20 Best Data Warehouse Software Of 2024
10 Best Data Warehouse Software Shortlist
Here's my pick of the 10 best software from the 20 tools reviewed.
Our one-on-one guidance will help you find the perfect fit.
Disparate data can result in inconsistent and even conflicting information, which can lead to poor decision-making. So how can you centralize your data to facilitate timely business intelligence (BI) and insightful reporting? The right data warehouse software solution can help.
Below, I’ve put together a list of the top data warehouse software solutions. I’ve included an explanation of why I chose them as well as a summary of their key features to help you make an informed decision.
What Is Data Warehouse Software?
A data warehouse is a central repository used for data storage. It collects and aggregates data from sources like databases, transactional systems, and applications.
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Data warehouse software provides tools for extracting, transforming, and loading (ETL) data from disparate sources, as well as for managing and analyzing stored data. This enables companies to perform data analysis and identify trends that can inform decisions.
Best Data Warehouse Software Summary
| Tools | Price | |
|---|---|---|
| Amazon Redshift | From $0.25/hour, which roughly translates to about $180/user/month (assuming continuous usage). This price may vary depending on specific storage and query needs. | Website |
| IBM Db2 Warehouse | From $99/month | Website |
| ClicData | From $75/month | Website |
| Informatica | Pricing upon request | Website |
| Oracle Autonomous Data Warehouse | From $0.335/ECPU/hour | Website |
| Snowflake | Pricing upon request | Website |
| SAP Datasphere | From $12.84/Capacity Unit (CU) | Website |
| Integrate.io | From $1500/month | Website |
| Fivetran | Pricing upon request | Website |
| Google BigQuery | From $5.00 per Terabyte | Website |
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Compare SoftwareBest Data Warehouse Software Reviews
Amazon Redshift is a fully managed, cloud-based data warehouse solution that allows you to analyze structured and semi-structured data at scale.
Why I picked Amazon Redshift: I put Amazon Redshift on this list because it can analyze enormous amounts of data. It combines columnar data storage with Massively Parallel Processing (MPP) technology, which distributes tasks across many nodes.
Amazon Redshift Standout Features and Integrations:
Features that differentiate Amazon Redshift include its zero-ETL approach, which allows for data querying in near real time across various sources. This means you don’t have to build or maintain ETL data pipelines. Concurrency Scaling is another great feature, which automatically adds new clusters to support thousands of concurrent users and queries.
Integrations are available natively with other AWS services like Amazon S3, Amazon DynamoDB, and AWS Glue. You can also query data from over 3,500 third-party data sets in the data marketplace.
Pros and cons
Pros:
- Flexible pricing based on usage
- Offers built-in machine learning (ML) capabilities using SQL
- Built to handle massive amounts of data with relative ease
Cons:
- Moving data in and out may require additional processes
- Some users report a lack of detailed documentation
IBM Db2 Warehouse is a scalable data warehouse designed for advanced, real-time analytics. It allows you to store and analyze data across different sources.
Why I picked IBM Db2 Warehouse: I picked IBM Db2 Warehouse because it offers a robust architecture that can easily scale analytics workloads to meet fluctuating demand. With its parallel query engine and caching technology, you can expect fast performance and lower storage costs.
IBM Db2 Warehouse Standout Features and Integrations:
Features that make IBM Db2 Warehouse stand out from its competitors include its integration with watsonx.data — a data store that uses AI to optimize workloads and reduce data warehouse costs. I also liked that IBM Db2 Warehouse integrates with business intelligence tools like Tableau, which made it easy for me to build all kinds of reports.
Integrations are available natively for various IBM products, including InfoSphere Data Replication, Segment, and Data Studio. Integrations are also available for BI tools like Microsoft PowerBI and Google Looker, as well as ETL tools like DataStage and Informatica.
Pros and cons
Pros:
- Supports a range of data types and sources
- Integrates with popular data science and analytics tools
- Offers flexible on-premise, cloud, or hybrid deployments
Cons:
- Its steep learning curve means that some training is required
- Can be complex to set up, especially for small businesses
ClicData is a cloud-based data management platform that allows businesses to centralize their data and generate interactive data visualizations.
