6 Best Data Extraction Tools for 2023 (Pros, Cons, Best for)

The 3 main problems a data extraction tool will solve for you

Data extraction is the first step of the ETL process. You Extract data from various sources, then you Transform the relevant data (clean, validate, and test it), and finally, you Load the data into a destination where it can be used for data analysis (business intelligence tools, data warehouses, …).

The data extraction process affects all the downstream processes. If the data extraction process fails, you’re not going to have good data to use for business decisions down the line. 

How can the data extraction process fail?

1. Manual data entry is time-consuming. Entering the data yourself (for example in a spreadsheet) instead of using scripts takes too much time. And it is more likely to introduce errors.
2. The method you use to extract information does not scale with incoming data. If you have a home-brewed script to extract data, it can break down when the volume, speed, or variety of incoming data increases. 
3. The data source changes. When calling a social media API (e.g. Facebook Advertising API or Shopify e-commerce API), the API provider can change the API behavior (different endpoints, different throttling limits, …). You either need to constantly adjust your own solution (correct scripts you’ve written) or your extraction will fail.

A good data extraction tool has built-in safeguards to prevent all these problems. It automates data extraction via code so there is no manual entry. It automatically scales with incoming data. And the data extraction software provider adjusts the script when the data source changes, so you don’t have to waste time maintaining the tool.

The 6 best data extraction tools 

1. Keboola

Keboola is a data platform as a service that helps you automate all your data operations. 

Its core feature is to build and automate ETL, ELT, and reverse ETL pipelines. The “extract” features within the pipelines help you collect data from data sources at scale.

Keboola offers multiple ways to extract data. You can use the no-code solution, such as the Visual Flow Builder which lets you drag-and-drop components that extract, transform, and load data for you. 

Or you can use a fully coded solution geared towards developers, where you can tinker with Python, SQL, R, Julia, or CLI tools to create your data pipelines.

Pros:

• The pre-built Extractors in Keboola are one of the largest collections of data sources so you can automate extraction with a couple of clicks: Social media applications (Facebook Ads, Facebook Pages, Google Ads, Linkedin Ads, …), e-commerce data (Shopify, Woocommerce, …), sales and communication data (Salesforce, Mailchimp, …), files (CSV, JSON, XML, Excel, Google Sheets, …), databases, data lakes, and data warehouses (PostgreSQL, MySQL, Amazon Redshift, Snowflake, Microsoft Azure, Google BigQuery, …), unstructured data from S3 buckets, and many, many more (check the full list of 250+ pre-built connectors here).

• If there is no pre-built connector, you can use the Generic Extractor that can collect data from any API-like source.
• Data extraction can be automated via Orchestrators and Webhooks.
• Get notified if a data extraction fails. Continuous monitoring and extensive logs help you inspect how your extraction pipelines failed.
• Keboola is ideal for big data workflows. With the batch processing functionality, Keboola can ingest large amounts of data and scale seamlessly.
• Extensive data use cases beyond ETL: enterprise data security for every business size, data governance and master data management, DataOps (development branches and versioning, CDC to speed up operations, CLI), Data Catalog for data sharing, and machine learning toolbox to build advanced AI projects.

Cons:

• Keboola offers near real-time data integration. Orchestrators can trigger data extraction every 1 minute and webhooks can be used for almost instantaneous data collection. But Keboola is not a data streaming service and does not offer continuous data extraction.

Best for: teams of technical data experts (scientists, engineers, analysts) and data-driven business experts who would like to extract the data to drive business opportunities. 

Keboola offers an always-free tier so you can test the platform and build data pipelines without any risks.

2. Improvado

Improvado is an ETL platform that focuses on streamlining data extraction, transformation, and load for marketing and sales use cases. 

The main use case Improvado offers is their team building end-to-end analytic pipelines that give you insights into how your marketing and sales initiatives are performing.

Pros:

• Wide area of marketing and sales data sources covered.
• If you need a custom integration and there is no existing component in Improvado, the team will build one for you.
• As part of onboarding,  they will set up the entire ETL ecosystem for you. This could be a plus (no work needed) or a minus (no self-service ability).

Cons:

• The use cases are limited to marketing and sales. If you need data for a wider business use case, Improvado is not a good fit.
• No self-service ability. This is a bespoke solution that does not offer much customizability.
• Hard to test. Improvado is not transparent about pricing, nor does it make it easy to try and test the platform.

