My Take on the Top 10 Best Data Extraction Software

Data is the lifeblood of modern decision-making, but let’s face it—extracting meaningful information from vast amounts of unstructured or scattered data is no easy feat. 

I’ve been there—struggling with clunky processes, endless copy-pasting, and tools that overpromised but underdelivered. It became clear that I needed a robust solution to streamline my workflow and save precious hours.

I began my search with one goal: to find the best data extraction software that is powerful yet user-friendly, integrates seamlessly into my existing systems, and, most importantly, delivers accurate results without the hassle.

My journey wasn’t just about trial and error. I read detailed reviews on G2, tested various tools hands-on, and compared features like automation, customization, and scalability. The result? A curated list of the best data extraction software designed to meet diverse needs—whether you're managing business intelligence, improving customer insights, or simply organizing large datasets.

If you’re tired of inefficient processes and want tools that deliver real value, this list is for you. Let’s dive into the top options that stood out during my testing!

My top 10 best data extraction software recommendations for 2025

Data extraction software helps me collect, organize, and analyze large amounts of data from various sources.

The best data extraction software goes beyond manual methods, automating tedious processes, ensuring accuracy, and seamlessly integrating with other platforms. It has become an essential part of my workflow, making data projects far less overwhelming.
When I started working with data, extracting and organizing it felt like a nightmare.

I spent hours manually reviewing spreadsheets, only to miss key insights. Once I began using the best data extraction software, data collection became faster and more efficient. I could focus on interpreting insights rather than wrestling with messy data. These tools not only made my work easier but also improved the accuracy of my reports and gave me back valuable hours each day.

In this article, I’ll share my personal recommendations for the top 10 best data extraction software for 2025. I’ve tested each tool and will highlight what makes them stand out and how they’ve helped me tackle my biggest data challenges.

What I look for in data extraction software

When selecting a data extraction software, I prioritize a few key features:

