Data warehouse automation is the process of automating each step of the data warehouse lifecycle by reducing human code development and automating the data warehouse's repetitive, labor-intensive, and time-consuming processes. These responsibilities include preliminary analysis, design, and modeling. Data warehouse automation, in essence, enables organizations to swiftly collect, clean, and prepare data for analysis without having programmers write any code.

In the list of the top tools, we have mentioned the top 25 Data Warehouse Automation tools along with their features and pricing for you to choose from.

1. Green Plum

Green Plum is open-source analytics, AI, and machine learning platform with a massively parallel architecture. Greenplum's data analytics include data processing, textual information, graph data, time-series data, and geospatial data. Some of the computer languages supported are Java, Perl, Python, pgSQL, and R.

Key Features:

• Scale interactive and batch analytics to petabyte-scale datasets without sacrificing query performance or throughput.

• Greater software control, less vendor lock-in, and more open input into product direction

• By merging analytic and operational functions, such as streaming ingestion, in a single, scale-out environment, data silos are reduced.

Cost:

This program is completely free.

2. Astera DW Builder

Astera DW Builder is an end-to-end data warehousing tool that employs a metadata-driven approach to design, create, and deploy high-volume data warehouses. A full data model designer and extensive ETL/ELT capabilities are included in the solution, making it easy to construct a data warehouse on-premises or in the cloud.

Key Features:

• Built-in connections for SQL Server, Oracle, SAP HANA, and other popular on-premises databases, as well as cloud services like Azure Cloud and Amazon.

• Use multiple data modeling techniques, such as star schema, 3NF, and data vault, to create your warehouse schema.

• A data model verification module may be used to test and debug your data warehouse prior to deployment.

• To allow real-time querying, publish your data model using the OData protocol.

• Job planning and workflow automation can help you automate ETL procedures.

Cost:

Request the sales team for a quote

3. Oracle

Oracle data warehouse system is a group of data that is considered as a single entity. This database's aim is to store and retrieve connected information. It enables the server to manage large volumes of data reliably, allowing several people to access the very same data.

Key Features:

• Distributes data across drives in the same way to provide consistent performance.

• Single-instance and real-world application clusters are supported.

• Provides real-world application testing

• Any Private Cloud and Oracle's Public Cloud share the same architecture.

• Large data requires a high-speed connection.

• Compatible with both UNIX/Linux and Windows systems.

• It has virtualization support.

• Connecting to a remote database, table, or view is possible.

Cost:

Request a quote from the sales team.

4. Xplenty

Xplenty is a data warehousing platform that connects several data sources to SQL and NoSQL databases, as well as cloud storage. With a single mouse click, Xplenty allows users to combine and manage a variety of data. It will be valuable to everyone who demands a single platform for data integration.

Key Features:

• A simple, intuitive, and easy-to-use drag-and-drop, no-code, or low-code GUI.

• Connect to more than 140 data sources, including databases, data warehouses, and cloud-based SaaS applications.

• Cutting-edge security, encryption, and data compliance features.

• Your data is extracted and converted in real-time, at scale, and is ready for analysis.

Cost:

Request a quote from the sales team.

5. IBM Db2 Warehouse

IBM Db2 Warehouse is a cloud-based data warehouse that enables self-scaling data storage and processing. IBM Db2 is a data management program that contains the Db2 relational database. Its goal is to store, analyze, and retrieve data quickly.

Key Features:

• Client-handled containers are clustered together for better mobility across platforms.

• Scaling is automated, and deployment is rapid and flexible.

• You'll benefit from scalability flexibility and the convenience of updating and upgrading your system.

• With Spark and R open-source, predictive modeling techniques are integrated directly into the database, making enterprise AI quicker and more efficient.

• With only a few clicks, you can transform unstructured data sources into a structured format for analysis.

Cost:

Request a quote from the sales team.

6. SAP

SAP is an integrated data management platform that maps all of an organization's business operations. It's an open client/server application package for enterprise systems. It's one of the top data warehouse tools, and it's established new benchmarks for giving the best commercial data management solutions.

Key Features:

• It offers extremely adaptable and transparent business solutions.

• SAP-based applications may be integrated with any system.

• It has a modular design for quick setup and space efficiency.

• You may build a database system that incorporates both analytics and transactions. These next-generation databases may be used on any platform.