Why I picked ClicData: ClicData deserves a spot here because it offers powerful data visualization features. It includes over 100 dashboards and reports for a range of use cases, from marketing and finance to sales and project management. You can also choose from over 70 widgets and customize your dashboards to display the exact information you need.
ClicData Standout Features and Integrations:
Features that stood out to me about ClicData include its data management functionalities. You can use its native connectors or data loaders to import structured and unstructured data into one central place. I also found the drag-and-drop Data Flow module fairly straightforward to use for data cleansing and preparations.
Integrations include over 250 pre-built data connectors to services like AWS, Basecamp, Confluence, Salesforce, HubSpot, Google Analytics, MongoDB, and Oracle.
Pros and cons
Pros:
- Receives frequent product updates
- Offers iOS and Android mobile apps
- Includes over 100 dashboards and reports
Cons:
- Doesn’t offer native connectors to some popular services like Stripe
- Responsive and knowledgeable customer support team
Informatica is a data integration tool that uses an ETL architecture to ingest data from different sources and consolidate it into a centralized location.
Why I picked Informatica: I put Informatica on this list for its data integration capabilities, which allow you to ingest data using hundreds of pre-built data connectors. The platform also includes APIs that you can use to integrate on-premise and cloud applications without coding.
Informatica Standout Features and Integrations:
Features that make Informatica a good data integration tool include its advanced data cleansing and transformation capabilities. These features help maintain the integrity and consistency of your data sets. I found the operational dashboard particularly helpful, as it helped me monitor project utilization and potential performance issues in one location.
Integrations are available through pre-built data connectors to services like AWS, DataSift, Google BigQuery, JD Edwards, Microsoft Azure, MongoDB, Qlik, and Salesforce.
Pricing: Pricing available upon request
Pros and cons
Pros:
- Has an intuitive and user-friendly interface
- Includes an option to transform data using SQL or Python
- Offers an extensive range of pre-built data connectors
Cons:
- Some users report slow performance with the web app
- Initial setup requires a high degree of technical expertise
Oracle Autonomous Data Warehouse is a cloud-based data warehouse platform built for demanding analytic workloads. It allows you to bring in your data from any source, no matter where they reside.
Why I picked Oracle Autonomous Data Warehouse: I chose Oracle Autonomous Data Warehouse because it automates many of the routine tasks associated with data warehousing, like provisioning, configuring, and scaling. It can also automatically “tune” itself using ML algorithms to boost performance.
Oracle Autonomous Data Warehouse Standout Features and Integrations:
Features that impressed me during my time with Oracle Autonomous Data Warehouse include its built-in Data Studio tool. While the self-service analytics tool has an initial learning curve, I was able to use it to generate insights and share the results with my team.
Integrations are available natively with other Oracle services, including Oracle GoldenGate, Oracle Analytics Cloud, and Oracle Data Integrator. Other native options include Alteryx, Domo, Looker, Power BI, Nexla, and Tableau.
Pros and cons
Pros:
- Offers flexible deployment options
- Includes security features like always-on encryption and granular access controls
- Uses a powerful SQL processing engine for better performance
Cons:
- Requires some technical expertise to set up properly
- Not as many customization options as other data warehouse solutions
Snowflake is a scalable data warehousing solution that supports structured and semi-structured data. It offers features like automatic query caching, policy-based access controls, and native integrations with popular BI tools like Qlik.
Why I picked Snowflake: I chose Snowflake because it’s one of the few data warehousing solutions that use a multi-cluster architecture. It’s built on top of AWS, GCP, and Microsoft Azure, which means it can scale on-demand to meet sudden increases in data loads.
Snowflake Standout Features and Integrations:
Features that differentiate Snowflake from other data warehouse solutions include the option to unify analytical and transactional data in one platform with Unistore. This allows you to centralize your data without having to maintain separate systems for both types. I also like that Snowflake includes data protections out of the box, like encrypting data in transit and at rest.