• Ease of integration: I need data extraction software that seamlessly integrates with my existing systems, whether on-premises or cloud-based. It must offer robust API support, enabling me to interact programmatically with platforms like CRMs, ERPs, and analytics tools. Pre-built connectors for commonly used tools, such as Salesforce, Google Workspace, AWS S3, and databases like MySQL, PostgreSQL, and MongoDB, are essential to reduce setup time and effort. The software must support middleware solutions for connecting with lesser-known platforms and allow for custom connectors when required. Additionally, it should provide native support for exporting data to data lakes, warehouses, or visualization tools like Tableau or Power BI.
• Customizable extraction rules: I need the ability to define detailed extraction parameters tailored to my specific needs. This includes advanced filtering options to extract data based on field conditions, patterns, or metadata tags. For unstructured data, the software must offer features like natural language processing (NLP) to extract relevant text and sentiment analysis for insights. It should support regular expressions for identifying patterns and allow for custom rule-building with minimal coding knowledge. The ability to create templates for repetitive extraction tasks and adjust configurations for different data sources is crucial to streamlining recurring workflows.
• Support for multiple data formats: I require software capable of handling a wide range of structured and unstructured data formats. This includes industry-standard file types like CSV, Excel, JSON, XML, and databases, as well as specialized formats like electronic data interchange (EDI) files. It should support multilingual text extraction for global use cases and retain the integrity of complex table structures or embedded metadata during the process.
• Scalability: I need a solution that can effortlessly scale with increasing data volumes. It should be capable of processing millions of rows or handling multiple terabytes of data without compromising performance. The software must include features like distributed computing or multi-threaded processing to handle large datasets efficiently. It should also adapt to the complexity of data sources, such as extracting from high-traffic websites or APIs, without throttling or errors. A cloud-based or hybrid deployment option for scaling resources dynamically is preferred to manage peak workloads.
• Real-time data extraction: I require software that supports real-time data extraction to keep my systems up-to-date with the latest information. This includes connecting to live data streams, webhooks, or APIs to pull changes as they occur. The tool must support incremental extraction, where only new or modified data is captured to save processing time. Scheduled extraction tasks should allow for minute-level precision, ensuring timely updates. Furthermore, it should integrate with event-driven architectures to trigger automated workflows based on extracted data.
• Data accuracy and validation: I need robust data validation features to ensure that extracted data is clean, accurate, and usable. The software should include built-in checks for duplicate records, incomplete fields, or formatting inconsistencies. Validation rules must be customizable, enabling me to set thresholds for acceptable data quality. Error reporting should be detailed, providing insights into where and why issues occurred during the extraction process. An interactive dashboard for reviewing, correcting, and reprocessing invalid data would further enhance accuracy.
• User-friendly interface: The software must feature an intuitive interface that caters to both technical and non-technical users. It should provide a clean dashboard with drag-and-drop functionality for creating extraction workflows without coding. A step-by-step wizard for configuring tasks, along with in-app tutorials and tooltips, is necessary for a smooth user experience. Additionally, it should include role-based access controls to ensure users only see relevant data and options.
• Security and compliance: I need software that prioritizes data security at every stage of the extraction process. This includes end-to-end encryption for data in transit and at rest, secure authentication methods like multi-factor authentication (MFA), and role-based access controls to limit unauthorized access. Compliance with regulations like GDPR, HIPAA, CCPA, and other industry-specific standards is essential to ensure the legal and ethical handling of sensitive data. The software should also provide audit trails to track who accessed or modified the extracted data.
• Automated workflows: I need the software to offer advanced automation features to streamline repetitive tasks. This includes the ability to schedule extraction jobs at predefined intervals and set up triggers for specific events, such as a file upload or database update. Workflow automation should allow integration with tools like Zapier, Microsoft Power Automate, or custom scripts to perform actions like data transformation, storage, or visualization automatically. Notifications or alerts on the success or failure of automation tasks would be highly beneficial for monitoring.
• Advanced analytics and reporting: I require a solution that provides in-depth insights into the extraction process through detailed analytics and reporting. The software must track metrics such as processing times, success rates, error counts, and resource utilization. Reports should be exportable in multiple formats and customizable to include KPIs relevant to my workflows. The ability to visualize data and identify bottlenecks in the process through dashboards is also critical for optimizing performance and ensuring efficiency.

The list below contains genuine user reviews from our best data extraction software category page. To qualify for inclusion in the category, a product must:

• Extract structured, poorly structured, and unstructured data
• Pull data from multiple sources
• Export extracted data in multiple readable formats

This data has been pulled from G2 in 2025. Some reviews have been edited for clarity.

1. Bright Data

One of Bright Data's best features is the Datacenter Proxy Network, which includes over 770,000 IPs across 98 countries. This global coverage made it easy for me to access data from almost anywhere, which was incredibly useful for large-scale projects like web scraping and data mining. I also appreciated the customization options, as I could set up scraping parameters to meet my specific needs without feeling limited by the platform.

The compliance-first approach was another aspect I valued. Knowing that Bright Data prioritizes ethical and legal data collection gave me peace of mind, especially when handling sensitive or large datasets. In a world where data privacy is so critical, this was a major plus for me.

Having a dedicated account manager made a big difference in my experience. Anytime I had questions or needed guidance, help was just a call away. The 24/7 support team also resolved issues quickly, which kept my projects running smoothly. I found the flexible pricing options to be helpful as well. Choosing between paying per IP or based on bandwidth usage allowed me to select a plan that worked for my budget and project requirements.

I also found the integration process simple. With just a few lines of code, I connected Bright Data with my applications, regardless of the coding language I was using.

However, I did encounter some challenges. At times, the proxies would drop unexpectedly or get blocked, which disrupted the flow of my data collection. This was frustrating, especially when working on urgent tasks, as it required additional troubleshooting.

I also found the platform to have a steep learning curve. With so many features and options, it took me a while to get comfortable with everything. Although the documentation was helpful, it wasn’t always clear, so I had to rely on trial and error to find the best configurations for my needs.

