Keeping all your business data in one place makes it much easier to extract value from it. With a cloud data warehouse, you can collect and store all your data from disparate sources. You may then process or analyze the data to derive timely business insights.

Looking for a super-fast, analytics-oriented, cloud-based data warehousing tool? Here are some of our recommended tools:

(Looking for data integration tools? Check out our roundup on the best data integration tools.)

Related Reading: How to Choose the Right Data Warehouse Tool for Your Business

1. Amazon Redshift

Redshift is a cloud-based data warehousing tool for enterprises. The fully-managed platform can process petabytes of data in seconds. That's why it's suitable for high-speed data analytics. It also supports automatic concurrency scaling. The automation increases or decreases query processing resources to match workload demand. This way, you can execute hundreds of concurrent queries without the operational overhead. Additionally, Redshift allows you to scale your cluster or switch between node types. Thus, it enables you to optimize data warehouse performance and cut operational costs. 

Related Reading: How to Set Up an Amazon Redshift Data Warehouse

2. Microsoft Azure

Azure SQL data warehouse is a cloud-based relational database from Microsoft. You can optimize it for petabyte-scale data loading/processing and real-time reporting. The platform has a node-based system, and it employs massively parallel processing (MPP). The architecture is suitable for optimizing queries for concurrent processing. Thus, it enables you to extract and visualize business insights much faster.

The data warehouse is compatible with hundreds of MS Azure resources. For example, you may build intelligent apps with the platform's machine learning tools. Also, the platform lets you store different types of structured and unstructured data. The data may come from diverse sources, such as on-premise SQL databases and IoT devices.

3. Google BigQuery

BigQuery is a cost-effective data warehousing tool with built-in machine learning capabilities. You can integrate it with Cloud ML and TensorFlow to create powerful AI models. It can also execute queries on petabytes of data in seconds for real-time analytics.

This cloud-native data warehouse supports geospatial analytics. With it, you may analyze location-based data or discover new lines of business.

BigQuery can separate compute and storage. So, it enables you to scale processing and memory resources based on business needs. Separation lets you manage the availability, scalability, and cost of each resource.

4. Snowflake

You may use Snowflake to set up an enterprise-grade cloud data warehouse. With the tool, you can analyze data from various unstructured and structured sources. The multi-cluster, shared architecture separates storage from processing power. Thus, it allows you to scale CPU resources based on user activities. The scalability also accelerates querying performance to deliver actionable insights faster.

Snowflake's multi-tenant design lets you share data across your organization in real time. You can do this without moving any data.

5. Micro Focus Vertica

Vertica is an SQL data warehouse available in the cloud on platforms like AWS and Azure. You may also deploy it on-premise or as a hybrid. The tool supports columnar storage and uses MPP to increase query speed. Its shared-nothing architecture reduces competition for shared resources.

Vertica offers built-in capabilities for analytics. These include machine learning, pattern matching, and time series. It also supports standard programming interfaces, such as OLEDB. The software uses compression to optimize storage. 

6. Teradata

Teradata is a data warehousing platform for collecting and analyzing vast amounts of enterprise data in the cloud. The tool provides super-fast parallel querying infrastructure. This way, it speeds up access to actionable insights. Teradata's QueryGrid delivers best-fit engineering. It does this by deploying multiple analytic engines to deliver the right tool for the job.

It also employs smart in-memory processing to optimize database performance at no extra costs. Using SQL, the data warehouse connects to commercial and open-source analytical tools.   

7. Amazon DynamoDB

DynamoDB is a scalable NoSQL, cloud-based database system for enterprises. It can scale querying capacity to 10 or even 20 trillion requests per day over petabytes of data. Also, it uses key-value and document data management to create a flexible schema. Thus, tables can scale automatically by adding new columns based on growing requirements.

The database system comes with DynamoDB Accelerator (DAX). That's an in-memory cache that can shorten the time required to read tabulated data from milliseconds to microseconds. Thus, it powers super-fast querying processes, including at millions of request per second.

8. PostgreSQL

PostgreSQL is an open-source database management solution available in the cloud.  SMEs and large enterprises alike can use the resource as their primary database. For example, you may use it to drive internet-scale business applications. To work with geospatial data, consider integrating PostgreSQL with the PostGIS extension. The integration will enable you to offer location-based business solutions.

