Top ETL Tools

10 Best Cloud Data Warehouse Tools to follow in 2024

May 14, 2024
Cloud Data Warehouse Tools have emerged as indispensable assets for businesses seeking to unlock the full potential of their data. This summary encapsulates the essence of the article, which explores the top 10 cloud data warehouse tools of 2024, showcasing their capabilities and impact on modern organizations.

From scalability and flexibility to real-time analytics and robust security measures, these tools offer a comprehensive solution for organizations navigating the complexities of data management in today's digital landscape.

By delving into the significance of cloud data warehouse tools and highlighting key factors to consider when choosing the right tool, this article provides valuable insights for businesses looking to harness the power of data.

With a focus on real-time data processing and analytics capabilities, as well as the importance of security measures in cloud data warehouses, this summary underscores the critical role these tools play in driving business insights and facilitating informed decision-making.

Overview of Cloud Data Warehouses

Data warehousing tools are critical to your business as they help you manage data analytic processes to drive the workflow. If you are looking for a cloud data warehouse with automation tools, this article will give you a quick overview of some options available. It is important to note that the list is in no particular order or ranking.

Cloud data warehouses have revolutionized the way organizations store, manage, and analyze data. These platforms leverage cloud computing technology to provide scalable and flexible solutions for storing and processing large volumes of data. By centralizing data storage and management in the cloud, organizations can streamline data access, improve collaboration, and derive valuable insights to drive business decisions.

Importance of Cloud Data Warehouse Tools

Cloud data warehouse tools play a crucial role in modern data-driven organizations by enabling efficient data storage, management, and analysis. These tools offer a wide range of features and capabilities, including data integration, transformation, and visualization, allowing organizations to extract actionable insights from their data assets. Moreover, cloud data warehouse tools provide scalability, reliability, and cost-effectiveness, making them essential for organizations looking to harness the power of data to gain a competitive edge in today's digital landscape.

10 Best Cloud Data Warehouse Tools of 2024

Discover the leading cloud data warehouse tools revolutionizing data management and analytics in 2024.

Amazon Redshift

Amazon Redshift is a petabyte-scale cloud data warehouse that supports high-end data analytics and integrates seamlessly with other AWS automation tools. It facilitates SQL querying on structured data and provides lightning-fast performance without requiring extensive infrastructure investments.


  • Massively Parallel Processing (MPP): Redshift optimizes query performance by employing columnar storage, data compression, and zone maps. The MPP architecture is used to distribute SQL operations across resources efficiently.
  • Automated Backups and Provisioning: Administrative tasks like infrastructure provisioning, data backups, and replication are all automated by Redshift. There are customized options to fine-tune your settings according to the workload.
  • Machine Learning: Redshift makes use of machine learning to maintain its high performance, irrespective of concurrent usage. It employs advanced algorithms to predict query run times and allocates them to the optimal queue for better processing.
  • End-to-end encryption: Robust encryption systems, fault-tolerant nodes, and compliance with significant data security laws such as HIPAA, PCI, SOC, and more make Redshift a secured data warehouse tool for your business.


The Amazon Redshift storage pricing begins at $0.25 per hour, but the prices may vary depending on your business needs and the region you are operating in.

Google BigQuery

Google BigQuery is a cloud-native data warehouse that offers state-of-the-art automation tools to store and query vast amounts of data. It helps you execute super-fast SQL queries within seconds, providing real-time analytical insights. BigQuery is considered a cost-effective data warehouse tool where you can incorporate built-in machine learning functionalities to craft robust AI models.


  • Columnar Storage Format: This cloud data warehouse tool organizes data in tables consisting of rows and columns. It also offers comprehensive support for database transaction semantics (ACID).
  • Geospatial Analysis: BigQuery offers functionalities related to location-based data mapping and analysis. This feature is especially useful when you are looking for new business avenues or want to improve data spread over multiple regions.
  • Business Intelligence (BI) Engine: BigQuery’s BI engine is a high-speed, in-memory analysis service that expedites numerous SQL queries from various sources, including data visualization tools. You can also leverage clustering and partitioning techniques to enhance performance, especially with large datasets.


BigQuery has separate pricing for storage and computation. You can choose between two compute pricing models—on-demand and capacity pricing. For running queries through the on-demand mode, you are charged $6.25 per TiB. The capacity compute pricing has a pay-as-you-go model, charging you for whichever editions and slot commitments you choose.

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Snowflake is a cloud-based data warehouse tool that is renowned for its ease of use, agility, and adaptability. It operates within a comprehensive Software as a Service (SaaS) architecture and can be hosted on three different cloud platforms–Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure.