Another drawback was the account setup verification process. It took longer than I anticipated, with extra steps that delayed the start of my projects. This was a bit of a hassle, as I was eager to start but had to wait for the process to be completed.

Lastly, I struggled with the account management APIs. They were often non-functional or lacked intuitiveness, which made it harder for me to automate or manage tasks effectively. I ended up doing a lot of things manually, which added time and effort to my workflow.

What I dislike about Bright Data:

• While the global coverage was beneficial, the large-scale network might be overwhelming at times, making it difficult to identify the most relevant IPs for my specific needs.
• Although Bright Data emphasizes compliance, managing the ethical aspects of data collection was challenging for me, especially when navigating complex legal requirements for different regions.

What G2 users dislike about Bright Data:

"One downside of Bright Data is its slow response during peak traffic times, which can disrupt our work. Additionally, it can be overwhelming at first, with too many features that make it hard to focus on the most important ones we need. As a result, this has sometimes delayed critical competitor analysis, affecting the timing of our decision-making and our ability to quickly respond to market changes."

- Bright Data Review, Marcelo C.

2. Fivetran

I appreciate how seamlessly Fivetran integrates with a wide range of platforms, offering a robust selection of connectors that make pulling data simple and hassle-free.  Whether I need to extract information from Salesforce, Google Analytics, or other database software, Fivetran has me covered.

This versatility makes Fivetran an excellent choice for consolidating data from multiple sources into a single analysis destination. Whether I’m working with cloud-based applications or on-premise systems, Fivetran saves time and eliminates the headaches of manual data transfers. 

Another key feature I find incredibly useful is automated schema updates. These updates ensure that the data in my destination remains consistent with the source systems. Whenever the source schema changes, Fivetran handles the updates automatically, so I don’t have to spend time making manual adjustments.

One of Fivetran's standout features is its simple setup process. With just a few clicks, I can connect data sources without needing advanced technical skills or spending hours on complex configurations

Despite its strengths, there are some challenges I’ve faced with Fivetran. While it offers an impressive number of connectors, there are still gaps when it comes to certain critical systems. For example, I’ve encountered difficulties extracting data from platforms like Netsuite and Adaptive Insights/Workday because Fivetran doesn’t currently support connectors for these systems. 

Occasionally, I’ve encountered faulty connectors that disrupt data pipelines, causing delays and requiring manual troubleshooting to resolve the issues. While these instances aren’t frequent, they can be frustrating when they happen.

Another significant drawback is schema standardization. When I connect the same data source for different customers, the table schemas often vary. For instance, some columns might appear in one instance, but not another, column data types may differ, and, in some cases, entire tables may be missing.

To address these inconsistencies, I had to develop a set of complex custom scripts to standardize the data delivery. While this approach works, it adds an unexpected layer of complexity that I wish could be avoided.

What I dislike about Fivetran:

• While Fivetran offers many connectors, I’ve faced challenges with missing support for critical systems like Netsuite and Adaptive Insights/Workday, which limits my ability to extract data from these platforms.
• Schema standardization became an issue when connecting the same data source for different customers, leading to inconsistencies that required me to write complex custom scripts, adding an extra layer of complexity to my work.

What G2 users dislike about Fivetran:

"Relying on Fivetran means depending on a third-party service for important data workflows. If they experience outages or issues, it could affect your data integration processes."

- Fivetran Review, Ajay S.

3. NetNut.io

What I dislike about NetNut.io:

• The lack of detailed documentation made it challenging to fully understand all the advanced features and best practices. I had to rely on trial and error to figure things out, which could have been avoided with clearer guides.
• While understandable for security reasons, the KYC process was much slower and more tedious than I anticipated. It required multiple verification steps, which resulted in unnecessary delays and frustration.

What G2 users dislike about NetNut.io:

"More detailed documentation on setting up and using the proxies would be helpful, especially for those who are new to proxy services. It would improve ease of use and make the setup process smoother for all users."

