Best Data Warehouse Automation Tools
Data warehouse automation software streamlines and automates the entire data warehouse lifecycle. This includes automating all the key processes that are a part of data warehouse software—discovery, provisioning, designing, developing, deploying, and scaling. This software automates data warehouse software processes like data processing, transformation, and data ingestion to help businesses make critical data-driven decisions, but itself does not conduct any of the above-mentioned processes.
Data warehouse automation software differs from traditional ETL tools since the latter are used to transfer data between databases or for external use. ETL tools are primarily used to transform data sets to operationalize via querying and analysis, whereas data warehouse automation software automates all data-related processes from start to finish.
To qualify for inclusion in the Data Warehouse Automation category, a product must:
Best Data Warehouse Automation Software At A Glance
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TimeXtender is a Danish software company founded in 2006, dedicated to simplifying and accelerating data management. With over 3,000 global customers, TimeXtender offers a comprehensive suite of pro
- Business Intelligence Consultant
- Data Analyst
- Information Technology and Services
- Consulting
- 44% Mid-Market
- 34% Small-Business
18,122 Twitter followers
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dbt is a transformation workflow that lets data teams quickly and collaboratively deploy analytics code following software engineering best practices like modularity, portability, CI/CD, and documenta
- Analytics Engineer
- Data Engineer
- Computer Software
- Information Technology and Services
- 59% Mid-Market
- 25% Small-Business
13,116 Twitter followers
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Built to run the world’s mission-critical workloads. Designed by the world’s leading database experts, IBM Db2 empowers developers, enterprise architects, and data engineers to run low-latency tran
- Senior Software Engineer
- Software Engineer
- Information Technology and Services
- Banking
- 67% Enterprise
- 21% Mid-Market
711,096 Twitter followers
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Qlik Sense empowers people to make better data-driven decisions and take action. The solution provides augmented analytics for every business need from visualization and dashboards to natural language
- Consultant
- Data Analyst
- Information Technology and Services
- Computer Software
- 41% Mid-Market
- 41% Enterprise
65,790 Twitter followers
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- 44% Enterprise
- 39% Mid-Market
65,790 Twitter followers
- Overview
- User Satisfaction
- Seller Details
Qlik Gold Client improves the efficiency, cost and security of managing test data in SAP environments. Qlik Gold Client is designed to eliminate development workarounds by easily moving configuration,
- 47% Mid-Market
- 27% Enterprise
65,790 Twitter followers
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ActiveBatch is part of Redwood Software offering the easiest to use job scheduler and an extensible, highly reliable workload automation software. Redwood Software, a 2024 Gartner® Magic Quadrant™ for
- Information Technology and Services
- Financial Services
- 44% Enterprise
- 39% Mid-Market
836 Twitter followers
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- User Satisfaction
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WhereScape RED is a comprehensive data warehouse automation tool designed for developers, focusing on automating development, deployment, and operations of data infrastructure. It streamlines the data
- Financial Services
- Hospital & Health Care
- 52% Enterprise
- 38% Mid-Market
2,896 Twitter followers
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- User Satisfaction
- Seller Details
AnalyticsCreator is the ultimate toolbox for Data Engineers. It provides a fully automated, no-code, and zero-maintenance data pipeline platform, removing the hassle of ELT maintenance and unlocking e
- 57% Small-Business
- 21% Enterprise
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- User Satisfaction
- Seller Details
Xpert BI is a Microsoft SQL server based tool that is designed to automate and improve all aspects of data warehousing and Business Intelligence especially in the areas of extraction- and transformati
- 36% Small-Business
- 36% Mid-Market
12 Twitter followers
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- User Satisfaction
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beVault is a cutting-edge data warehouse automation software designed to accelerate the deployment of your data warehouse or data platform by up to five times using the Data Vault 2.0 methodology. By
- 64% Mid-Market
- 27% Small-Business
- Overview
- User Satisfaction
- Seller Details
biGENIUS automates the entire lifecycle of analytical data management solutions (data warehouses, data lakes, data marts, real-time analytics, etc.) while providing the foundation for turning your da
- 71% Small-Business
- 29% Mid-Market
239 Twitter followers
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- User Satisfaction
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What Is Oracle Autonomous Data Warehouse? Autonomous Data Warehouse is a fully managed database that’s tuned and optimized for data warehouse workloads. It combines the market-leading performance of
- Information Technology and Services
- 53% Mid-Market
- 27% Enterprise
824,405 Twitter followers
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- User Satisfaction
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Zap Data Hub is the fastest way to deliver accurate, trusted financial and operational reporting in BI tools including Tableau and Power BI. We have optimized solutions for Microsoft Dynamics, the Sag
- Oil & Energy
- Computer Software
- 58% Mid-Market
- 31% Enterprise
1,582 Twitter followers
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- User Satisfaction
- Seller Details
The Datavault Builder is a 4th generation Data Warehouse automation tool covering all aspects and phases of a DWH.
