What is a data warehouse and why do you need one?

Understanding Data Warehouses

In today’s data-driven world, businesses generate and collect vast amounts of data from a variety of sources, including transactional systems, operational databases, and customer interactions. This data can be a valuable asset, but it can also be overwhelming and difficult to manage. This is where a data warehouse comes into play. 

In this blog, we will explore the concept of data warehousing, benefits, and the evolving landscape of this essential technology.

What is a Data Warehouse?

A data warehouse is a data management system that helps businesses store, manage, and analyze their data in a centralized and structured way. Data warehouses provide a single source of truth for businesses, enabling them to make informed decisions across all departments.

Data warehouses are different from regular databases, which are mainly used for tracking daily transactions. Data warehouses focus on looking at past data to find patterns and trends. This helps businesses understand their customers better, make better products and services, and be more successful.

In 2023, the global data warehousing market was worth over $31.85 billion, showing how important it is in the data-driven world. As companies continue to make decisions based on information, data warehouses will become even more important for companies to succeed.

Benefits of a Data Warehouse

Data warehouses offer a multitude of benefits that can transform your business:

  • Improved Decision-Making

    • Data warehouses provide a single source of truth, enabling businesses to make informed decisions across all departments based on a comprehensive understanding of their operations.

  • Increased Efficiency

    • Data warehouses automate data extraction, cleansing, and transformation, freeing up valuable time for employees to focus on strategic initiatives.

  • Reduced Costs

    • By eliminating data silos and ensuring data integrity, data warehouses can help businesses reduce costs associated with data management and maintenance.

  • Improved Data Quality

    • Data warehouses centralize and standardize data, enabling organizations to maintain high data quality and ensure the reliability of analytical results.

The Evolving Landscape of Data Warehouses

In the ever-evolving data landscape, data warehousing technology has undergone a remarkable transformation. Cloud data warehouses have emerged as a cost-effective and scalable alternative to traditional on-premises solutions, driving a shift towards cloud-based data warehousing. This trend is gaining momentum as IT managers prioritize faster analytics processing, with 47% having already adopted cloud-based solutions and 52% emphasizing the need for improved speed.

The evolution of data warehousing is also being fueled by emerging trends such as data mining, real-time analytics, and AI-powered data insights. By 2025, data mining is projected to capture over 25% of the data warehousing market share, reflecting the growing demand for deeper data insights.

These advancements in data warehousing technology are empowering businesses to make informed decisions, improve operational efficiency, and gain a competitive edge. As cloud adoption accelerates and emerging trends continue to reshape the data landscape, data warehousing is poised to play an even more critical role in the success of modern businesses.

Data Warehouse Vendors

We’ve discussed the benefits of data warehouses and how they can help businesses make better decisions. Now, let’s take a look at some of the top data warehouse vendors in the market today. These vendors offer a variety of features and capabilities to meet the needs of businesses of all sizes.

Here is a list of the top 5 data warehouse vendors along with a few lines of description for each:

1. Snowflake

Snowflake is a cloud-based data warehouse that is known for its scalability and performance. It is a popular choice for businesses of all sizes, including enterprises. Snowflake is easy to use and can be deployed in a matter of minutes. It also offers a variety of features, such as real-time analytics and data sharing.

2. Databricks

Databricks was founded by the creators of Apache Spark. Databricks is more of a data lake than a data warehouse, we’ll talk more about the differences below. Databricks processes data with Spark, which allows it to process large amounts of data in parallel.

3. Amazon Redshift

Amazon Redshift is a cloud-based data warehouse that is part of the Amazon Web Services (AWS) platform. It is a powerful and scalable data warehouse that is popular for businesses that are already using other AWS services. Redshift is easy to use and offers a variety of features, such as real-time analytics and data sharing.

4. Google BigQuery

Google BigQuery is a cloud-based data warehouse that is part of the Google Cloud Platform (GCP) platform. It is a powerful and scalable data warehouse that is popular for businesses that are already using other GCP services. BigQuery is easy to use and offers a variety of features, such as real-time analytics and data sharing.

5. Microsoft Azure Synapse Analytics

Azure Synapse Analytics is a cloud-based data warehouse that is part of the Microsoft Azure platform. It is a powerful and scalable data warehouse that is popular for businesses that are already using other Azure services. Synapse Analytics is easy to use and offers a variety of features, such as real-time analytics and data sharing.

Pro tip:

Peliqan.io is a user-friendly data platform that empowers business teams of all sizes, from startups to enterprises, to seamlessly connect and combine data from various sources, without the need for a data engineer. Peliqan.io connects to your existing data warehouse (e.g. Snowflake or Azure) and provides a built-in data warehouse for companies that desire an all-in-one solution.

