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The Top 7 Data Aggregation Tools in 2024

May 10th, 2024

As data variety and volumes grow, extracting insights from data has become increasingly formidable. To put it into perspective, a bibliometrics study reveals that an astounding 2.5 million new papers are published annually. Processing this information is beyond traditional data processing tools.

Automated data aggregation tools offer a spectrum of capabilities that can overcome these challenges. They gather, process, and analyze data from diverse sources. From handling modest data processing tasks to managing large and complex datasets, these tools bolster an organization’s data infrastructure.

This blog reviews the top 7 data aggregation tools, exploring how each solution ensures that every byte of an organization’s data is harnessed for strategic insights.

What are Data Aggregation Tools?

Data aggregation tools are solutions designed to collect data from multiple sources, process it, and present it in a summarized format. These tools are essential for consolidating diverse data sets into a coherent whole, enabling organizations to analyze and derive insights from combined data. They typically include functionalities for extracting data from various databases, transforming it to align with business needs, and loading it into a central repository for easy access and analysis.

Why Are Data Aggregation Tools Important?

Data aggregation tools allow businesses to harness the power of their collective data, often siloed across different systems and formats. By aggregating data, these tools provide a unified view crucial for informed decision-making, trend analysis, and strategic planning. They also save time and resources by automating the data collection and transformation processes, reducing the likelihood of errors and ensuring data integrity.

Who Uses Data Aggregation Tools?

Business analysts, data scientists, IT professionals, and decision-makers across various industries rely on data aggregation tools to gather and analyze data. They are particularly valuable in finance, healthcare, marketing, and retail sectors, where data is a critical asset for understanding market trends, customer behavior, and operational efficiency. Essentially, any organization aiming to leverage data for competitive advantage will benefit from data aggregation tools.

Example Scenario: Data Aggregation Tools in Action

This example demonstrates how data aggregation tools facilitate consolidating financial data from multiple sources into actionable financial insights.

Scenario: A multinational corporation has branches worldwide, each operating with its financial systems and maintaining separate ledgers, expense records, and revenue reports.

The corporation’s CFO needs to prepare a consolidated financial statement for the annual report. However, the financial data is fragmented across different systems, currencies, and regulatory standards, making it challenging to compile a comprehensive statement.

Application of Data Aggregation Tool:

  1. Extraction: The tool extracts financial data from each branch’s ledgers, expense records, and revenue reports.
  2. Transformation: It converts various currencies into a single reporting currency and aligns disparate financial practices to the company’s standardized accounting principles.
  3. Loading: The transformed data is loaded into a central financial system.
  4. Aggregation: The tool aggregates the data to summarize total expenses, revenues, and financial performance metrics across all branches.
  5. Analysis: With the aggregated data, the CFO can analyze financial health, identify cost-saving opportunities, and assess the performance of each branch.

Outcome: The CFO successfully creates a unified financial statement that accurately reflects the corporation’s global financial status, enabling stakeholders to make informed investment decisions and strategic plans.

The 7 Best Data Aggregation Tools for Business


Astera is a data aggregation tool that combines a user-friendly interface with robust technical features. It caters to business and technical users without requiring extensive IT skills. At its core, Astera boasts a potent ETL engine that automates data integration.

Additionally, the platform’s customizable automation enhances efficiency by scheduling tasks and providing real-time monitoring to address integration errors quickly. These features streamline data integration, ensuring users enjoy uninterrupted data flow.

Likewise, Astera’s adaptability shines in handling diverse data sources. The platform leverages a high-performing ETL engine for efficient data movement and transformation, including mapping, cleansing, and enrichment.

