6 Best Data Aggregation Tools for 2025

Data aggregation tools expedite and empower the process of data analytics and business intelligence. Every business collects data. The difference between a business that reaps the benefits of their data and one that doesn’t is how they use their data and transform it to be analysed.  

We’re going to share some of the data aggregation tools and data aggregation examples, so you’ll be able to maximise the use of your own business’ data.

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What are the Best Data Aggregation Tools for 2025?

As we look ahead to 2025, several data aggregation tools stand out for their robust features, user-friendly interfaces, and scalability. This section will provide a detailed comparative analysis of the leading tools, helping you make an informed decision when selecting the best solution for your organization.

SolveXia

SolveXia is a powerful, user-friendly data automation platform that simplifies data aggregation, processing, and reporting. Its notable features include:

• No-code, drag-and-drop interface for building data workflows
• Seamless integration with various data sources and systems
• Automated data transformation and cleansing capabilities
• Dynamic reporting and data visualization tools

Databricks

Databricks is a powerful data aggregation platform that combines the capabilities of Apache Spark with a user-friendly interface. Its key features include:

• Seamless integration with various data sources and formats
• Scalable and flexible architecture for handling large datasets
• Support for real-time data processing and streaming analytics
• Collaborative workspace for data scientists and analysts

Matillion

Matillion is a cloud-based ETL (Extract, Transform, Load) tool that simplifies data aggregation and transformation processes. It offers:

• Drag-and-drop interface for building data pipelines
• Pre-built connectors for popular data sources and destinations
• Scalability and performance optimized for cloud environments
• Integration with leading cloud data warehouses like Amazon Redshift and Google BigQuery

Google BigQuery

Google BigQuery is a fully-managed, serverless data warehouse that enables fast and cost-effective data aggregation and analysis. Its notable features include:

• Petabyte-scale data storage and processing capabilities
• SQL-based querying for easy data exploration and analysis
• Real-time data streaming and batch processing
• Seamless integration with other Google Cloud services

Amazon Redshift

Amazon Redshift is a cloud-based data warehouse that offers high performance and scalability for data aggregation and analysis. It provides:

• Columnar storage and parallel processing for fast query performance
• Scalability to handle petabytes of data
• Integration with various data sources and AWS services
• Built-in security features and compliance certifications

Alteryx

Alteryx is a self-service data analytics platform that enables users to aggregate, blend, and analyze data from multiple sources. Its key features include:

• Intuitive drag-and-drop interface for building data workflows
• Support for a wide range of data sources and formats
• Advanced analytics capabilities, including predictive modeling and spatial analysis
• Collaboration and sharing features for team-based projects

What are Key Features of Data Aggregation Software?

When evaluating data aggregation tools, it's essential to consider the key features that contribute to their effectiveness and usability. Some of the crucial aspects to look for include:

Data Integration Capabilities

A robust data aggregation tool should seamlessly integrate with various data sources, including databases, spreadsheets, APIs, and cloud storage services. It should support various data formats and enable easy connectivity to ensure a smooth data aggregation process.

Real-time Data Processing

Real-time data processing is crucial for making timely decisions. Look for tools that offer real-time data streaming and processing capabilities, allowing you to analyze and act upon data as it arrives.

Scalability and Performance

As your data volumes grow, your data aggregation tool should be able to scale accordingly. Choose a solution that can handle large datasets efficiently, ensuring optimal performance even as your data needs evolve.

Security and Compliance

Data security and compliance are non-negotiable aspects of any data aggregation tool. Ensure that the solution you select employs robust security measures, such as data encryption, access controls, and auditing capabilities. Verify that the tool complies with relevant industry regulations and standards, such as GDPR, HIPAA, or SOC.

What is Data Aggregation?

Data aggregation is the process of gathering and combining data from multiple sources to create a unified dataset. This technique is essential for producing comprehensive insights, often used in reporting and analysis. By consolidating raw data, businesses can derive meaningful metrics such as sums, averages, or other statistical values. 

It supports various functions like trend analysis, financial forecasting, and decision-making. Automating this process, with tools like SolveXia, enhances efficiency and accuracy while reducing manual intervention. Effective data aggregation leads to higher-quality data, which is critical for actionable business insights.

What are the Best Data Aggregation Methods?

Data aggregation methods allow businesses to consolidate and analyze information effectively.

• Summarization: Reduces large datasets to key metrics like averages or totals.
• Grouping: Organizes data into meaningful categories or clusters for deeper analysis.
• Filtering: Focuses on extracting relevant data for specific insights.
• Real-time Aggregation: Analyzes data as it is collected, offering immediate insights.
• ETL (Extract, Transform, Load): Combines data from various sources, transforms it, and loads it into a unified system for comprehensive analysis.

These methods ensure accurate and timely data insights, driving better decision-making with tools like SolveXia.

When to Use Data Aggregation?

Data aggregation is most beneficial when you need to consolidate data from multiple sources to gain comprehensive insights. It is essential for reporting, where raw data is transformed into summarized metrics like totals or averages. 

It is also crucial for trend analysis and forecasting, helping identify patterns over time. Additionally, real-time aggregation is useful when quick decisions are needed based on up-to-date data. Finally, use data aggregation for compliance and auditing, ensuring accurate and comprehensive records.

What are ETL Processes and Data Aggregation?

Data aggregation is a critical component of the Extract, Transform, Load (ETL) process, which involves extracting data from various sources, transforming it into a consistent format, and loading it into a target system for analysis and reporting.