- NetNut.io Review, Latham W.

4. Smartproxy 

One of Smartproxy's standout features is its exceptional IP quality. It’s incredibly reliable, even when accessing websites with strict anti-bot measures. I’ve been able to scrape data from some of the most challenging sites without worrying about being blocked.

Another feature that makes Smartproxy indispensable is its versatile output formats, including HTML, JSON, and table. This flexibility ensures that no matter the project requirements, I can seamlessly integrate the extracted data into my tools or reports without spending hours reformatting. 

The ready-made web scraper completely removes the need to code custom scrapers, which is a big win, especially for non-technical users or when time is limited. The interface makes it easy to set up and run even complex tasks, reducing the learning curve for advanced data extraction. I also find the bulk upload functionality to be a game-changer. It allows me to execute multiple scraping tasks simultaneously, which is invaluable for managing large-scale projects. 

While the web extension is convenient for smaller tasks, it feels too limited for anything beyond the basics. It lacks the advanced capabilities and customization options of the main platform. On several occasions, I’ve started a project using the extension only to realize it couldn’t handle the complexity, forcing me to switch to the full tool and restart the process—a frustrating waste of time.

I also find the filtering options insufficient for more granular data extraction. For instance, during a recent project, I needed to extract specific data points from a dense dataset, but the limited filters couldn’t refine the results adequately. As a result, I ended up with a bulk of unnecessary data and had to spend hours manually cleaning it, which completely negated the efficiency I was expecting.

Another issue is the occasional downtime with certain proxies. Although it doesn’t happen frequently, when it does, it’s disruptive. Lastly, the error reporting system leaves much to be desired. When a task fails, the error messages are often vague, providing little insight into what went wrong. I’ve wasted valuable time troubleshooting or contacting support to understand the issue—time that could have been saved with clearer diagnostics or more detailed logs.

What I dislike about Smartproxy:

• While convenient for smaller tasks, the web extension felt too limited for handling complex projects. It often forced me to restart tasks on the full platform, which wasted valuable time and effort.
• Insufficient filtering options for granular data extraction left me with large volumes of unnecessary data during critical projects, requiring hours of manual cleaning and reducing overall efficiency.

What G2 users dislike about Smartproxy:

"For packages purchased by IP, it would be helpful to have an option to manually change all IPs or enable an automatic renewal cycle that updates all proxy IPs for the next subscription period. Currently, this feature is not available, but allowing users to choose whether to use it would greatly enhance flexibility and convenience."

- Smartproxy Review, Jason S.

5. Oxylabs 

Setting up Oxylabs is easy and doesn’t require much technical know-how. The platform provides clear, step-by-step instructions, and the integration into my systems is quick and straightforward. This seamless setup saves me time and hassle, allowing me to focus on data extraction rather than troubleshooting technical issues.

It stands out for its reliable IP quality, which is crucial for my data scraping work. The IP rotation process is smooth, and I rarely experience issues with proxy availability, making it dependable for various tasks. Their proxies are high-performing, ensuring minimal disruption even when scraping websites with advanced anti-scraping measures. 

Oxylabs also lets me send custom headers and cookies without extra charges, which helps me mimic real user behavior more effectively. This ability allows me to bypass basic anti-bot measures, making my scraping requests more successful and increasing the accuracy of the data I collect. 

One standout feature is OxyCopilot, an artificial intelligence-powered assistant integrated with the Web Scraper API. This tool auto-generates the code needed for scraping tasks, saving me a considerable amount of time. Instead of writing complex code manually, I can rely on OxyCopilot to quickly generate the necessary code, especially for large-scale projects. This time-saving feature is invaluable, as it allows me to focus on other important tasks while still ensuring that the scraping process runs efficiently.

However, there are a few downsides. Certain data restrictions make some data sources harder to access, particularly due to request limits set by the websites. This can slow down my work, especially when dealing with large datasets or websites that have tight access controls in place. 