  • Time Travel: Snowflake offers a distinctive feature where you can track data changes of tables and schemas for 90 days. You can also restore a few objects of any version within the given period.
  • Automation Tools for Clusters: This data warehouse has auto-scaling and auto-suspend functionalities that enable clusters to be automatically started, stopped, or adjusted dynamically per your business demands.
  • Zero Copy Cloning: The cloning feature in Snowflake is a swift and cost-effective way to generate copies of databases, schemas, and tables in near real-time. When the object is cloned, the entire storage content is not duplicated; only the metadata gets manipulated.


Snowflake provides four pricing options to suit your requirements.

  • Standard Edition: Starts at $2 per credit.
  • Enterprise Edition: Charges at $3 per credit.
  • Business Critical Edition: Commences at $4 per credit.
  • Virtual Private Snowflake (VPS): This is a customizable plan.

IBM DB2 Warehouse

IBM DB2 Warehouse is an advanced cloud data warehouse tool that facilitates self-scaling data storage and processing capabilities. Powered by the DB2 relational database, this warehouse can operate in private clouds, virtual private clouds, and other container-supported infrastructure. 


  • Continuous Availability: DB2 has a pureScale feature, which ensures robust business continuity and 99% availability. You can deploy it using AWS or an on-premise data warehouse. The continuous availability reduces the risk of unplanned downtime significantly.
  • Extensive Support for Data Types: This data warehouse offers support for a wide array of data types and formats, including XML, JSON, text, and spatial data, within a unified multi-model database. You can also leverage various programming languages such as Java, R, Python, C++, and more.
  • RESTful APIs: These are fully integrated within the DB2 distributed data facility. This feature simplifies your connectivity between the data repository and web, mobile, and cloud applications.


DB2 warehouse offers a free Lite plan for a sandbox environment where you get $200 free credit on signing up. The Standard plan has a monthly fee of $99, while the Enterprise plan charges $969 monthly.

Oracle Autonomous Data Warehouse

Oracle Autonomous Data Warehouse is a cloud-based solution that has numerous built-in tools for data analysts, scientists, and developers. These tools assist you with data loading and transformation, enabling queries across different data types, creating business models through machine learning analysis, and generating insights.


  • Elastic and Auto Scaling: You get the flexibility to scale CPU and IO resources to match increased workload demands. Additionally, this data warehouse automatically expands storage capacity, ensuring optimal database performance and no downtime.
  • Ease in Migrating Databases: You can seamlessly migrate data from a number of databases, which include MySQL, Amazon Redshift, and PostgreSQL, to Oracle Autonomous Data Warehouse using the Oracle Cloud Infrastructure Database Migration Service.
  • Extensive Support: With this data warehouse, you will get access to a host of automation cloud tools from Oracle, like Oracle GoldenGate Marketplace, Oracle Analytics Cloud, and more. There is support for third-party data integration tools and connectivity through SQL Net, JDBC, and ODBC.


You can deploy Oracle Autonomous Data Warehouse through three types of infrastructure—Serverless, Dedicated Exadata, and Exadata Cloud@Customer infrastructure. The pricing is calculated through the ECPU per hour, with unit prices beginning from $0.336 for serverless and $0.0807 for dedicated Exadata.

Teradata VantageCloud

Teradata VantageCloud is a cloud analytics platform known for being an all-encompassing solution. It offers you an extensive range of data management solutions, including data warehouse tools and applications.


  • Unified Data Analytics Environment: This comprehensive data warehouse integrates descriptive, predictive, and prescriptive analytics functionalities into a single platform. You can also incorporate autonomous decision-making capabilities, machine learning, and visualization tools within this unified environment.
  • Connectivity to Several Languages: You enjoy the flexibility to easily connect with tools and programming languages like R, Python, RStudio, Teradata Studio, and any SQL-based tools. 
  • Risk Mitigation: Teradata VantageCloud offers risk mitigation solutions by utilizing advanced analytical techniques to analyze patterns and signals within your dataset. Thus, you can comprehensively evaluate potential risks and align your risk management strategies effectively.


The Teradata Vantage Cloud offers two packages, Lake and Lake+. For the former, pricing begins at $4,800 per month. For the latter, the pricing is $5,700 per month.

SAP Datasphere

SAP Datasphere is one of the premier data warehouse tools built upon SAP Data Warehouse Cloud. This highly adaptable solution draws its foundation from the robust in-memory power of the SAP HANA Cloud database. It is structured in a modular architecture with semantic modeling features for simplified setup and optimal use of resources. 