• Assist with on-premise and cloud deployments.

• Data warehouse architecture simplified

• SAP and non-SAP application integration

Cost:

Request the sales team for a quote

7. Netezza

IBM's Netezza is a data warehousing platform. It develops and distributes high-performance data warehouse equipment, as well as innovative analytics for a wide range of data warehouses. It's a flexible and dependable platform, too, with a packaged architecture that combines Netezza core software and analytics within the IBM CloudPak data system.

Key Features:

• Failure detection and recovery as soon as possible.

• A single command line can be used to update existing systems.

• The capacity to query many systems at once.

Cost:

Request a Quote from the sales team

8. Azure Synapse Analytics

Data integration, big data analytics, and enterprise data warehousing are all combined in Azure Synapse Analytics. It uses machine learning technology to create apps that extract valuable information from any data. It accelerates project development by providing an end-to-end analytics solution.

Key Features:

• Materialized views and a result set cache.

• For Power BI, Azure Synapse Analytics is a performance accelerator.

• Azure Cognitive Services connection provides no code streaming analytics for a data warehouse.

• A controlled virtual network with private endpoints.

• At the column and row levels, there is security.

Cost:

Request for a quote from the sales team

9. MariaDB

MariaDB is an open-source database created by the original MySQL developers. MariaDB has a good array of storage engines, as well as upgraded storage engines, for working with many RDBMS data sources.

Key Features:

• It works with multiple operating systems as well as programming languages.

• MariaDB's Memory Storage engine will execute any data operating statement quicker than MySQL's standard storage engine.

• There are several commands available, as well as NoSQL-friendly interfaces.

Cost:

This program is completely free.

10. Panoply

Panoply is a cloud data platform that enables users to sync, save, and retrieve data from any location. To allow end-to-end data management, it automates all data preparation activities. It eliminates the need for coding and programming, reducing the time it takes to connect, manage, and transform data.

Key Features:

• Data connections that don't need to be coded and don't need to be maintained.

• You have total control over the tables you save for any data source.

• Table-level user privileges provide fine-grained control.

Cost:

The first package costs $399.

11. Redshift by Amazon

Amazon Redshift is a Data Warehousing platform that lets you explore data using current Business Intelligence tools and simple SQL queries. To conduct advanced analytical queries, it employs techniques such as high-performance computing, parallel execution, uniform query optimization, and columnar storage.

Key Features:

• After executing high-performance searches on petabytes of semi-structured and structured data, you can create attractive reports and dashboards.

• You can combine structured data from your data warehouse with semi-structured data from your S3 data lake, including application logs, to get real-time operational insights.

• Share data both inside and outside your company for safe and controlled collaboration on live data.

Cost:

The hourly rates begin at $0.25.

12. Google BigQuery

Google BigQuery is a serverless data warehousing system that runs on the cloud. It stores large amounts of data and connects with the database using SQL, or Structured Query Language. It extracts information from enormous amounts of data efficiently. It enables automated data transmission as well as complete access to the data recorded.

Key Features:

• Export ML models to Vertex AI or your own serving layer for online prediction.

• You may quickly answer queries and communicate insights across all of your datasets using traditional SQL and a familiar UI.

• With sub-second query response speeds and great concurrency, users may interactively explore big and complicated datasets.

Cost:

Request a quote from the sales team.

13. Snowflake

Snowflake is an analytical data warehouse that provides a framework that is quicker, easier to use, and customizable than a standard data warehouse. Snowflake has a comprehensive SaaS (Software as a Service) architecture since it is entirely cloud-based.

Key Features:

• Discover and securely share live, regulated data with customers and partners throughout your company.

• Unify your data warehouses, data lakes, and other separated data to comply with data protection requirements like GDPR and CCPA.

• Develop new income streams based on data to help your company grow.

Cost:

For beginners, there is a free package.

14. Micro Focus Vertica

Micro Focus Vertica is a big data analytics platform designed for data warehouses and other big data applications that need speed, scalability, simplicity, and openness. It's a self-monitored MPP database that's unlike anything else on the market in terms of scalability and versatility.

Key Features:

• It offers a wide variety of Machine Learning algorithms for classification, overfitting, and prediction to boost processing speed.

• New data-preparation functions are now supported, allowing you to get more out of your data while also improving the quality of your research.