The platform supports both SQL and JSON querying. And you can optimize database performance with features like Multi-Version Concurrency Control (MVCC).       

9. Amazon Relational Database Service (RDS)

Amazon RDS enables you to create a cost-effective cloud-based relational database. The platform is compatible with six database engines, including PostgreSQL and Amazon Aurora. You can generate replication within the system to boost availability for operational workflows. For instance, Read Replicas let you divert read traffic from your primary database to virtual copies. They're an option when you need to serve high-volume applications. You may also scale your RDS computing and memory capabilities to 32 vCPUs and 244 gigabytes of RAM.

10. Amazon Simple Storage Service S3

Amazon S3 can serve cloud storage needs at scale for small and large enterprises. The scalable, object-oriented service also supports big data analytics. It stores data in "buckets," each of which can hold up to 5 terabytes. The platform offers several cost-effective storage class options. For example, you may lower costs using S3 Standard-IA to store occasionally-accessed data.


SAP HANA is a cloud-based resource with in-memory caching capabilities. Thus, it supports high-speed, real-time transaction processing, and enterprise-wide data analytics. It also provides a simple, centralized interface for data access, integration, and virtualization.

With data federation, you can query remote databases without moving your data. These data sources include Hadoop and SAP Adaptive Server Enterprise (SAP ASE). SAP HANA supports text and predictive analytics and intelligence-driven app development. 

12. MarkLogic

MarkLogic provides a NoSQL database system with powerful querying and versatile application services. The schema-agnostic platform lets you ingest data of any form or type, as is. That's because it has native storage for predefined schemas. Supported formats include geospatial data, JSON, RDF, and massive binaries like videos. Its built-in search engine simplifies querying once you've loaded data. It enables you to start asking questions and getting answers right away. 

13. MariaDB

MariaDB is an enterprise-grade database tool with support for customer-facing applications. You may also use it to create a columnar database to perform real-time analytics. The solution employs massive parallel processing (MPP) too. So, it enables you to execute SQL queries across hundreds of billions of rows. You don't need to create indexes before doing this. MariaDB can scale out based on workload and business needs, or in the cloud.

14. Db2 Warehouse

IBM Db2 Warehouse is a fully-managed, scalable cloud data storage platform. It's suited to analytics and artificial intelligence applications. The system provides built-in machine learning tools. You may exploit these to train and deploy ML models within the ecosystem. Supported languages for ML developments include SQL and Python.

Also, Db2 Warehouse has an intuitive UI or REST API. You may use the tools to manage the elastic scaling of processing power and storage. Multiple servers crank up the platform's MPP capabilities. These facilitate super-fast concurrent querying for large data sets. 

15. Exadata

The Oracle's "autonomous data warehouse" runs on the Exadata cloud infrastructure. The self-driving platform leverages adaptive machine learning to automate administrative tasks. These range from tuning and patching to monitoring, upgrading, and securing your database.

Creating an autonomous Exadata data warehouse is easy. Start by specifying tables and loading your data with only a few clicks. The system employs parallelism and columnar processing to boost performance and scalability. 

16. BI360 Data Warehouse 

Solver BI360 enables enterprises to consolidate massive amounts of data from disparate sources. These include CRM, ERP, accounting software, and unstructured data stores. It's pre-configured to simplify database deployment and business intelligence workflows.  The cloud-based solution has intuitive dashboards and analytics interfaces. For example, you may use the Data Explorer to explore data. It's also possible to add modules and dimensions.

The data warehouse runs on MS SQL Server. And it offers built-in automated data loading tools. These make light work of database querying and searching.

17. Cloudera

Cloudera's operational database is a low-latency, high-concurrency cloud-hosted platform. It's ideal for analyzing big data and extracting real-time business intelligence. The resource supports portable and flexible distribution, which is cost-effective. Thus, it provides the necessary elasticity to move between on-premises and cloud-based servers.

The platform utilizes HBase to create columnar NoSQL storage for unstructured data. But Kudu helps to create a relational database for structured data within Cloudera. Also, the tool supports predictive modeling based on real-time and historical data.

A scalable cloud-based storage resource enables you to consolidate all your enterprise data. You may then analyze the massive chunks of data to get timely business intelligence. Looking for something to integrate with your data warehouse? Try out Xplenty now!