  • Business Data Fabric: This is one of the best and unique features of the SAP Datasphere. The open data ecosystem of SAP Datasphere creates a business data fabric that allows you to deliver valuable data to every consumer within your business. This feature enables stakeholders to access meaningful and contextualized data, fostering better decision-making.
  • Automation Tools: SAP Datasphere comes with a generative AI-based code development with Joule Copilot, specifically tailored for Java and JavaScript application development. With this feature, you can benefit from automated code generation, intelligent suggestions, and an optimized project development lifecycle.
  • Integration Suite: SAP Integration Suite is an integration platform-as-a-service (iPaaS) that facilitates the easy integration of on-premise and cloud-based processes. This suite will be useful in expediting innovation through smoother data flows and enhanced collaboration between different applications. 


The SAP Datasphere capacity unit (CU) offers a flexible pricing model to adapt to workloads of different scales. The quantity of CU determines the number of services (compute, storage, data lakes, catalogs, etc) \required for the billing. You can check out the SAP Datasphere Estimator to understand the number of capacity units for your business needs.


Firebolt is one of the best cloud data warehouses that delivers swift query performance. At its core, a robust SQL query engine segregates the storage and computing functionalities. Firebolt can provide you with numerous isolated resources within a unified environment, ensuring efficient and flexible operations.


  • SQL Supremacy: If you are proficient with SQL, your transition to Firebolt will be quite simple. This cloud data warehouse tool has a PostgreSQL dialect and complies with ANSI-SQL standards. You can scale operations through SQL commands and integrate orchestration tools like Apache Airflow to manage your workloads better.
  • Order-of-magnitude Leap: Firebolt has engineered an order-of-magnitude leap in performance where you can load raw data stored in Parquet, JSON, and Avro file formats. To efficiently analyze vast volumes of semi-structured data, this petabyte-scale platform allows you to perform SQL array manipulations through native Lambda functions.
  • Superior Performance: Firebolt has a unique decoupled architecture that facilitates seamless horizontal scaling. Here, you can leverage a range of node types and scale the infrastructure from 1 to 128 nodes within seconds. 


Firebolt has a simple pay-as-you-go model where you are billed only for the services you currently employ. The rate of each resource is mentioned on your Firebolt’s user interface, promoting transparency and simplicity.

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Yellowbrick Data Warehouse

The Yellowbrick Data Warehouse is a fully elastic data warehouse designed to meet diverse workloads. It employs a massively parallel processing (MPP) SQL database to efficiently handle batch, real-time, ad hoc, and mixed workloads. This cloud data warehouse supports AWS, Google Cloud, Azure, and on-premises.


  • Independent Instances: Each instance generated within this data warehouse runs independently. Thus, there is no shared metadata or a single point of failure, eliminating global outages and ensuring resilience in data operations.
  • Direct Data Accelerator Architecture: Yellowbrick’s proprietary Direct Data Accelerator Architecture employs an OS bypass technology that boosts the in-memory analytics performance. This infrastructure does not rely on a conventional database buffer cache, resulting in predictable response times and lower data costs.
  • SQL-driven Elasticity: Yellowbrick has distinct storage and computing capabilities. The virtual compute clusters (VCCs) can be created, resized, and dropped as needed using SQL commands. You have the flexibility to route ad hoc and critical workloads to different clusters for parallel processing and create more clusters whenever demand arises.


Yellowbrick offers three pricing plans.

  • The On-demand monthly plan is priced at $0.28 per vCPU per second metering.
  • The Predictable Value Subscription plan for one year is priced at $613 per vCPU. 
  • The Guaranteed 3-Year Price Subscription plan is priced at $482 vCPU. 

It is important to note that the subscription rates for the latter two plans are calculated for 8,760 hours annually.

Azure Synapse Analytics

Azure Synapse Analytics is an enterprise-grade analytics service built upon the Azure SQL Data Warehouse. It combines top-tier SQL technologies with the capabilities of Apache Spark for handling big data. In addition, it integrates with Azure Data Explorer for log and time series analysis.


  • Comprehensive Integrations: One of the best features of Azure Synapse is how it encompasses cloud data warehousing, machine learning analytics, and dashboarding functionalities. You can seamlessly integrate it with Azure Data Factory, Azure Data Explorer, Azure SQL Database, CosmoDB, Synapse Studio, Synapse Workplace, and more.
  • Machine Learning and Visualization: You can train your models with machine learning by using Apache Spark MLlib, Azure Machine Learning, and various other open-source libraries. To generate impactful visualizations on your analyzed data, you can utilize advanced tools from Microsoft Power BI.
  • Advanced Storage Solution: Azure Synapse leverages Azure Data Lake Storage (ADLS) Gen2 to handle vast volumes of data. It combines ADLS Gen1 and Azure Blob Storage, providing you with a high availability, secure, scalable storage option.