Integrations are available natively for various platforms, including Ab Initio, Boomi, Datameer, Denodo, Fivetran, Hevo Data, Informatica, Sisense, and Tableau.
Pros and cons
Pros:
- Offers automatic scaling to meet changing demands
- Supports a variety of data sources, including SQL and NoSQL databases
- Uses a multi-cluster architecture to ensure high availability
Cons:
- Security features can be difficult to set up and manage
- May be challenging to integrate open-source tools
SAP Datasphere, the next iteration of SAP Data Warehouse Cloud, is a data warehousing solution that allows organizations to access their data across all cloud environments.
Why I picked SAP Datasphere: I picked SAP Datasphere for its intuitive self-service analytics tools that allow non-technical users to perform data analysis. The Data Builder tool made it easy to create and apply an analytic model to existing data sets for new insights. There’s no coding required with the drag-and-drop graphical interface.
SAP Datasphere Standout Features and Integrations:
Features that make SAP Datasphere a top data warehousing solution include its ability to prepare and visualize data across on-premise and multi-cloud environments. This helps facilitate data access across the entire organization. SAP Datasphere also has data governance capabilities to ensure the accuracy and consistency of your data.
Integrations include native options for a range of platforms, such as Collibra, Confluent, Databricks, DataRobot, and GCP.
Pros and cons
Pros:
- Self-service analytical tools allow non-technical users to analyze insights
- Built-in security features help ensure compliance with regulatory requirements
- Allows you to visualize data from on-premise and cloud sources
Cons:
- May be too costly for small businesses
- No mobile applications for iOS or Android devices
Integrate.io is a cloud-based data integration and ETL solution that provides businesses with a centralized platform to unify their data from various sources.
Why I picked Integrate.io: I put Integrate.io on this list because it offers a simple way to connect and manage data sources. In addition to offering pre-built connectors to popular platforms and services, Integrate.io also includes a drag-and-drop interface to build ETL pipelines without writing any code.
Integrate.io Standout Features and Integrations:
Features that make Integrate.io worth considering include its ability to automate data pipelines and instantly scale to millions of rows per second as needed. It also includes free data observability with every plan, so you can receive instant alerts about any issues.
Integrations are available through pre-built data connectors to sources like Amazon Redshift, Snowflake, NetSuite, HubSpot, Klaviyo, Google BigQuery, MariaDB, and GitLab.
Pros and cons
Pros:
- Pre-built data connectors eliminate the need for manual coding
- Offers extensive documentation and 24/7 customer support
- Provides a drag-and-drop interface to build data pipelines
Cons:
- Cost of the software may be high for businesses with limited budgets
- Some users report performance issues when working with a lot of data
Fivetran is a data integration platform that allows businesses to move and replicate data from disparate sources into a centralized location like a data warehouse.
Why I picked Fivetran: I picked Fivetran because it offers a range of pre-built data connectors that connect to a wide variety of sources. Whatever tool your company uses, Fivetran likely has a connector for it. These connectors require minimal configuration, which cuts down on development time.
Fivetran Standout Features and Integrations:
Features that impressed me during my testing with Fivetran include its quick start data models that allow you to transform data in destinations like Snowflake and Redshift. This means you can quickly turn analytics-ready datasets into business insights. I also like that Fivetran offers data governance features, like access control and user provisioning.
Integrations include over 300 pre-built data connectors to platforms and services like Amazon S3, Marketo, HubSpot, MySQL, Oracle, SAP ERP, Salesforce, and Zendesk. It also integrates with data warehouses like Azure Synapse, Google BigQuery, and Snowflake.
Pros and cons
Pros:
- Integrates with popular data warehouses like Amazon Redshift
- Offers reliable data syncing (99.9% uptime across a million daily syncs)
- Has built-in data governance and security features like single sign-on (SSO)
Cons:
- Some users report slow customer support response times
- Can be expensive for small to medium-sized businesses
Google BigQuery is a scalable enterprise data warehouse that lets you analyze data across multiple cloud environments. Its built-in AI and ML capabilities enable near real-time analytics.