Key Features:

  • AI-Driven Data Management: Streamlines data extraction, preparation, and data processing through AI and automated workflows.
  • Enterprise-Grade Integration Engine: Offers comprehensive tools for integrating diverse data sources and native connectors for easy mapping.
  • Interactive, Automated Data Preparation: Ensures data quality using data health monitors, interactive grids, and robust quality checks.
  • Advanced Data Transformation: Offers a vast library of transformations for preparing analysis-ready data.
  • Dynamic Process Orchestration: Automates data aggregation tasks, allowing for execution based on time-based schedules or event triggers.
  • User-Centric Design: With its no-code, drag-and-drop interface, Astera democratizes data management, making it accessible to users of all technical backgrounds.
  • Seamless Integration: Integrating with a wide array of data sources, both on-premises and cloud-based, ensures a smooth data management experience.
  • Comprehensive Data Handling: From extraction to insights, Astera offers a unified platform for all data-related tasks backed by a vast library of data operations.

Best for: Businesses looking for an end-to-end data management solution from extraction to data integration, data warehousing, and even API management.


Alteryx is a data analytics platform offering a suite of data aggregation tools. Its open-data engineering environment streamlines data preparation tasks, enabling users to aggregate data and share results without coding expertise. Alteryx’s drag-and-drop interface and deployment options allow collaboration and sharing among team members. Alteryx’s data preparation, blending, and cleansing features provide a solution for processing large data volumes.

Key Features:

  • Data collection
  • Data processing and presentation
  • Integration with various sources
  • User-friendly interface
  • Multi-server support, backup and recovery, and maintainability.


  • User-friendly interface for data preparation and analysis
  • Wide range of data sources and connectors
  • Flexible and customizable reporting and visualization options
  • Scalable for large datasets
  • Offers a variety of pre-built templates and tools for data analysis


  • Some users have reported that Alteryx’s customer support is lacking.
  • The documentation and assistance videos can be unclear and overly technical.
  • Integrating this platform for white-label/embedded use cases can be challenging.

Best for: Data analysts and businesses needing a robust data aggregation tool.

IBM Cloud Pak for Data

IBM Cloud Pak for Data is an integrated data and AI platform that aids in removing data silos and improving data security and accessibility. It offers a modular set of software components for data management.

The tool has features such as data fabric and AI lifecycle management, governance, security, integration, observability, and master data management.

Key Features:

  • Cloud-Native Solution
  • Unified Interface
  • Data Fabric Architecture
  • Built on Red Hat® OpenShift® Container Platform


  • Modular software components for streamlined data analysis and management.
  • Seamless integration with Red Hat OpenShift container platform
  • Data virtualization layer for consolidating distributed data sources
  • Supports a wide range of IBM and third-party services spanning the entire data lifecycle


  • Some users have reported that it can be difficult to scale due to the need to purchase additional licenses or upgrade to a higher-tier plan.
  • While IBM Cloud Pak for Data is touted as a low-code platform, considerable coding is still required for expression languages, advanced application programming interfaces, and webhooks.
  • Complex setup and configuration
  • Steep learning curve for some features

Best for: Businesses seeking to enhance productivity and simplify data management complexities.


Stata is a statistical software that offers data management and statistical analysis capabilities. It has a collapse command feature. This feature enables users to group data and compute summary statistics like mean, median, mode, and standard deviation.

The command also allows the creation of new variables derived from existing ones. Stata’s interface and extensive documentation further enhance its accessibility to novices in statistical software.

Key Features:

  • Broad Suite of Statistical Features
  • Publication-Quality Graphics
  • Automated Reporting
  • Python Integration (PyStata)
  • Community-Contributed Features


  • Wide range of statistical analysis and modeling capabilities
  • User-friendly interface for data manipulation and analysis
  • Supports a wide range of data formats and sources
  • Offers a variety of pre-built templates and tools for data analysis


  • Some users report that the Stata interface is not intuitive.
  • Limited visualization capabilities compared to other data aggregation tools
  • Limited collaboration features
  • Steep learning curve for some advanced features

Best for: Researchers and data analysts across diverse sectors.

Google Looker

Google Looker is a cloud-based business intelligence platform designed to support businesses in collecting, analyzing, and visualizing data from various sources. Its data modeling layer helps users integrate data from disparate databases, CRMs, and systems into a single view. Looker’s calculated fields and aggregate awareness features optimize query performance and facilitate the extraction of insights from large datasets.