Azure Synapse has pre-purchase plans. You have the option to choose from 6 tiers, beginning from $4,700 and going up to $259,200 per year.

Integration of Cloud Data Warehouse Tools with Airbyte

After going through some of the best cloud data warehouse tools, you must have understood how crucial it is to choose the right one for your business. However, before selecting one, you must assess your business requirements and the data type you are dealing with.

If you have large datasets spread across multiple sources, there is a straightforward way to load them onto the data warehouse tool of your choice. Data integration and replication platforms like Airbyte seamlessly migrate data from different source points through connectors.

On this no-coding platform, you can find pre-built connectors for almost all the data warehouses mentioned above. If you do not see a connector among their 350+ catalog, you can always build a custom one with the help of their Connector Development Kit. With this guide, you can establish the source and destination of your database in just a few minutes!

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Final Takeaways

Cloud-based data warehouse and automation tools are fast, highly scalable, and most available on a pay-per-use basis. They improve access to information through quick query responses and allow you to perform data analysis to get deeper insights. With a multitude of choices available, be sure to consider costs, data security, performance, and ease of use before finalizing one.

Data warehousing tools are critical to your business as they help you manage data analytic processes to drive the workflow. If you are looking for a cloud data warehouse with automation tools, this article will give you a quick overview of some options available. It is important to note that the list is in no particular order or ranking.


  1. What are the key advantages of using cloud data warehouse tools over traditional on-premises solutions?
    Cloud data warehouse tools offer scalability, flexibility, and cost-effectiveness compared to traditional on-premises solutions. They enable organizations to easily scale their data storage and processing capabilities as needed, without the need for expensive hardware investments. Additionally, cloud data warehouses provide built-in redundancy and disaster recovery features, ensuring data availability and reliability.
  2. How do I choose the right cloud data warehouse tool for my organization's needs?
    Selecting the right cloud data warehouse tool involves considering factors such as scalability, performance, integration capabilities, pricing, and security features. Evaluate each tool's strengths and weaknesses in relation to your specific use case and business requirements. Conduct thorough research, seek recommendations from peers, and perform proof-of-concept tests to inform your decision-making process.
  3. What are the pricing models for cloud data warehouse tools?
    Pricing models for cloud data warehouse tools vary among providers and may include factors such as storage capacity, data transfer volume, compute resources, and additional features or services. Some providers offer pay-as-you-go pricing, while others may require upfront commitments or offer tiered pricing plans. Carefully review each provider's pricing structure and consider your organization's budget and anticipated usage patterns when making a decision.
  4. Can cloud data warehouse tools handle real-time data processing and analytics?
    Many cloud data warehouse tools support real-time data processing and analytics capabilities, allowing organizations to analyze streaming data and derive insights in near real-time. Leveraging technologies such as in-memory processing, distributed computing, and event-driven architectures, these tools enable fast and efficient data processing. Real-time analytics is beneficial for applications such as fraud detection, IoT data analysis, and personalized customer experiences.
  5. What security measures are in place to protect data stored in cloud data warehouses?
    Security is a top priority for cloud data warehouse providers, who implement measures such as encryption at rest and in transit, access controls, data masking, threat detection, and compliance certifications to protect stored data. Additionally, providers offer tools and services to help organizations monitor and manage security risks effectively. It's crucial for organizations to understand the security features and compliance standards of their chosen cloud data warehouse provider and implement best practices to protect their data.

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Frequently Asked Questions

What is ETL?

ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.

What is ?

What data can you extract from ?

How do I transfer data from ?

This can be done by building a data pipeline manually, usually a Python script (you can leverage a tool as Apache Airflow for this). This process can take more than a full week of development. Or it can be done in minutes on Airbyte in three easy steps: set it up as a source, choose a destination among 50 available off the shelf, and define which data you want to transfer and how frequently.

What are top ETL tools to extract data from ?

The most prominent ETL tools to extract data include: Airbyte, Fivetran, StitchData, Matillion, and Talend Data Integration. These ETL and ELT tools help in extracting data from various sources (APIs, databases, and more), transforming it efficiently, and loading it into a database, data warehouse or data lake, enhancing data management capabilities.

What is ELT?

ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.

Difference between ETL and ELT?

ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.