- 60% Small-Business
- 40% Mid-Market
66 Twitter followers
Learn More About Data Warehouse Automation Software
What is Data Warehouse Automation (DWA) Software?
Data warehouse automation (DWA) software automates and streamlines every part of the entire data warehouse lifecycle. It helps ensure the automation software automatically manages a data warehouse's numerous tasks—discovery, designing, developing, deploying, provisioning, and scaling.
Automating data warehousing ensures that there is a reduction or a complete elimination of repetitive tasks. Data warehouse software usually provides built-in templates or uses data modeling (patterns to ensure functionality) to automate. Automating these repetitive tasks helps companies develop data-driven strategies and provide data-driven insights and hence jump on the digital transformation bandwagon.
By automating each step of the data warehouse lifecycle, there is much less time required to manage it, thereby providing data engineers with more time on other tasks instead of managing the data warehouse 24/7.
For businesses, data is at the core of decision-making. However, it's not just the data that is important, but the workflow. Specifically, how business users can access the data and the speed to access that data also matters, driving the need for DWA solutions.
Traditional data warehouse architecture has intensive manual code writing for data modeling, design, etc. DWA helps eliminate these steps and allows clean data preparation and integration without requiring engineers to write code.
Data in a data warehouse goes through three stages primarily:
- Extraction, where data is extracted from numerous internal and external data sources (big data sources). SQL scripts/code written by data engineers is used to extract all data from the database. In this step, data preparation (cleansing the data) also occurs.
- Data modeling is done using different schemas, and the data sets are transformed. This data is then loaded into the data warehouse.
- Data can then be exported into analytics or business intelligence (BI) tools to make data-driven decisions.
The extract, transform, and load (ETL) or extract, load, and transform (ELT) process in the first two steps above used to be a manual process, but the introduction of different ETL tools and DWA processes makes the process much more efficient. DWA tools help optimize the ETL/ELT process for real-time data warehousing. The difference between ETL and ELT is that ELT uses the target system to transform the data instead of pre-processing the data like in ETL.
As shared earlier, all the above steps, from extraction to exporting to business intelligence (BI) tools, happen automatically within the DWA software.
What Does DWA Stand For?
DWA stands for dData wWarehouse aAutomation. The main task of this software is automating multiple processes, ensuring the speed and agility of the entire data warehouse lifecycle.
What are the Common Features of Data Warehouse Automation Software?
The following are some core features within DWA solutions that can help users in several ways:
Automation: The key feature of DWA tools is the introduction of automation into a traditionally manual data warehouse process. Automating the numerous steps involved helps reduce manual error and the time for the data to be used by BI tools to drive analytics.
Batch processing and scheduling: DWA tools support businesses to schedule and run any of their data warehousing jobs automatically, reducing any need for manual support. Automating batch processing and scheduling ensures resources are being allocated judiciously.
