Data Warehouse Software: 5 Best Data Warehousing Tools
ScienceSoft has been helping businesses choose optimal data warehousing solutions for 15 years.
Data warehouse systems: the essence
Data warehouse software is a central component of a company’s or a department’s data ecosystem that serves to retrieve, aggregate, and store data from internal and external data source systems for further analysis and reporting.
Data warehouse system: key features
- On-premises deployment.
Cloud deployment (public, private, multi-cloud).
- Data processing with ETL/ELT.
Full and incremental data extraction/load.
Structured, semi-structured, unstructured data ingestion.
Big data ingestion.
Streaming data ingestion.
Data loading and querying using SQL.
- Subject-oriented data storage.
Time-variant (data from the historical point of view) data storage.
Nonvolatile (read-only) data storage.
Granular data storage.
- Massively parallel processing.
Improved data searching efficiency (materialized view support, data indexes, result-caching, etc.).
ML capabilities to manage DWH performance and concurrency.
Security and compliance
- Data encryption.
- Securing data access with user authentication and authorization.
- Fine-grained access control (row- and column-level).
- Compliance with national, regional, and industry-specific regulations (for example, GDPR, HIPAA, PCI DSS).
Top 5 data warehouse products for comparison
Our list of data warehouse tools suitable for most mid-sized and large businesses features leaders in the Gartner Magic Quadrant and Forrester Wave for Data Management Solutions for Analytics.
- Automated infrastructure provisioning.
- SQL data querying (including big data).
- Native integrations with the AWS ecosystem, including S3, Amazon EMR, AWS Glue, Amazon SageMaker, Amazon QuickSight.
- Federated query support.
- Automated backups and cluster health monitoring.
- Result caching.
- Separate storage and compute scaling.
- Data encryption in transit and at rest.
- Row- and column-level security.
- On-demand pricing: $0.25/hour (dc2.large) - $13.04/hour (ra3.16xlarge).
- Reserved instance pricing can save up to 75% over the on-demand option (in a 3-year term).
- Data storage (RA3 node types): $0.024/GB/month.
- Unified workspace for building analytics solutions.
SQL querying of relational and non-relational data.
Multilanguage support (T-SQL, Python, Scala, Spark SQL or .Net).
Native integrations with Apache Spark, Power BI, Azure ML, Azure Stream Analytics, Azure Cosmos DB, etc.
Automated restore points and backups.
End-to-end data encryption.
Dynamic data masking.
Granular access control.
- Compute on-demand pricing: $1.20/hour (DW100c) - $360/hour (DW30000c).
- Compute reserved instance pricing can save up to 65% over the on-demand option (in a 3-year term).
Data storage: $122.88/TB/month.
- Deployment in the Oracle public cloud (shared/dedicated infrastructure) or in the customer’s data center.
Automated scaling, performance tuning, patching and upgrades, backups and recovery.
Querying across structured, semi-structured, unstructured data types.
Connection with custom applications and third-party products via SQL*Net, JDBC, ODBC.
Native integration with Oracle Analytics Desktop.
Connectivity to Oracle Cloud Infrastructure Object Storage, Azure Blob Storage, Amazon S3.
Graph and spatial analytics.
Independent storage and compute scaling.
Data encryption at rest and in transit.
- Compute: $1.3441/CPU/hour.
Data storage: $118.40/TB/month (in the public cloud).
- Public cloud (AWS, Azure, Google Cloud), multi-cloud, on-premises (Teradata IntelliFlex, VMware) deployment.
All data types (structured, semi-structured, unstructured.)
Support for SQL, R, Python.Integration with Amazon S3, Azure Blob Storage, Hadoop, etc.
Pre-built processing engines (Advanced SQL Engine, ML Engine, Graph Engine).
User authentication and authorization.
Advanced SQL engine – $5/vantage unit (Amazon EC2 instance/Azure VM).
Storage: primary - $0.291/TB (Amazon EBS/Azure Premium), backup - $0.044/TB (Amazon S3) and $0.045/TB (Azure Blob Storage).
- SQL-querying of any data type (structured, semi-structured, unstructured).
Integration with SAP and Non-SAP applications and data sources.
Simplified data modeling and administration.
Classifying data depending on the cost and performance requirements for it.
Built-in predictive analytics and text analysis capabilities.
Prices are available by direct request to SAP.
Data warehouse implementation
With a 15-year experience in delivering data warehouse solutions, ScienceSoft helps you establish flexible data storage on a fitting platform, populate it with data from your internal and external sources, set up ETL processes, and integrate your DWH into a comprehensive analytics system.
Data warehouse consulting
- Analyzing your data storage needs and eliciting requirements for a future DWH solution.
- Designing a DWH implementation/migration strategy.
- Outlining the optimal set of tools and the technology stack making up a DWH.
- Advising on data integration and data quality procedures.
- Conducting DWH admin training.
Data warehouse implementation
- Data storage needs analysis and DWH solution architecture design.
- Data modeling.
- ETL/ELT setup.
- DWH platform integration into the existing data environment (a data lake, big data platform, BI tools, etc.).
- Setting up data- and metadata management procedures
- Data cleaning and data migration.
- Admin & user training.
- DWH support and evolution (if required).
ScienceSoft is a global IT consulting and IT service provider headquartered in McKinney, TX, US. Since 2005, we offer a full range of data warehousing services to help companies select suitable DWH technologies, integrate them into the existing data environment, and support analytics workflows.