Occasionally, proxy issues, such as slow response times or connectivity problems, can cause delays in the scraping process. Although these issues aren't frequent, they do require occasional troubleshooting, which can be a minor inconvenience.

The whitelisting process for new websites can also be frustrating. It takes time to get approval for new sites, and this delay can hold up my projects and reduce productivity, especially when dealing with time-sensitive tasks.

Lastly, the admin panel lacks flexibility when it comes to analyzing data or costs. I don’t have direct access to detailed insights about data processing or cost distribution across scraping tasks. Instead, I have to request this information from Oxylabs support, which can be time-consuming. Having more control over these aspects would greatly improve the user experience and make the platform more efficient for my needs.

What I dislike about Oxylabs:

• Data restrictions, such as request limits imposed by websites, make accessing certain sources challenging, particularly when handling large datasets. These constraints can slow down my workflow and impact productivity.
• The admin panel lacks flexibility in providing detailed insights into data processing or cost distribution. Having to request this information from support instead of accessing it directly delays project analysis and decision-making.

What G2 users dislike about Oxylabs:

"After signing up, you receive numerous emails, including messages from a "Strategic Partnerships" representative asking about your purpose for using the service. This can become annoying, especially when follow-ups like, "Hey, just floating this message to the top of your inbox in case you missed it," start appearing. Oxylabs is not the most affordable provider on the market. While other providers offer smaller data packages, unused GBs with Oxylabs simply expire after a month, which can feel wasteful if you don’t use all your allocated data."

- Oxylabs Review, Celine H.

6. Coupler.io

Coupler.io is a powerful data extraction tool that has greatly streamlined my process of gathering and transforming data from multiple sources. With its user-friendly interface, I can effortlessly integrate data from a variety of platforms into a unified space, saving time and improving efficiency.

One of the standout features is its ability to integrate data from popular sources like Google Sheets, Airtable, and various APIs. This integration has significantly enhanced my ability to perform in-depth data analysis and uncover insights that would have otherwise been missed. Coupler.io enables seamless connection between multiple data sources, making it easy to centralize all my information in one place.

Another highlight is Coupler.io’s customized dashboard templates. These templates have been a game-changer, allowing me to build intuitive and interactive dashboards tailored to my specific needs without requiring advanced technical skills. By combining data from sources such as CRMs, marketing platforms, and financial tools, I can create more powerful and holistic analytics dashboards, improving the depth and accuracy of my analysis.

Coupler.io also stands out as a no-code ETL solution, which I greatly appreciate. As someone with limited coding experience, I’m able to perform complex data transformation tasks within the platform itself—no coding required. This feature makes the tool accessible, allowing me to focus on data management and analysis rather than needing separate tools or developer support.

However, there are a few areas that could use improvement. One issue I’ve encountered is with the connectors. Occasionally, I’ve faced intermittent connectivity problems when linking certain platforms, which can be frustrating, especially when I need quick access to my data.

Additionally, managing large volumes of data once it’s pulled into Coupler.io can be challenging. While the tool offers excellent options for combining data sources, organizing and keeping track of everything can become cumbersome as the datasets grow. Without a clear structure in place, it can feel overwhelming to manage everything, which can hinder productivity.

Another drawback is the limited data transformation options. While Coupler.io does offer basic transformation capabilities, they are somewhat restricted compared to more advanced platforms. For more complex data manipulation, I may need to rely on additional tools or workarounds, which add extra steps to the process and reduce the overall efficiency of the tool.

What I dislike about Coupler.io:

• I’ve faced intermittent connectivity issues with certain platforms, which can be frustrating when I need quick access to my data for time-sensitive projects. It disrupts my workflow and slows me down.
• Managing large datasets within Coupler.io sometimes feels overwhelming. Without better organizational features, it’s hard to keep track of everything, which impacts my productivity.

What G2 users dislike about Coupler.io:

"Currently, syncing operates on preset schedules, but it would be great to have the option to set up additional triggers, such as syncing based on changes to records. This would make the process more dynamic and responsive to real-time updates."