Key Features:

  • Aggregate awareness
  • Integrated data exploration
  • GUI with visual profiling and insight tools.


  • User-friendly interface for data exploration and analysis
  • Seamless integration with Google Cloud Platform
  • Offers a wide range of data visualization options
  • Supports a wide range of data sources and connectors


  • Large dashboards have a slow loading time.
  • No SQL CLI.
  • Not enough high-resolution dashboards.
  • Needs better user management. IAM, SSO, etc., are for the embedded use case.
  • Limited scalability for large datasets
  • Steep learning curve for beginners

Best for: Businesses analyzing extensive data volumes seeking query efficiency.

Zoho Analytics

Zoho Analytics offers a platform for data analytics and aggregation. The platform is complete with dashboards and reports that support decision-making.

Zoho’s aggregate formulas enable insight generation from summary statistics, while its data blending capabilities ensure a single view from multiple sources. Zoho Analytics supports various data import formats and provides pivot tables for summarizing data across multiple dimensions. Its APIs help with data integration and customization, enabling streamlined reporting.

Key Features:

  • Data preparation with ZOHO DataPrep
  • Quick visual creation
  • Filter and user filter
  • Simple drag-and-drop dashboard


  • Extensive APIs for seamless data integration and dynamic report embedding.
  • User-friendly interface for data preparation and analysis
  • Wide range of data sources and connectors
  • Flexible and customizable reporting and visualization options
  • Supports real-time data integration and analysis
  • Offers a variety of pre-built templates and tools for data analysis


  • Limited customization options for more advanced users, such as creating custom formulas or customizing the look and feel of reports and dashboards.
  • Does not currently support connecting to all data sources, such as NoSQL databases or unstructured data sources.
  • Difficult to scale due to the need to purchase additional licenses or upgrade to a higher-tier plan.
  • Limited data source integration compared to other data aggregation tools
  • Limited customization options for dashboards and reports

Best for: Companies seeking a flexible and intuitive data analytics tool. is a cloud-based data integration platform streamlining, connecting, and aggregating data from diverse sources.

The platform has several pre-built connectors for cloud services and on-premises applications. It is also complemented by a no-code interface that helps users to create custom connectors.

Key Features:

  • Easy In-Pipeline Data Transformations
  • Simple Workflow Creation to Define Dependencies Between Tasks
  • Salesforce to Salesforce Integrations
  • Supports connectivity to over 200 e-commerce-related data stores
  • Data Transformation Tools


  • User-friendly interface for data integration and transformation
  • Supports a wide range of data sources and connectors
  • Offers a variety of pre-built templates and tools for data integration
  • Highly customizable and extensible


  • The drag-and-drop interface becomes extremely tough to use for complicated pipelines.
  • Deployment of pipelines can be quite confusing.
  • Error logs are not always detailed.
  • Transforming JSON or other nested data is not easy.

Best for: Scenarios that involve Salesforce Integration, File Data Preparation, REST API Ingestion, and Database Replication.

How to Choose a Data Aggregation Tool

Choosing the right data integration tool is crucial for effective data management and analysis. A data aggregation tool must meet an organization’s needs and be flexible enough to adapt to future changes. It should be able to handle the growing volume and variety of data and provide the functionality users need to transform and analyze data effectively.

Identify Data Sources

  • List all potential data sources such as databases, cloud storage, and third-party APIs.
  • Verify compatibility with the data aggregation tool for each source.
  • Test connections to ensure reliable data retrieval.

Assess Connectivity

  • Evaluate the tool’s ability to connect with listed data sources.
  • Check for pre-built connectors or APIs that facilitate easy integration.
  • Ensure support for real-time data access if needed for operations.

Evaluate Scalability

  • Understand the tool’s architecture and how it handles large data sets.
  • Conduct stress tests to see how the tool performs under heavy data loads.
  • Review the provider’s roadmap for future scalability improvements.

Consider Speed and Efficiency

  • Benchmark processing times for typical data aggregation tasks.
  • Compare performance against other tools.
  • Look for features that optimize performance, like in-memory processing.