In the context of ETL, data aggregation typically occurs during the transformation stage. This is where data from different sources is consolidated, cleansed, and transformed to ensure consistency and compatibility. Top data aggregation tools often provide built-in ETL capabilities or integrate seamlessly with dedicated ETL solutions.

For example, Matillion offers a user-friendly interface for building ETL pipelines, allowing users to design data transformations and aggregations visually. Similarly, SolveXia's no-code platform enables users to create ETL workflows easily, automating the process of data extraction, transformation, and loading.

By leveraging the power of ETL processes and data aggregation, businesses can ensure that their data is accurate, consistent, and ready for analysis. This enables them to make informed decisions based on reliable insights.

What are Real-life Examples of Data Aggregation Tools?

To understand the value of data aggregation tools, let's explore real-life applications and case studies of companies that have successfully leveraged these solutions.

Insignia Financial - APRA Reporting

About Insignia Financial

Established in 1846, Insignia Financial is a prominent Australian financial services provider, managing over A$202 billion in funds. Focusing on excellence and consistency, the company sought to optimize and automate the preparation of its APRA returns using SolveXia.

The Challenge

The Fund Statutory Reporting team needed to prepare over 50 forms for APRA, a manual, time-consuming process prone to human error. The anticipated launch of APRA Connect threatened to increase the manual effort required, posing risks of higher compliance costs and error rates.

Solution and Outcomes

SolveXia automated the preparation of XBRL data, drastically reducing the time and effort needed to submit APRA returns. The automated solution mitigated the risk of human error and dependency on key staff, saving Insignia Financial 20 days annually and enhancing compliance with regulatory requirements.

7-Eleven - From Days to Minutes by Automating E-Wallet Reconciliations

About 7-Eleven

Philippine Seven Corporation, the exclusive licensor of 7-Eleven in the Philippines, operates over 3,400 stores nationwide. To streamline its e-wallet reconciliation process, 7-Eleven turned to SolveXia.

The Challenge

The manual reconciliation of transactions from multiple sources was extremely time-consuming, taking days to complete and introducing control risks. As transaction volumes grew, the process became increasingly unsustainable.

Solution and Outcomes

SolveXia automated the three-way reconciliation process, handling over 500,000 daily transactions across various payment gateways and bank accounts. The reconciliation is now completed in minutes, reducing fraud, improving staff work-life balance, and enabling the extension of SolveXia's capabilities to more processes.

Emma Sleep - Seamless ERP Integration and Reconciliation

About Emma Sleep

Emma Sleep, a global D2C sleep brand, manages transactions from various Payment Service Providers (PSPs) and marketplaces. To handle the growing complexity of their reconciliation processes, Emma Sleep adopted SolveXia.

The Challenge

The Accounts Receivable team faced challenges in standardizing and reconciling data from multiple PSPs due to the limitations of their ERP system and existing reconciliation tools.

Solution and Outcomes

SolveXia automated the reconciliation of payment data with the ERP, increasing matching efficiency by 500% and enabling daily reconciliations. The solution facilitated a seamless ERP transition and eliminated the need for business process outsourcing, leading to significant cost savings and operational efficiency.

What are the Future Trends in Data Aggregation Technology?

As we look towards the future, data aggregation technology is poised to evolve and adapt to the ever-changing business landscape. Here are some of the key trends that are likely to shape the future of data aggregation:

Artificial Intelligence and Machine Learning

Integrating artificial intelligence (AI) and machine learning (ML) capabilities into data aggregation tools will enable businesses to extract more value from their data. AI and ML algorithms can automate complex data processing tasks, identify patterns and anomalies, and provide predictive insights to drive proactive decision-making.

Edge Computing and IoT Integration

With the proliferation of Internet of Things (IoT) devices, edge computing is becoming increasingly important for data aggregation. Edge computing allows data to be processed and analyzed closer to the source, reducing latency and enabling real-time insights. Data aggregation tools seamlessly integrate with edge computing architectures and will be well-positioned to handle the massive influx of IoT data.

Data Governance and Privacy

As data privacy regulations evolve, data aggregation tools must prioritize data governance and compliance features. These features include robust access controls, data lineage tracking, and data masking capabilities to ensure that sensitive data is protected and handled in accordance with regulatory requirements.

Cloud-Native Solutions

The shift towards cloud-based infrastructure will drive the adoption of cloud-native data aggregation tools. These solutions are designed to leverage the scalability, flexibility, and cost-efficiency of cloud computing, enabling businesses to handle large volumes of data without the need for extensive on-premises infrastructure.

By staying attuned to these trends and adopting innovative data aggregation solutions, businesses can future-proof their data management strategies and stay ahead of the curve in an increasingly data-driven world.

Conclusion and Recommendations

When selecting a data aggregation tool for your organization, consider the key features and capabilities discussed in this guide, such as data integration, real-time processing, scalability, and security. Additionally, evaluate the tool's ease of use, compatibility with your existing infrastructure, and the vendor's support and resources.

Among the top data aggregation tools for 2025, SolveXia stands out for its user-friendly interface, powerful automation capabilities, and seamless integration with various data sources. Its no-code platform enables users to create data workflows easily, automate data transformation and cleansing processes, and generate dynamic reports and visualizations.

As you embark on your data aggregation journey, remember that the value of these tools lies in their ability to unlock the insights hidden within your data. By leveraging the power of data aggregation, you can drive innovation, improve operational efficiency, and gain a competitive edge in your industry.