- Coupler.io Review, Matt H.

7. Skyvia 

One of the standout features I truly appreciate about Skyvia is its robust data replication capabilities. Whether I’m working with cloud databases, applications, or on-premises systems, Skyvia makes it incredibly easy to replicate data across different platforms in a reliable and efficient manner. This flexibility is invaluable for maintaining a unified and up-to-date data ecosystem.

Skyvia handles data transformations seamlessly.  It allows me to map and transform data as it moves between systems. The platform offers an intuitive interface for creating transformation rules, making it easy to manipulate data on the fly. Whether I need to clean up data, change formats, or apply calculations, Skyvia lets me do it without any hassle. This feature alone has saved me countless hours of manual work, especially with complex transformations that would otherwise require custom scripts or third-party tools.

Another impressive aspect of Skyvia is its handling of complex data mappings. As I work with multiple systems that use different data structures, Skyvia makes it easy to map fields between systems. Even when data formats don’t match exactly, I can define custom field mappings, ensuring accurate data transfer between systems. 

Its synchronization feature keeps my data warehouse in sync with real-time data changes is a game-changer. With sync intervals as frequent as every 5 minutes, my data is always up-to-date, and I don’t have to take any manual action to maintain accuracy. 

However, there are a few areas where Skyvia could improve. One limitation I’ve encountered is related to data handling when working with exceptionally large datasets. While Skyvia excels in syncing and replicating data, the process can become a bit sluggish when dealing with massive volumes of data. This can slow down the workflow, especially in high-demand environments.

Another area that could be improved is Skyvia’s error reporting system. Although the tool logs errors, I’ve found that the error messages often lack actionable detail. When something goes wrong, it can be challenging to immediately identify the root cause of the issue. The absence of specific error descriptions makes troubleshooting more difficult and time-consuming.

Skyvia can be a bit restrictive regarding advanced customizations. For example, if I need to implement a highly specialized data mapping rule or perform a complex data transformation that goes beyond the platform’s standard features, I may encounter limitations. While custom scripts are supported, users with advanced needs might find these constraints a bit frustrating.

While the platform offers connectors for many popular services, there are times when I need to integrate with a less common or niche system that isn't supported out of the box. In such cases, I either have to rely on custom scripts or look for workarounds, which can add complexity and extra time to the setup process. The lack of pre-built connectors for some platforms can be a significant inconvenience, especially when working on projects with diverse data sources or when needing to quickly integrate a new tool or system into my workflow. 

What I dislike about Skyvia:

• When working with exceptionally large datasets, I noticed that the replication process tends to slow down, creating bottlenecks in my workflow during high-demand situations.
• The error reporting system often frustrates me because it doesn’t provide enough actionable detail. Due to vague error messages, I end up spending extra time identifying and resolving the root cause of issues.

What G2 users dislike about Skyvia:

"During the beta connection stage, we encountered an error due to an incompatibility with the Open Data Protocol (OData) version in Microsoft Power Business Intelligence (Power BI). Unfortunately, there’s no option to edit the current endpoint, so we had to create an entirely new one, selecting a different Open Data Protocol version this time."

- Skyvia Review, Maister D.

8. Coefficient 

What I dislike about Coefficient:

• I’ve occasionally encountered issues when importing data from certain sources. The data doesn’t come through as expected or requires additional adjustments, which can be frustrating and time-consuming.
• When handling large tables with complex structures, Coefficient’s performance can slow down, and I’ve encountered errors when rendering large datasets, hindering my work with extensive data.

What G2 users dislike about Coefficient:

"A small issue, which may be difficult to resolve, is that I wish Coefficient could create sheets synced from another tool (e.g., a CRM) without the blue Coefficient banner appearing as the first row. Some products rely on the first row for column headers, and they can’t find them if the Coefficient banner is there."
- Coefficient Review, JP A.