Check for Data Transformation Features:

  • Identify the types of data transformations required, such as filtering, sorting, or merging.
  • Test the tool’s transformation capabilities with data samples.
  • Ensure the tool can handle complex transformations without significant performance impact.

Ensure Data Governance

  • Check for compliance with relevant data protection regulations.
  • Assess the tool’s data quality management features.
  • Verify that the tool provides audit trails and version control.

Look for Customization and Flexibility

  • Determine if the tool allows for customization to fit specific needs.
  • Test the tool’s flexibility by simulating changes in data processes.
  • Ensure the tool can adapt to future changes in data strategy.

User-Friendly Interface

  • Evaluate the UI/UX of the tool through a hands-on trial.
  • Gather feedback from potential users within the organization.
  • Check for the availability of training resources or certifications.

Support and Documentation

  • Research the level of support offered, including availability and response times.
  • Review the documentation for comprehensiveness and clarity.
  • Look for an active user community or forums for additional support.

 Essential Features for Choosing a Data Aggregation Tool

Not all data aggregation tools are created equal. Here’s a detailed breakdown of the essential features and capabilities that any robust data aggregation tool should offer:

Flexible Grouping Options

The ability to aggregate data by grouping it based on specified column(s) is fundamental. Look for data aggregation tools that allow for flexible grouping options, including single or multiple columns, date ranges, or custom criteria. This flexibility ensures you can tailor the aggregation process to suit your data requirements.

Comprehensive Summarization Methods

A reliable data aggregation tool should support various summarization methods to derive meaningful insights. These methods may include:

  • Count: Determine the total number of occurrences or records within each group.
  • Sum: Calculate the total sum of numerical values within each group.
  • Average: Compute the mean or average value of numeric data within each group.
  • Minimum and Maximum: Identify the smallest and largest values within each group.
  • Variance and Standard Deviation: Measure the dispersion or spread of data around the mean.

Ensure the data aggregation tool offers these standard summarization methods and the flexibility to create custom aggregations as needed.

data aggregation tools

Data Cleansing and Transformation

Look for built-in capabilities to cleanse and transform raw data before aggregation. This includes handling missing values, removing duplicates, standardizing formats, and converting data types. Robust data cleansing ensures the accuracy and reliability of aggregated results.

Scalability and Performance

Evaluate the data aggregation tool’s scalability and performance capabilities, especially when dealing with large datasets. It should efficiently handle increasing volumes of data without compromising speed or accuracy. Consider factors such as parallel processing, optimized algorithms, and support for distributed computing frameworks.

Data Source Compatibility

Ensure compatibility with various data sources, including databases, cloud storage platforms, APIs, and file formats. The data aggregation tool should seamlessly integrate with your existing data infrastructure and support real-time or batch data ingestion.

Customization and Extensibility

Seek data aggregation tools that offer customization options and extensibility features to adapt to evolving business needs. This may include custom functions, scripting capabilities, and integration with external libraries or frameworks for advanced analytics and visualization.

Security and Compliance

Prioritize data security and compliance requirements when selecting a data aggregation tool. Look for features such as role-based access control, encryption, audit trails, and compliance certifications (e.g., GDPR, HIPAA) to ensure the protection and privacy of sensitive data.

User-Friendly Interface and Accessibility

A user-friendly interface with intuitive navigation and visualization capabilities enhances user adoption and productivity. Additionally, consider accessibility features such as cloud-based deployment, cross-platform compatibility, and mobile support for seamless access to aggregated data from anywhere, anytime.

Take the Next Step with Astera

Data aggregation tools offer various features tailored to meet the evolving needs of businesses of all sizes. From simplifying complex data landscapes to empowering decision-makers with actionable insights, these tools are indispensable in harnessing the true potential of data.

Astera’s combination of user-friendliness, powerful AI capabilities, extensive connectivity, focus on data quality, and customization options make it a standout choice for organizations looking to streamline data aggregation.

Explore the power of Astera first-hand. Start your free trial today to see how Astera can transform your data aggregation workflow.

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