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Data collected by enterprises is vital to help them make better decisions. But simply collecting high-quality and reliable data is not enough for analytics or business intelligence – especially when you’re dealing with large datasets.
Data aggregation is the process of collecting data from multiple sources and combining it into a single, unified view. At its most basic level, this involves compiling information from a range of prescribed databases and organising it into a simpler, easy-to-use medium, usually using sum, average, mean, or median references.
There are hundreds of great data aggregation tools available that can help you streamline this process, many of which are pre-built into data analytics platforms. Of course, however, not all of these tools are made the same.
In this list, we’re counting down ten of the best data aggregation tools on the market today, ranking them based on their ease of use, price and popularity with customers.
We kick off our list with Integrate.io – a cloud-based data integration platform that enables companies to connect to and aggregate data from a wide range of sources. Integrate.io makes it easy to prepare and transform your data with the platform’s powerful drag-and-drop interface. You can also stop attacks at the source, keeping your data secure while also allowing it to be quickly collected, processed, and presented into usable data for the company.
Integrate.io offers a variety of pre-built connectors for popular cloud and on-premises applications, as well as a no-code interface for creating custom connectors. It also includes a number of features for data transformation and cleansing, making it a one-stop shop for all of your data aggregation needs.
Known for its powerful data management and statistical analysis tools, Stata is a statistical software package that can be used for data analysis and visualisation. The platform includes a variety of tools for data aggregation, making it a popular choice for researchers and data analysts in a variety of fields. One of the most powerful features of Stata for data aggregation is the collapse command, which allows you to aggregate data by group, calculating summary statistics such as the mean, median, mode, and standard deviation for each group. You can also use the collapse command to create new variables based on existing variables.
Stata is a statistical software package, so it includes a wide range of statistical analysis tools. This means that you can easily analyze your aggregated data using the same software that you used to aggregate it. The platform is also a relatively easy-to-use software package, even for users with no prior experience with statistical software. The Stata interface is intuitive and the documentation is comprehensive and well-written.
Google Looker is a cloud-based business intelligence platform that helps businesses collect, analyze, and visualize data from multiple sources. Looker stands out for its data modelling layer, which allows users to combine data from multiple sources into a single, unified view. This makes it easy to aggregate data from different databases, CRMs, and other systems. Looker's calculated fields feature also allows users to create new variables from existing variables, which can be useful for aggregating data to different levels of granularity or for creating new metrics.
Looker also boasts an incredibly powerful Looker's aggregate awareness feature that automatically detects and uses aggregated data when possible. This can improve the performance of queries and make it easier to get insights from large datasets, making it a great choice for companies swimming in complex data.
With customisable dashboards and reports for data-driven decision-making, Zoho Analytics is a great choice for companies looking for a versatile, easy-to-use data analytics and aggregation tool. The platform offers a variety of aggregate formulas that can be used to calculate summary statistics for aggregated data, making it easy to get insights from your data quickly and easily. It also allows users to blend data from multiple sources into a single, unified view, making it easy to aggregate data from different databases, CRMs, and other systems.
The Zoho Analytics platform supports a wide range of data import formats, including CSV, Excel, JDBC, and Google Sheets, allowing users to seamlessly import data from a variety of sources, including databases, spreadsheets, and other applications. With its pivot tables feature, users can seamlessly create interactive tables that summarize data by different dimensions.
Fast, scalable and easy to use, ElasticSearch is a popular distributed search and analytics engine data aggregation. Designed to provide fast search and analytics capabilities for large datasets, the platform uses a distributed index structure to store and retrieve data efficiently. It also offers a rich query language that allows users to perform complex queries on their data. This includes support for aggregation queries, which can be used to calculate summary statistics for data.
With its simple API for indexing and querying data, Elasticsearch is a relatively easy-to-use tool, even for users with no prior experience with distributed search and analytics engines. But, despite its simplicity, it also includes a number of powerful analysis capabilities, such as geospatial analysis, text analysis, and machine learning. This makes it a good choice for users who want to perform complex analytics on their data without needing the technical know-how to do so.