9. Rivery 

Rivery is a powerful AI data extraction tool that has completely transformed the way I build end-to-end ELT (Extract, Load, Transform) data pipelines. It provides an intuitive yet robust platform for handling even the most complex data integration tasks with ease, making it a game-changer in streamlining my data processes.

What stands out to me the most is the flexibility Rivery offers. I can choose between no-code options for quick, streamlined builds or incorporate custom code when I need to perform more intricate transformations or workflows. Whether I’m working on analytics, AI projects, or handling more complex tasks, Rivery adapts to my needs, providing a seamless experience that scales with my requirements.

One of Rivery's standout features is its GenAI-powered tools, which significantly speed up the process of building data pipelines. These tools help me automate repetitive tasks, cutting down on manual work and saving me valuable time. With GenAI, I can streamline big data flows effortlessly, ensuring that each stage of the pipeline runs smoothly and efficiently.

The speed at which I can connect and integrate my data sources is nothing short of impressive. Whether I’m working with traditional databases or more specialized data sources, Rivery makes it incredibly easy to connect them quickly—without the need for complicated manual configurations. This has saved me valuable time and effort, allowing me to focus on extracting insights rather than worrying about integration hurdles.

However, while Rivery is an incredibly powerful tool, there was a noticeable learning curve when I first started using it. For someone not familiar with advanced data processing or coding, getting up to speed can take some time. Although the platform is intuitive, unlocking its full potential required me to spend considerable time experimenting and understanding its intricacies.

I’ve also noticed that some basic variables, such as filter conditions or dynamic date ranges, which are commonly found in other ETL tools, are missing in Rivery. This can be frustrating when trying to fine-tune processes, particularly for more customized extraction or transformation steps. The absence of these features sometimes forces me to spend extra time writing custom code or finding workarounds, which can slow down the workflow.

I feel there’s room for improvement when it comes to the visualization of data pipelines. The current tools don’t offer as much clarity when tracking the flow of data from one step to the next. A more detailed, intuitive visualization tool would help me better understand the pipeline, especially when troubleshooting or optimizing the data flow.

Lastly, the documentation could use some improvement. It doesn’t always provide the level of clarity I need to fully understand the more advanced features. Expanding and updating the documentation would make the platform easier to use, especially for those who may not have a deep technical background.

While the user support portal offers some useful resources, I often need to expand my search beyond what is readily available in the knowledge base. More comprehensive support and better documentation would definitely enhance the overall user experience.

What I dislike about Rivery:

• The noticeable learning curve when I first started using Rivery required me to invest considerable time in experimenting and understanding the platform's features, especially since it wasn't immediately intuitive for someone without advanced coding knowledge.
• Missing features like filter conditions or dynamic date ranges, which are available in other ETL tools, forced me to write custom code or find workarounds, sometimes slowing down my workflow and creating additional complexities.

What G2 users dislike about Rivery:

"To improve the product, several basic areas need attention. First, more user-friendly error messages would help avoid unnecessary support tickets. Essential variables like file name, file path, number of rows loaded, and number of rows read should be included, as seen in other ETL tools. Additionally, expanding the search functionality in the user support portal and increasing the support team would enhance the user experience. The documentation also needs improvement for better clarity, and having a collection of examples or kits would be useful for users."

- Rivery Review, Amit K.

10. Apify

Apify offers a vast ecosystem where I can build, deploy, and publish my own scraping tools. It’s the perfect platform for managing complex web data extraction projects, and its scalability ensures that I can handle everything from small data pulls to large-scale operations. 

What I love most about Apify is its web scraping efficiency. I can scrape data from a wide variety of websites and APIs with remarkable speed, ensuring I get the data I need without long delays. The process is highly optimized for accuracy, which saves me a lot of time and effort compared to other scraping solutions.

Another major advantage for me is verbose logging. I really appreciate how detailed the logs are, as they give me clear insights into how the scraping is progressing and any potential issues I need to address.