Peliqan’s intuitive spreadsheet-like interface makes it easy for business users to clean, edit, and transform data, while power users can leverage SQL and developers can utilize low-code tools to build interactive data apps, implement writebacks, and apply machine learning capabilities.

Difference between Data Warehouse, Data Lake & Data Lakehouse

Data warehouse

A data warehouse is a repository for structured data that has been cleaned and transformed into a format that is optimized for analysis. Data warehouses typically store data from a variety of sources, such as transactional systems, customer relationship management (CRM) systems, and other enterprise applications. The data is then organized into a relational database, which makes it easy to query and analyze.

Data lake

A data lake is a repository for raw, unprocessed data. Data lakes can store any type of data, including structured, semi-structured, and unstructured data. This makes them a good choice for storing large amounts of data from a variety of sources, such as social media, IoT devices, and sensors. Data lakes typically store data in files in a flat file format, which makes them easy to scale and manage. A data lake can be implemented for example on Amazon S3 cloud storage.

Data lakehouse

A data lakehouse is a hybrid architecture that combines the best of both data warehouses and data lakes. Data lakehouses can store structured, semi-structured, and unstructured data, and they can also provide the ability to query and analyze data in real time. This makes them a good choice for organizations that need to store and analyze large amounts of data from a variety of sources.

Feature Data Warehouse Data Lake Data Lakehouse
Purpose Store and analyze structured data for business intelligence and reporting Store raw, unstructured data for machine learning and predictive analytics Store and manage both structured and unstructured data for a variety of use cases
Strengths High data quality
Efficient query performance
Strong data management
Can store any type of data
Low-cost storage
Ideal for complex data processing
Can store any type of data
High data quality 
Efficient query performance Strong data management
Weaknesses Does not handle raw or unstructured data
Expensive to maintain
Not ideal for complex data processing
Poor data quality
Less efficient query performance
Weak data management
Emerging technology
More expensive than data lakes

Data warehouses, data lakes, and data lakehouses are all important tools for data analytics. The best data storage architecture for you will depend on your specific needs.

If you need to store and analyze structured data, then a data warehouse is a good choice. If you need to store and analyze raw, unprocessed data, then a data lake is a good choice. And if you need to store and analyze both structured and unstructured data, then a data lakehouse is a good choice.

Data Warehouses Use Cases

Data warehouses empower organizations to address diverse business challenges through data-driven insights:

  • Customer Analysis

    • Understand customer behavior, preferences, and demographics to enhance marketing campaigns, develop personalized experiences, and improve customer retention.

  • Product Analysis

    • Analyze product sales, performance, and usage data to optimize product development, marketing strategies, and customer satisfaction.

  • Operational Efficiency

    • Identify and address inefficiencies in business operations, leading to cost savings, improved productivity, and enhanced customer experience.

  • Risk Management

    • Analyze data to identify and assess potential risks, enabling organizations to proactively implement mitigation strategies.

Data warehouses are at minimum the source of data for BI tools. Analytics (building dashboards, reports etc.) is typically done in a BI tool such as Microsoft Power BI, Qlik, Tableau or Metabase. These tools can connect directly to a source, but by using a data warehouse, a future proof data strategy is deployed that allows organizations to start doing more with their data. This can include data activation, reverse ETL (writing data back into business applications), applying machine learning models for predictions, building data products, setting up data APIs for external partners etc.

How can a data warehouse help your business to gain a competitive edge?

Think of a data warehouse as a central information hub, a digital vault storing all your company’s data – from customer behavior to operational metrics to marketing campaign results. A data warehouse allows every department in an organization to become data-driven.

Peliqan’s data warehouse capabilities let you seamlessly integrate data from diverse sources, cleaning, transforming, and organizing it into a unified, accessible format. This unified view is the key to unlocking insights that were previously hidden in siloed systems.

So, how exactly does the Peliqan data warehouse help you gain a competitive edge? Peliqan provides all the components needed to implement data activation:

  • Connectors: Peliqan provides connectors to a wide range of data sources including databases, business applications (CRM, accounting, ERP etc.), APIs, cloud storage, files, SFTP etc.
  • Peliqan makes it easy to explore, transform and combine your data in a spreadsheet interface. Power users can use magical SQL.
  • Peliqan provides a low-code visual UI to implement data activation flows.

Peliqan is used for a wide range of use cases, helping companies to explore the possibilities of AI, setting up data syncs between business applications and exchanging data with partners.

Conclusion: Data Warehouses – A Catalyst for Growth

In today’s data-driven world, organizations that fail to harness the power of their information are at a significant disadvantage. Data warehouses, with their ability to centralize, organize, and analyze vast amounts of data, have emerged as a critical tool for businesses seeking to gain a competitive edge.

In conclusion, data warehouses are not just tools; they are catalysts for growth. By investing in a data warehouse solution, organizations can unlock the power of their data, transform their decision-making processes, and achieve sustainable growth.