With an industry-leading SERP Scraper API and a host of data extraction tools, NetNut is an incredible choice for those looking for real-time, structured web data tailored to their specific needs. The platform delivers structured data from global search engines, providing granular targeting down to the city/state level through a proxy network of over 20 million residential IPS and a growing number on a weekly basis. This allows businesses to seamlessly work around geo-blocked content and scrape data from across the web, giving them access to a plethora of actionable insights for SEO and market research.
In addition to its ISP proxy network, NetNut stands out for its dynamic P2P network deployed for increased scalability. This is especially useful for web scraping, market research & SEO tracking, and delivering outstanding success rates for your next big project with a cost-effective, pay-as-you-go payment plan. NetNut also provides easy integration and tracking solutions for your proxies, such as API access, Chrome extension, and a dedicated account manager to help you make the most out of your structured data.
Our best data analytics tool for 2023, Microsoft’s Power BI also boasts a host of incredible data aggregation tools that can help you gain actionable insights from your data. Power BI’s automatic aggregation feature, for example, uses machine learning to continuously optimise DirectQuery datasets without the need for extensive data modelling and query-optimisation skills. This allows users at any skill level to improve query performance, providing faster report visualisations for large and complex datasets.
Built on the back of Azure, the key focus of Power BI is data scraping, mining and reporting. The platform offers near-limitless data mining capabilities and can extract insights from traditional databases as easily as it can extract data from newer SaaS architectures like GitHub and Zendesk. Power BI’s UI is intuitive for users familiar with Excel, and its deep integration with other Microsoft items makes it a flexible self-service tool requiring minimal preparation.
At number three we have MongoDB, a No-SQL document model providing fully managed data aggregation in the cloud. Promising one of the most innovative cloud database services on the market, the MongoDB platform provides unmatched mobility and data distribution across multiple cloud environments, including Google Cloud, Azure, and AWS. You can process data records in a variety of different ways, such as grouping data, sorting data into a specific order, or restructuring returned documents. There’s built-in automation included for workload and resource optimisation and easy migration too, allowing you to make the most out of your time and out of your data.
MongoDB provides aggregation operations through aggregation pipelines – a series of operations that process data documents sequentially. You can filter, sort, group, and transform documents directly through the platform, and then use all these features together to form a multi-stage processing pipeline.
Next up we have Alteryx – an industry-leading data analytics platform with a vast collection of tools and features to help you aggregate data. The Alteryx open-data engineering environment, for instance, significantly reduces the time it takes to perform data preparation tasks like data aggregation. This self-service data engineering cloud is open, interactive, and collaborative, making it easy to compile data from multiple sources, perform aggregations, and share the results with your data team – no coding or technical skills required.
As well as aggregation, Alteryx offer advanced analytics capabilities such as predictive and spatial analytics and AI and ML-powered automation. Its intuitive drag-and-drop interface and flexible deployment options make it easy to collaborate and share results with team members. Meanwhile, its data preparation, blending, and cleansing features allow you to clean, aggregate, and analyse your data seamlessly, providing a scalable architecture for the processing of large volumes of data.
Our number one spot goes to Tableau, the one-stop solution for all of your data analytics and aggregation needs. With Tableau, you can seamlessly aggregate measures or dimensions through a single BI analytics and infrastructure platform. All of the data – filtered for different factors – is highlighted across dashboards to enable limitless visual data exploration without interrupting the flow of data. It is also incredibly user-friendly, meaning that even those without much technical expertise can seamlessly aggregate or analyse data.
Tableau has a multitude of different features for data aggregation and visualisation in different forms. Both measures and dimensions can be aggregated and viewed as types as per the data that is being focused on. You can also alter the aggregation levels within a specific view and fine-tune the factors you want to incorporate in the aggregation process.
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