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Types Of Data Warehouses & 7 Best Tools For Modern Data Stack

In today’s modern ecosystem, enterprises use distinct types of data warehouse tools to handle large data volumes and get quick insights. Modern data tools can store, centralize, and query large data volumes to aid the decision-making process across various departments. The best data warehousing tool helps large enterprises reserve pools of information in their database beyond terabytes.

The lack of tools and the massive size of information stored in the organization often overshadows the value data can provide. A data warehousing solution is a sophisticated and structured approach to storing data and driving value from it. This guide will reveal the various types of data warehouses and the best warehousing tools a company can use to store data effectively.

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Types Of Data Warehouses to Escalate Business

Data warehousing solutions are the ultimate solution to access critical data from disparate sources to make quick business decisions. It is a central repository where data is stored, processed, transformed, and ingested to help teams make strategic decisions. Leveraging the right data warehousing tool and solution can help businesses scale and unlock the true potential of enterprise data, which is why the data warehousing market will exceed $30 billion by 2025. Here are the three main types of data warehouses for different business sizes.

  • Enterprise Data Warehouse: An enterprise solution is a centralized place where all business information and data are available. Data stored consistently and logically makes it easier for teams to utilize data and scale business. 
  • Operational Data Store – Also known as ODS, it allows refreshing the information stored in the data warehouses in real time. The best use of ODS is to reserve employee records and make them easy to manage for large enterprises. 
  • Data Mart – The third type of data warehouse is data mart which collects data subsets. The primary goal of storing the information in the data mart is to enhance user response and reduce data volumes for analysis.


Top 7 Data Warehouse Tools to Implement in 2023

With different types of data warehouse tools available on the market, it can be challenging for business owners to invest their money in the right modern data stack tool. Learn about the top 7 highly efficient and scalable data warehouse platforms.

  • Snowflake

Snowflake is an enterprise-grade data warehouse solution that helps businesses analyze data from structured and unstructured sources. Snowflake’s high scalability and multi-tenant design accelerate query performance, deliver actionable insights, and share data across their organization.

  • Google BigQuery

Organizations that intend to choose a cost-effective data warehousing solution should choose Google BigQuery. The built-in Machine Learning capabilities help organizations execute queries and get real-time analysis for petabytes of data. This cloud-native solution supports geospatial analytics to help teams analyze location-based data and improve business operations.

  • Amazon Redshift

Amazon Redshift is specially designed for enterprises that store exabytes of data and plays a primary role in establishing the data warehousing industry. Startups and large enterprises prefer AWS Redshift as it prioritizes speed and scalability and has an easy-to-use structure. Furthermore, the data warehousing solution separates computing and storage to help enterprises scale as per their business needs.

  • Microsoft Synapse Analytics

Microsoft Synapse Analytics is an efficient data warehouse tool hosted in the Azure ecosystem. Organizations operating in the Microsoft Azure ecosystem can easily integrate the tool with Microsoft SQL Server. In addition, it is equipped with advanced security features like data masking and row-level security to keep data secure.

  • Teradata

Teradata is a data warehousing solution that collects and analyzes data in the cloud. The super fast parallel querying infrastructure helps data experts to get real-time actionable insights. Organizations can optimize their database performance to employ smart in-memory processing without extra expenses.

  • PostgreSQL

An open-source database management solution suitable for SMEs and large enterprises is PostgreSQL. Like Google BigQuery, it can help data teams with location-based business solutions. However, they should integrate PostgreSQL with the PostGIS extension to drive value from geospatial data.


SAP HANA is a cloud-based solution that supports enterprise-wide data analytics and high-speed real-time data processing. SAP HANA’s simple and centralized interface enables teams to access, integrate, and virtualize data. The data federation feature helps data experts query remote databases without moving data from one location to another.

Inferenz has experienced analysts and data migration experts who assist startups, SMEs, and large enterprises choose and integrate the right data warehousing solution. Read the case studies of Inferenz to learn how our experts help enterprises switch from on-premise solutions to cloud computing tools.


ALSO READ: 5 Best Practices For Snowflake Implementation in 2022

Get Best Data Warehouse Services With Inferenz

Organizations that intend to scale their business should focus on improving data quality and making it available for in-house data experts. However, storing data in disparate resources can make data mining challenging for teams.

The best solution to save time and resources while using the stored data to get valuable insights is switching to the right data warehousing solution. If you want to choose the right type of data warehouse tool or a cloud-based solution for your business, feel free to contact the experts of Inferenz.