Consolidation of the data management process: Since DWA ensures that data warehouse processes are automated from start to finish, companies may not require specific ETL tools or even additional BI platforms since the DWA software can offer the same. DWA solutions can exist as a one-stop shop for several data management processes, making it much easier for admins and developers to handle them as it exists in a single platform.
Checkpoint support: Although automation is key here, any automation failure could cause numerous problems. To support this, many DWA tools can add checkpoints throughout the data pipeline process to keep things running smoothly. If at any point the automation fails, only that checkpoint would be paused and corrected without impacting the entire process.
Analytics support: As shared earlier, a key outcome of using DWA tools is providing data-driven business insights. A key feature of any DWA solution is ensuring the user can build analytic models to help achieve fast and accurate business intelligence reporting. Without DWA, it would take weeks, or even months, to deliver insights. And by the time those insights are received, the data would be old, hence not real time and accurate.
Built-in connections: DWA tools also support built-in connections to various on-premises databases or cloud services such as Microsoft Azure, Amazon Web Services (AWS), etc.
What are the Benefits of Data Warehouse Automation (DWA) Software?
Increased productivity and ROI: The key ability of DWA solutions is that it helps businesses deliver projects much faster by consuming fewer resources since the process is fully automated from start to finish. Ensuring the right set of design templates is being used for the process makes the job of a data engineer easier. With less time spent on manual work, faster time to completion for projects, and quicker decision making, companies can expect a much faster ROI.
Increased business agility: It has become essential for businesses to react to market changes at the earliest possible time to ensure business continuity. In this instance, C-level execs and decision makers need the most up-to-date information to make decisions. In traditional data warehouse processes, by the time business decision makers get their hands on the data, it’s not new anymore. By using DWA tools, the ROI can be realized much faster since it shortens the time to get access to analytics reports.
Better data quality: The introduction of automation into the enterprise data warehouse processes helps reduce manual errors. The DWA software takes up the preparation, cleaning of data, and data integration automatically, helping save hours of manual work. This reduction in inconsistencies helps businesses ensure they have quality data when making decisions, thereby driving reliability.
Improved data management processes: Data is being created and consumed at a tremendous pace. This is causing a considerable challenge to the teams that use and manage this data via data warehouses. The challenge here is that the number of data or analytics requests far outnumbers the speed at which data can be processed. DWA tools have alleviated some of this stress by automating the entire process, thereby speeding up the time to evaluate analytics requests.
More free time for developers: Automated enterprise data warehouse processes allow developers to get more time back in their day, and their expertise could be utilized elsewhere. Without automation, developers must spend hours writing long lines of code for any data warehouse project. Developers can spend more time on other critical projects, and simultaneously other teams can access the data for business intelligence in a much shorter time. Operations become much more self serve and lean.
Standardization and Compliance: Privacy and security are vital to every business, and companies need to meet these critical business requirements. Since DWA solutions also help in documentation, this feature ensures companies remain transparent and compliant since the data is being documented at every step. Privacy teams can use this documentation and aligned methodologies to ensure how data flows internally and externally for a company and raise any concerns if observed.
Deployment type: Several enterprise DWA can be deployed on-premises, in the cloud, or even take a hybrid approach.
Who Uses Data Warehouse Automation Software?
The following roles use DWA tools:
Data warehouse developers: Data warehouse developers are a key persona that can use DWA to increase and improve productivity. Without a DWA tool, these developers spend hours writing lines of code for a project which could take months to complete. With the introduction of DWA solutions, developers have more time and control over the process and can focus on critical tasks.
Data engineers: Data engineers are another important persona to use DWA software. They would be in charge of not only using the software but also ensuring the software works as intended to achieve overall business goals. They ensure the platform can be accessed by those who need it, and also, in case of any breakdown in the process, they can quickly step in and resolve the issues.
BI analysts: BI needs reliable data. With DWA tools, a BI analyst would have access to clean, prepared, and processed data to help them make the best decision possible. BI analysts can also use DWA tools to move enterprise warehouse data into other systems, such as data visualization tools, cloud-based BI tools, etc.