The graphical displays of scraping runs are also a huge help, allowing me to visualize the scraping process in real-time. These tools make it incredibly easy for me to troubleshoot any errors or inefficiencies, and they help me monitor performance in a way that feels intuitive.

Plus, Apify supports multiple languages, which is great for me since I often collaborate with international teams. This multi-language support makes the platform accessible to developers worldwide and ensures that the platform is adaptable to a wide range of projects.

One issue I’ve run into with Apify is occasional performance inconsistencies with Actors. Sometimes, the actors I use don’t work perfectly every time, which can lead to delays in my scraping tasks. This can be a bit frustrating, especially when I need to meet tight deadlines or when the scraping process is critical to a larger project. 

Additionally, Apify doesn’t allow me to build my own Docker images for actors. For someone like me who likes to have complete control over the execution environment, this limitation can feel a bit restrictive. Customizing Docker images for my actors would allow me to better align the environment with my specific needs and preferences, providing a more tailored experience for my tasks.

Another thing I’ve noticed is that the SDK support is somewhat limited. While Apify provides a decent set of APIs, the SDKs aren’t as flexible as I would like them to be. There are times when I need to integrate Apify into a more complex custom setup, and the SDKs don’t quite meet my needs in those situations. 

I also can’t upload a file directly to an actor input, which makes working with file-based data a bit cumbersome. This limitation adds an extra step to my workflow when I need to process files alongside my scraping tasks.

Additionally, a feature that I really think would be helpful is a “Retry Failed Requests” button for actors. Right now, when an actor run fails, I need to manually restart the process, which can be time-consuming and adds unnecessary friction to the workflow. 

What I dislike about Apify:

• Occasional performance inconsistencies with Actors cause delays in scraping tasks, which can be frustrating when working under tight deadlines or on critical projects where reliability is key.
• The inability to build custom Docker images for actors limits my control over the execution environment. This prevents me from tailoring the setup to my specific needs and hinders the flexibility I require.

What G2 users dislike about Apify:

"Despite its strengths, Apify has a few limitations. It has a steep learning curve, requiring technical knowledge to fully leverage its advanced features. The pricing structure can be complex, with different tiers that may confuse new users. Additionally, there are occasional performance inconsistencies, with some actors not working perfectly every time."

- Apify Review, Luciano Z.

Best data extraction software: frequently asked questions (FAQs)

Q. How to extract data for free?

Data can be extracted for free using open-source software through manual methods such as web scraping, provided the website’s terms allow it. You can also explore free data extraction tools that offer basic features, which can be ideal for smaller datasets or specific use cases. 

Q. What are the advantages of using data extraction solutions?

Data extraction solutions automate the process of collecting data from various sources, which reduces manual effort and human error. They ensure greater accuracy in data retrieval and can handle complex data formats. These solutions can also scale to accommodate large volumes of data, allowing businesses to extract and process data at a faster rate.

Q. How much does a data extraction tool cost?

Q. How to choose the best data extraction software for my requirement?

Consider factors such as the type of data you need to extract, the sources it will come from (web, database, documents, etc.), and the complexity of the extraction process. You should also evaluate the software's scalability, ensuring it can handle your current and future data volume. Ease of use and integration with existing systems are key considerations, as a user-friendly interface will save time in training and deployment. 

Q. Can data extraction software work with a large volume of data?

Yes, many data extraction tools are designed to handle large datasets by offering batch processing and cloud integration.

Because 'guessing' is so 1990s!

After thoroughly exploring and using the top 10 data extraction tools, I’ve gained valuable insights into the strengths and limitations each offers.

While some excel in user-friendliness and scalability, others shine in handling complex data formats. The key takeaway is that selecting the right tool largely depends on your specific needs, data volume, and budget.

It’s essential to balance ease of use with the ability to handle large datasets or intricate data structures. After all, extracting data shouldn't feel like pulling teeth, even though sometimes it might! 

After extraction, protect your data with the best encryption tools. Secure it today!