• A simplified end-to-end strategy makes it easier to deploy Machine models in production.

Cost:

It offers a free trial version.

15. PostGRESQL

PostgreSQL is a prominent open-source data warehousing solution that stores, integrates, and analyses data using its built-in capabilities and analytics tools. Procedures and functions can be written in a variety of languages. (e.g., PL/python, pgSQL) It's a data warehousing solution that's low-cost, simple to use, and effective.

Key Features:

• Without needing to rebuild your database, you may create unique data types, functions, and even code in multiple programming languages.

• Many of the elements of the SQL standard are supported, although with somewhat modified syntax or behavior in some circumstances.

• PostgreSQL is tremendously extensible: many features, like indexes, have published APIs that you may use to modify PostgreSQL to address your issues.

Cost:

This program is completely free.

16. Teradata

Teradata is a subscription-based corporate software platform for database analytics. It allows for the unification of different types of data as well as the construction of a hybrid multi-cloud platform. This implies that deployments may take place both on-premises and on public clouds such as AWS, Azure, and Google Cloud.

Key Features:

• A 360-degree perspective of your complete business, which is combined from all data sources, provides richer insights.

• You may achieve the performance of in-memory databases without the expense by automatically storing the most frequently used data in memory.

• Mission-critical availability and performance.

Cost:

Request a Quote from their sales team

17. SAS Cloud

SAS Cloud is a statistical tool for data management, advanced analytics, business intelligence, predictive analysis, and multivariate analysis. SAS data warehouse allows users to store and process enormous volumes of data in a manner that can be understood. SAS-managed data enables users to access data from any location in the world without difficulty.

Key Features:

• You just create an account, log in, and begin working on your analytic issues. There is no need for installation.

• Create and manage cloud services and solutions on SAS's cloud, your cloud, or your own servers.

• SAS Results does not need the acquisition of a software license or infrastructure.

Cost:

Request a quote from their sales team

18. MarkLogic

The MarkLogic Data Hub Service links and curates your organization's data to provide instant business advantages. It's multi-model, elastic, transactional, secure, and cloud-ready, with a NoSQL database for speed and scalability.

Key Features:

• Data integration is adaptable, allowing you to load data from any source in its current state and perform real-time data discovery.

• Use smart and automated capabilities to improve, harmonize, and master data more easily and rapidly.

• There's no need to wait for ETL to complete before accessing data services; developers may use data services right now. It's agile DataOps for agile development.

Cost:

It offers a free trial.

19. Cloudera

Cloudera is the first enterprise data cloud or multi-functional analytics platform to break down silos and expedite the generation of data-driven insights in the industry. It adds uniform security, governance, and metadata to shared data instances.

Key Features:

• Without engaging the IT department, quickly change data, generate new reports and tasks, and access interactive dashboards.

• Eliminate the inefficiencies of data silos by combining data marts into a Climbable analytics platform to meet company goals.

• Construct and implement AI solutions at scale while staying inside a budget.

Cost:

Offers a free trial

20. Informatica

Informatica is a data integration and management system developed by Informatica Corporation for gaining business insights. The repository saves the metadata information. Metadata information is the information contained in the destination systems, source systems, and transformations.

Key Features:

• With ease, create, implement, and manage complicated APIs. Any application can connect and combine your data.

• Deliver dependable, managed data to empower your analytics, enhance customer experience, and speed cloud modernization.

• You can ingest, integrate, and cleanse your data with the market-leading, cloud-native ETL and ELT solution from the ETL pioneer.

Cost:

It is free

21. MongoDB

MongoDB's document data format comes with JSON compatibility by default, and its powerful query language is easy to learn and use. Built-in features include automatic failover, horizontal scaling, and the ability to allocate data to a specific location.

Key Features:

• Auto-scaling performance improvement with actionable advice tailored to your specific workloads.

• You may simplify your data architecture by using a powerful query API that supports operational, transactional, full-text search, and real-time analytics workloads.

• Provides mission-critical database administration as well as strong data security and privacy measures.

Cost:

Monthly subscriptions start at $57.

22. Domo

Domo is a cloud-based data warehouse management platform that lets you effortlessly combine spreadsheets, databases, social media, and practically any cloud-based or on-premise data warehouse solution.

Key Features:

• Assists you in creating your ideal dashboard.

