake or cloud data warehouse
• Transformations are performed within the platform itself
• Data can be stored in its original format before being modelled

ELT offers greater scalability for handling large data volumes. It provides more flexibility as business requirements evolve. By loading raw data first, organisations can ingest information faster and transform it within the platform, making it well suited to advanced analytics, machine learning, and AI workloads.

Designing a Data Platform That Works for Your Business

Whether you are building a new data warehouse or modernising an existing data platform, Climber works with organisations to design structured, scalable environments that support reporting, analytics, and AI use cases.

With experience across cloud infrastructure, data integration platforms, and analytics tools including Qlik, Microsoft Fabric, Snowflake, AWS, and Google Cloud Platform, we help turn complex data landscapes into reliable, governed foundations for better decision-making.

Contact us to discuss how your data platform can deliver long-term value!