Why I picked Google BigQuery: Working with data and querying workloads isn’t easy. I chose Google BigQuery as one of the top data warehouse solutions for its ease of use. It features an intuitive interface that even new users of the platform can navigate. The system also lets you use familiar SQL syntax to analyze and query your data.
Google BigQuery Standout Features and Integrations:
Features that impressed me about Google BigQuery include its built-in ML tool called BigQuery ML, which allows you to create and run ML models using SQL. You don’t need knowledge of specialized frameworks to start leveraging ML. I also like that you can query structured, semi-structured, and unstructured within the platform.
Integrations are pre-built and available for various platforms, including Confluent, Informatica, Tableau, Collibra, ZappySys, Databricks, Dynatrace, and New Relic.
Pros and cons
Pros:
- Integrates natively with Google Cloud Platform (GCP)
- Lets you use SQL to analyze your data where it resides
- Can easily scale up or down as needed
Cons:
- Can suffer from high latency when querying large datasets
- Can be costly for large datasets and frequent queries
Other Data Warehouse Software Options
Here are some more data warehouse tools that didn’t make my top list but I think are still worth checking out:
- VantageCloud
Best for deploying AI initiatives
- Microsoft Azure Synapse Analytics
Best for building code-free data pipelines
- Panoply
Best for end-to-end data management
- Qlik
Best for data warehouse automation
- Cloudera
Best for unified data security and governance
- QuerySurge
Best for data validation and ETL testing
- Talend Open Studio
Best open-source ETL tool
- Tableau Data Management
Best for streamlining data preparation tasks
- Pentaho
Best for data flow orchestration
- Vertica
Best for big data analytics
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Selection Criteria For Data Warehouse Software
Wondering how I came up with my list? Here’s a summary of the evaluation criteria I used to compile the best data warehouse software:
Core Functionalities
A data warehouse solution had to have the following core functionalities to make it on my list:
- Allows you to store different types of data across on-premises and cloud environments
- Automatically scales analytics workloads based on demand
- Enables you to integrate with popular BI tools like Power BI and Tableau
- Includes data connectors to popular platforms
- Provides self-service analytics capabilities
Key Features
I prioritized data warehouse solutions with the following key features:
- Data ingestion: Data resides in more places than ever. I looked for data warehouse solutions that are capable of ingesting and handling large volumes of data.
- Data querying: Data is only valuable if you can make use of it. Throughout my testing, I gave more weight to platforms with robust features for querying and analyzing data.
- Data connectors: These allow you to integrate data from different sources. Tools that offered a broad range of pre-built data connectors were more likely to be included here.
- Data security: I prioritized tools with advanced security features like always-on encryption and access controls to ensure data privacy.
Usability
Usability was another key consideration when I put together this list. While any platform will have an initial learning curve, you also don’t want your team spending a lot of time with a new tool and have little to show for it.
I was more likely to prioritize data warehouse software with intuitive interfaces and extensive documentation. Solutions with drag-and-drop functionality and customizable dashboards also earned bonus points.
People Also Ask
Here are some answers to frequently asked questions about data warehouses:
What’s the difference between a database and a data warehouse?
A database is a computer system that stores data in an organized manner. It’s designed to support transactional processing, which can include adding, updating, and deleting records for an application. A data warehouse is a system that extracts data from different sources into a central repository. It’s designed to support analytical processing for business insights.
What’s the difference between a data warehouse and a data lake?
A data warehouse is a repository that can hold structured and semi-structured data from disparate sources. Businesses typically use them to gain insights into their data. In contrast, a data lake can store a massive volume of data, including structured, semi-structured, and unstructured data. The downside is that storing data in its raw format without processing it can result in poor data quality.
Is a data warehouse an ETL tool?
No, a data warehouse isn’t an ETL tool. Extract, transform, load (ETL) tools are designed to gather data from different sources, transform it into a usable format, and load it into a destination like a data warehouse for analysis.
Conclusion
It’s not easy to make sense of your data when it’s siloed across disparate sources. But with the right data warehouse software, you can centralize your data and make it easier to analyze. Use this list to find a solution that meets your needs.
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