Privacy analysts: With DWA tools, privacy personas in companies can help keep track of the company meeting different compliances and standards such as GDPR, HIPAA, etc.
Challenges with Data Warehouse Automation Software
DWA solutions can come with their own set of challenges:
Lack of clean, quality data: The lack of data quality is a huge concern regarding data warehouses. With a large amount of transactional data being generated, DWA also needs to be able to scale while maintaining data quality. A lack of clean data across the entire data pipeline can lead to incorrect business insights and cause companies to make poor decisions.
Job scares: With any sort of automation, there is a strong possibility that many roles may be made redundant. This is a challenge for DWA software because there could be a potential backlash to its implementation, as data-focused employees might feel that their jobs are at stake and will not accept the adoption of DWA software.
Integration challenges: The DWA tool must integrate seamlessly into a company's current data warehouse processes while managing disparate data platforms and file formats. A bad tool selection could cause massive losses not just in time (since developers would need to go back to manual ETL coding) but also in the company's finance. To rectify this, understanding the buying process is critical, which is provided in the section below.
How to Buy Data Warehouse Automation (DWA) Software
Requirements Gathering (RFI/RFP) for Data Warehouse Automation (DWA) Software
Before purchasing a DWA software, some important criteria need to be considered. Some of the key things to consider before purchase are as follows:
- Lack of a clear data vision: As companies understand the value of data, it's essential to set up a data vision to help drive analytics and business insights. For a company looking to understand its data vision, DWA software is an excellent choice because it helps automate the entire data warehousing process and provides a clear vision for data application across analytics and reports.
- Type of data warehouse: The architecture of a business data warehouse will be different across companies. The first step would be to analyze the current data warehouse and associated processes and identify a need for DWA.
- Labor-intensive workforce: If developers are spending hours writing code, DWA would be a good option to free up time.
- Need for real-time analytics: Without automation, BI can become a time-consuming task. Automating is a great way to ensure the data warehouse is maintained and provides accurate data, thereby driving crisp, clear information.
Data warehouse automation helps not only solve the above problems but also ensures a streamlined process between numerous teams that require data for their roles.
Compare Data Warehouse Automation (DWA) Software Products
Create a long list
In this step, buyers should keep their options open to consider the full range of products. Buyers have the freedom to explore this software market's numerous offerings. The long list can be made more concise and smaller by addressing the above requirements or goals.
Create a short list
Buyers can make much more granular comparisons on this step. In addition, buyers can use the G2 reviews to narrow this list further. Factors such as price also play an important role in creating the short list.
Conduct demos
Once the list has been reduced to a couple of vendors, buyers can request a demo. During the demo, buyers should seek out information related to their non-negotiable terms. This is a good stage where the buyer can delve more deeply into understanding the DWA software. They can check out automation and self-service features, dashboards and visualizations, any after-service support, staff training, and other additional features that can be provided when opting for their DWA solution.
Several DWA vendors also offer a 30-day free trial which is very useful when purchasing the software.
Selection of Data Warehouse Automation (DWA) Software
Choose a selection team
Choosing the right team to work together to decide the right DWA software is critical since several employees would need to access the data warehouse applications as required. The team should include a mix of different personas who have the required skills, interests, and time. Some technical roles include chief data officers or senior data engineers, data warehouse developers, privacy managers (to ensure data governance), along with project managers.
Negotiation
A buyer can choose to negotiate to trim costs. It is a good practice to check with the DWA vendor if they offer support, training, and other services. Keeping such factors in mind will help the buyer put forward better negotiation tactics for the specific functions.
Final decision
Once all the steps are complete, the final decision is made, weighing all factors and scenarios. Having a trial run of the software is a good place to start by using a pilot project. A small group of data warehouse admins, developers, and engineers can pass their views to the team making the final decision.
