Best Software to Build a Data Warehouse in the Cloud: Features, Benefits, Costs

Cloud Data Warehouse Overview - ScienceSoft

ScienceSoft has been rendering data warehouse consulting services for more than 15 years.

Cloud data warehouse: the essence

A cloud data warehouse is a system, which uses the space and compute power allocated by a cloud provider to integrate and store data from disparate data sources. It is employed for data structured storage, analysis and reporting.

Cloud vs. On-premises DWH


Cloud data warehouses

On-premises data warehouses






General features

  • Flexible querying with SQL (including big data).
  • Data ingestion with ETL/ELT.
  • Quick deployment.
  • Automation of DWH maintenance tasks (backups, patching, replication, etc.)


  • Relational data.
  • Structured big data.
  • Unstructured data, including real-time data.
  • Federated query support.
  • Integration with BI and analytics software.


  • Massively parallel processing.
  • Optimized data storage for high performance query processing (columnar data storage, data compression, etc.).
  • Materialized view support and result-caching.
  • ML capabilities to manage performance and concurrency.


  • On-demand near-infinite scaling of compute and storage resources.
  • Possibility to scale compute and storage separately.


  • High availability (up to 99,99%).
  • Fault tolerance due to automatic backups, (re)-replication and restore.

Agile pricing

  • Consumption-based and flat-rate pricing options.
  • Controllable pricing with the possibility to save up with long-term commitments.

Security and compliance

  • Data encryption.
  • Built-in authentication and authorization.
  • Columnar-level access control.
  • Automated threat detection, vulnerability assessment, etc.
  • Compliance to national, regional, and industry-specific requirements.

Need Expertise to Implement a Secure Cloud DWH?

ScienceSoft is ready to design and implement a cloud DWH that meets your specific data storage needs and helps you consolidate disparate data for further analysis and reporting.

Cloud DWH – Important Software Integrations for Reduced Costs and Time to Value

Important integrations for a cloud DWH - ScienceSoft

A data lake

– for storing structured data ready for analysis in a cloud DWH, and big volumes of semi-structured and unstructured data – in the data lake.

Analytics and reporting software

– for analyzing, reporting and visualizing data to handle end-to-end analytics workflows.

What Determines Cloud DWH Success

Creating a PoC

– for testing the ease of use, performance and concurrency scaling capacity of the cloud data warehouse for all users.

On-demand pricing model

– to meet precise usage needs without big upfront costs and overprovisioning.

High security and data protection capabilities

– to avoid the risk of data leakage, prevent unauthorized data access, disclosure of protected data attributes and malicious recovery.

Out-of-the-box integrations with data sources; SDKs in most common programming languages

– to reduce the development costs.

Cloud DWH benefits

TCO savings

Cloud DWH does not require purchasing and maintaining expensive hardware; it scales cost-effectively, minimizing the risk of infrastructure overprovisioning.

Decreased development costs

Decreased IT staff time due to DWH automation – automatic up- and down-scaling of storage and compute resources, data management tasks (data collection, aggregating, modeling).

Fast time to insight

Instant scalability, flexibility and reliability of the cloud enables DWH enhanced performance and availability, which results in accelerated business intelligence and, thus, faster business decisions.

Top 6 Cloud DWH Platforms We Recommend

The presented cloud data warehouse solutions are recognized leaders of Gartner Magic Quadrant and Forrester Wave reports. All of them are suitable for mid-sized and large businesses.

Amazon Redshift

ScienceSoft recommends


An enterprise-level DWH to handle diverse workloads by scaling up and down storage and compute and paying for them separately.

  • Agile SQL data querying (including big data)
  • Native integrations with the AWS ecosystem (including Amazon S3)
  • Federated queries support
  • Advanced security, etc.

Best for:

Big data warehousing.


  • On-demand pricing starts from $0.25/hour (depends on the type and number of nodes in the cluster).
  • Reserved instance pricing includes three options (No, Partial, All Upfront) and allows saving up to 75% over the on-demand option.

Azure Synapse Analytics

ScienceSoft recommends


The Microsoft product to unify enterprise data warehousing and big data analysis.

  • Quick DWH deployment and SQL data querying
  • Native integrations with a data lake, operational databases, BI and ML software
  • Intelligent workload management
  • Comprehensive security features
  • Separate billing for compute and storage, etc.

Best for:

Enterprise DWH.


  • Data storage: $122.88/TB/mo ($0.17/TB/hour). The data storage size includes your DWH data and 7 days of incremental snapshot storage.
  • Storage transactions are not billed.
  • Query performance pricing depends on the service level and region.