• Stay connected wherever you go.

• All existing company data is integrated.

• It assists you in gaining actual insights into your company data.

• All of your existing company data is connected.

• A platform for easy communication and communications

• It allows you to do SQL queries on the fly.

Cost:

Offers a free trial.

23. Amazon S3

Amazon S3 is an object storage service that allows you to store and retrieve unlimited amounts of data from anywhere. It's a low-cost storage system that offers industry-leading durability, accessibility, performance, security, and almost unlimited scalability.

Key Features:

• Scale your storage resources up and down to fit fluctuating demands without previous investments or resource procurement procedures.

• You may save money without losing speed by storing data across the S3 Storage Classes.

• You may store your data on Amazon S3 and secure it from unauthorized access using encryption and access management technologies.

Cost:

Request a quote from their Sales team.

24. Oracle Autonomous Data Warehouse

Oracle Autonomous Data Warehouse is a cloud-based data warehousing system that makes the process of creating a data warehouse, data security, and data-driven application development easier.

Key Features:

• Among the operations that may be automated are backup, setup, and patching.

• A complete solution built on a converged database that supports multimodal data and different workloads.

• Encrypts data in transit and at rest, safeguards regulated data, applies all security patches, and identifies threats automatically.

Cost:

Request a quote from their sales team.

25. Numetric

Numetric is a quick and simple business intelligence tool. From data consolidation and cleansing through analysis and publication, it provides business intelligence solutions. It is capable of being used by anyone. This data warehousing solution aids in the measurement and enhancement of productivity.

Key Features:

• Numbers and codes are transformed into the forms of words that people naturally use to look for information on the site. The result is a search experience that is surprisingly simple and straightforward.

• When your data is consolidated in its Traffic Safety Analytics Platform, you enable your team the ability to view, interpret, and act on all of your data quickly and efficiently to optimise your safety ROI.

• Data consistency is embedded into the centre of their technology, running QA/QC procedures, recognising any data conflicts, and offering remedies.

Cost:

Request the sales team for a quote.

Things to keep in mind while choosing Data Warehouse Automation tools

Can keep all of your data in one Data Warehouse with Full Management

Having your data saved in many locations might cause problems. You will not get the benefits of data ownership without a central database. The platforms you use, whether marketing or sales platforms or other services, should own and manage it, as well as restrict what you can access and do with it, whether it's live data or historical data.

Can create engaging data narratives

Data quality, which comes in a variety of forms, is one of the obstacles to gathering data from several sources into one location. That data must be standardized, processed, and converted before it can be used to generate useful insights. This means that different aspects of the data may need to be modified or structured in order to be used in visualizations.

Compatibility

Finally, the platform you choose must be compatible with your current system otherwise there is no point in using an automation tool. Before choosing a Data Warehouse Tool always check carefully if it can work efficiently with your existing system.

Conclusion

Data warehousing technologies are critical for enterprises. We've seen some of the top examples of data warehouse automation solutions in this article. The quantity of the data supplied and the number of queries conducted to manage and monitor data determines the efficient data warehouse technology. Similarly, based on the corporate data and queries you wish to conduct, you might pick your own.

FAQS

What is the definition of a data warehouse?

A data warehouse is a database that stores vast volumes of different data. Every department contributes data to a data warehouse.

What are the Benefits of Data Warehouses?

Businesses use data warehousing technologies for the following purposes:

• To learn about strategic and operational issues

• It is possible to speed up decision-making and assistance methods.

• Examine and evaluate marketing efforts' efficacy.

• Examine your workers' performance.

What are the functions of Data Warehouse Tools?

Extract, convert, and load are the three steps of data warehousing (ETL). This approach extracts relevant data from the source system. After extraction, the data quality is repaired and adjusted to ensure that it is suitable for usage in a corporate data warehouse. Finally, the data has been loaded and is ready for monitoring, analysis, and evaluation in order to enhance and assess the product.

In data warehousing, what are ETL tools?

ETL stands for Extract, Transform, and Load, and it is a Data Warehouse procedure. An ETL tool collects data from numerous data source systems, transforms it in the staging area, and then loads it into the Data Warehouse system.

What is Data Mart?

A data mart is a subset of a data warehouse that is specifically tailored to meet the needs of a particular department or business function. Data Marts are often built and controlled by a single department within an enterprise.