Machine learning and business analytics require enormous storage capacities for large amounts of data. Therefore, many companies offer scalable, fast and user-friendly OLAP (Online Analytical Processing) data warehousing services for storing, transforming and analysing data - for a wide variety of business applications. Data warehousing in the cloud has many advantages: High scalability, fast data processing and flexible pricing are good arguments for migrating to the cloud. We have compiled the top10 OLAP data warehousing services that one could consider for building a cloud data infrastructure.

Rank 10 - DuckDB

The DuckDB is an embedded, column-based database for data science and analytics applications. Most databases are designed for client-server use cases and are therefore not suitable for local queries. In-memory tools such as Pandas or Datatables are used to solve this problem when processing data locally before uploading it to servers. However, these are very limited to the amount of memory available. DuckDB addresses this challenge by providing a DB engine that enables fast OLAP query performance in conjunction with an in-process database on local devices. While the engine is not suitable for large enterprise data warehousing use cases, as an embedded database it serves its purpose for smaller applications.

Rank 9 - Yellowbrick Data Warehouse

The fledgling cloud warehousing company has a multi-parallel processing analytics database in its portfolio that combines the advantages of on-premises and cloud data warehouses. With industry-leading speed, agile and elastic provisioning, and a predictable subscription pricing model, queries can be executed in real-time and databases can scale elastically. A special feature of Yellowbrick's data warehouse: the cloud-native architecture based on Kubernetes, which can basically be used anywhere and is therefore inherently very flexible.

Rank 8 - Teradata Vantage

Teradata Vantage is a modern analytics platform that combines open source and commercial analytics technologies to operationalise insights and solve complex business problems. The cloud warehousing solution can be deployed in the cloud, on-premise or as a hybrid solution. With a pay-as-you-go options, Teradata offers predictable 'total cost of ownership' (TCO) and an easily scalable system. Teradata Data Warehouse software enables users to gain business value and insight through integrated in-database analytics and its parallel processing architecture.

Rank 7 - SAP Data Warehouse Cloud

The SAP Data Warehouse Cloud combines various data warehousing and analysis functions in one platform for business users and advanced database users. Based on the SAP HANA database, the Data Warehouse Cloud offers automatically scalable computing and storage capacities as well as fast query performance. With advanced self-service analytics built in, Data Warehouse Cloud can easily load, join and transform data and find information on specific KPIs or business use cases.

Rank 6 - IBM Db2 Warehouse

Distilled from IBM's Db2 database, the data warehousing solution is designed to handle structured and unstructured data in local, private and public cloud environments at scale. It combines an enterprise-grade data management system with an AI platform for transforming and manipulating data. With a fast in-memory data processing engine and independently scalable compute and storage capabilities, the Db2 Warehouse provides fast and flexible OLAP performance, as well as built-in capabilities to perform machine learning on the Cloud computing.

Rank 5 - Oracle Autonomous Data Warehouse

The Oracle Autonomous Data Warehouse is a cloud-based, automatically scalable data warehouse with self-service capabilities for loading, transforming and cataloguing data. With the ADW, Oracle focuses on an end-to-end experience rather than just a data warehousing service. The ADW has implemented many tools such as machine learning, data loading tools, RESTful services as well as graph analytics. Integrated tools such as APEX, AutoML functionality and SQL tools make the ADW attractive for business users and data scientists to gain insights faster than with traditional data warehousing solutions.

Rank 4 - Microsoft Azure Synapse Analytics

Azure Synapse brings together data analytics and data warehousing. With serverless or dedicated options, Azure Synapse can be used for many purposes. By offering serverless and dedicated options, Azure Synapse Analytics provides fast database analytics performance without data protection risks. The Apache Spark and SQL engines are integrated out of the box, enhancing collaboration between data scientists on advanced analytics solutions. Synapse Analytics provides a unified experience of rapid data storage, exploration, transformation and delivery at scale - especially suited for BI and ML.

3rd place - Google Big Query

Google BigQuery is a serverless Data Warehouse as a Service (DWaaS). The system automatically scales storage and processing power to meet customers' needs and provides an engine that uses standard SQL to access and manipulate data. Because storage and nodes are completely serverless and scale automatically, the required maintenance and operating costs are lower compared to solutions from other providers. Google's data warehousing service offers options for integrating ML, BI, cross-platform data analytics and geospatial data analytics using the BigQuery SQL engine.

2nd place - Redshift

Redshift is a managed cloud data warehouse provided by AWS. Like an on-premises cluster, Redshift is based on the concept of virtual nodes that need to be provisioned, configured and managed. With SQL-based tools, Redshift provides fast query performance and scalability. By estimating the workload, clusters can be resized to match compute and storage capabilities. In addition, the database system can be scaled automatically. Depending on the user's skill level, AWS offers multiple approaches to cluster management and overall complexity of use.

Place 1 - Snowflake

Snowflake is a cloud-based data warehouse and analytics system based on standard SQL that supports both structured and semi-structured data. With its cloud-native architecture and low management overhead, Snowflake is a very flexible and highly scalable database service. Snowflake's shared multi-cluster data architecture allows a given data set to be used concurrently without slowing down processing power by logically separating storage and processing power. Both memory and processing power are automatically adjusted to real-time needs and even suspending clusters that are not in use is possible.