Google BigQuery


A cloud-based DWH that allows SQL querying against huge data sets with results delivered in seconds.

  • Multi-cloud capabilities
  • NLP
  • Built-in integrations with a data lake, operational databases, big data ecosystem, BI and AI, etc.

Best for:

Data mining and varied workloads.


Storage costs: $0.02/GB/mo ($0.01/GB/mo for long-term storage).

Streaming inserts: $0.01/200 MB.

For query performance, 2 subscription options are available:

  • Pay-as-you-go ($5/TB, 1st TB/mo is free).
  • Flat-rate pricing (from $10K/mo for a dedicated reservation of 500 processing units).

Loading, copying or exporting data, metadata operations, deleting datasets, tables, views, etc. – free.

Oracle Autonomous Data Warehouse


An easy-to-run cloud DWH service with automatic scaling and management of patches and updates.

  • Elastic separate scaling of storage and compute
  • SQL Developer and BI tools support
  • Built-in support for analytical SQL and ML
  • Profound security
  • Deployment variations, etc.

Best for:

High performance queries.


  • Compute costs: $1.3441/CPU/hour
  • Data storage: $118.40/TB/mo (in the public cloud).

IBM Db2 Warehouse on Cloud


An elastic cloud DWH built for high-performance analytics and ML workloads.

  • Flexible SQL data querying
  • Integration with Apache Spark
  • Compatibility with IBM Netezza and IBM workloads
  • Built-in ML and geospatial capabilities
  • Independent scaling of storage and compute
  • Deployment on multiple cloud providers (IBM Cloud, AWS), etc.

Best for:

Hybrid cloud deployment needs and intensive analytics workloads.


  • Flex One:

    Data storage: $0.005/10GB/hour

    Compute: $0.68/Instance/hour, $0.05/Virtual Processor Core/hour.

  • Flex:

    Data storage: $0.48/1TB/hour

    Compute: $2.11/Instance/hour, $1.48/16 Virtual Processor Cores/hour.

  • Flex performance:

    Data storage: $2.03/2.4TB/hour

    Compute: $7.76/Instance/hour, $2.60/24 Virtual Processor Cores/hour.



An elastic data warehouse running on three major clouds (AWS, Microsoft Azure and Google Cloud).

  • Querying structured and semi-structured data with SQL, storage with 2-3x compression and multi-cluster computing resources for near-unlimited concurrency.
  • Pre-built connectors for BI and analytics tools.
  • Data encryption, dynamic data masking and tokenization.
  • Compliance with SOC2 Type 2, ISO/IEC 27001, PCI DSS, HIPAA, HITRUST, FedRAMP and more.


An easily deployed DWH.


Not available publicly.

These platforms offer similar functionality within the key criteria for choosing DWH technology – scalability, reliability, flexibility, and security. Thus, the decision on what cloud DWH platform to opt for mostly depends on:

  1. A platform’s performance (ability to accomplish the assigned tasks).
  2. Implementation costs, which can be different for every particular situation.

Puzzled to Decide on the Best Cloud DWH?

ScienceSoft’s team will help you choose the cloud DWH platform to suit your short- and long-term business goals and make your DWH project a success.

Implementation of a cloud data warehouse

Having 15+ years of hands-on experience in delivering DWH solutions, ScienceSoft can provide you with flexible centralized storage on a fitting cloud platform and enable analytics capabilities for you to optimize internal business processes and enhance decision-making.

Cloud data warehouse consulting

Our team:

  • Analyzes your business needs and elicits requirements for a future cloud DWH solution.
  • Designs cloud data warehouse architecture.
  • Outlines the optimal cloud DWH platform and its configurations.
  • Consults on data governance procedures.
  • Designs a cloud DWH implementation/migration strategy.
  • Conducts admin trainings.
  • Delivers PoC for complex projects.

Cloud data warehouse implementation

Our team:

  • Analyzes your business needs and defines the required cloud DWH configurations.
  • Delivers PoC for complex projects.
  • Does data modeling and sets up ETL/ELT pipelines.
  • Develops and integrates a cloud DWH into the existing data ecosystem.
  • Runs QA.
  • Provides user training and support, if required.

About ScienceSoft

ScienceSoft is a global IT consulting and IT service company headquartered in McKinney, TX, US. Since 2005, we assist our clients in delivering DWH solutions with the help of end-to-end data warehousing services to encourage agile and data-driven decision-making. Our long-standing partnerships with global technology vendors such as Microsoft, AWS, Oracle, etc. allow us to bring tailored end-to-end cloud data warehousing solutions to business users.