Best for: The marketing and sales teams who are willing to pay a bit extra for a bespoke analytic solution that does everything out of the box.

3. Fivetran

Fivetran is an ETL tool that specializes in driving data integrations for business users who are looking for a no-code solution to build data insights from their data.

Fivetran focuses on getting data to you, specializing in the Extract and Load parts of the data pipeline. It is not as strong in the Transform component, but that is compensated by offering 190+ connectors to get the E and L jobs done.

Pros:

• Scales well with large amounts of incoming data.
• Intuitive and easy-to-use tool.
• Especially good for data source replication. 

Cons:

• There is no generic extractor. If Fivetran does not offer an extractor for your use case, you’ll not be able to extract data. Make sure to match your data needs against Fivetran’s connectors before you lock in.
• Transformations are only possible pre-load (so the ELT architecture).
• The tool can be pricier than its competitors. In Fivetran you get charged based on the number of rows processed. This price can accumulate in data pipelines and you can get charged multiple times for the same processes: once they extract data, when it is used in transformations, and when it is written into a data source.
• Fivetran specializes in ELT and doesn’t have a wide range of data operation capabilities. Fivetran customers need additional tools if they want a data catalog or advanced orchestrations. Its customizability of connectors is also limited.
• Fivetran cannot be used to reverse ETL (sending data to apps).

Best for: Business experts who want to ingest large amounts of data, but do not need a tool to customize, transform, or analyze complex data pipelines.

Recommended read: Fivetran alternatives

4. Hevo Data

Hevo Data is a no-code cloud-based ETL tool that simplifies building ETL pipelines for the business expert who does not want to code.

Hevo Data is quite versatile and allows you to build ETL, ELT, and reverse ETL pipelines. 

Pros:

• Covers a wide area of data sources you would typically find in a fast-growing startup.
• If a data source does not have a pre-built extractor, you can use Hevo’s API or webhooks extractors to collect data.
• Alerts for failed workflows.
• Great for ETL processes involving replication - leverage CDC to speed up data copying. 

Cons:

• The data sources you can extract from in the freemium tier are limited in number. In the freemium model, you’ll be able to extract mostly sources from Finance and Project management SaaS applications.
• Overall, Hevo Data does not cover as many data sources as its competitors. 

Best for: The startup business user who would like to simplify data extraction from SaaS applications and use the ETL tool to build no-code data pipelines.

5. Dataddo

Dataddo is a fully managed ETL service that takes data from apps/storages and sends them to apps/storages.

You can use Dataddo to extract data from say Facebook Ads and send them to your Snowflake data warehouse.

Pros:

• Data can be extracted and sent directly to your business intelligence tool.
• Fully managed service. Dataddo takes care of all connectors, maintains connectors when they break, and builds new connectors on request.

Cons:

• New extractors are built only for paying clients. The free version offers only pre-built connectors and no “build it yourself” option.
• The free product version offers only 3 data flows. A data flow in Dataddo offering is a connection between source and destination. For example, when you extract data from Facebook Ads and send the data to Google Spreadsheet that counts as 1 flow. 
• Transformations are done while extracting data and are a bit cumbersome to perform (you need to specify the JSON of how data is to be extracted).

Best for: Non-technical user who would like to integrate data from applications into their business intelligence tools and does not need a lot of transformations.

6. Domo

Domo Business Cloud is a proprietary cloud-based SaaS that helps you integrate your data across disparate sources and build ETL pipelines.

Domo Business Cloud acts as an intermediary between your data sources and your data destination (data warehouse) and helps you extract data from the former and load it into the latter.

Pros:

• Over 1,000 pre-built connectors help you extract data.
• Domo can operate between different cloud vendors (AWS, GCP, Microsoft, …) and on-premise deployments.
• ETL pipelines can be built using no-code visualization wizards or with SQL code within the dashboard.

Cons:

• Pricing is steep, or “enterprise-grade”. You will have to contact sales to get a quote since pricing models are tailored to each customer. 
• Domo is feature-packed so it can be overwhelming and not easy to use.
• Some customers complain that the moment you start customizing the scripts and move out of the pre-built automated extractions, Domo breaks down and does not function properly.

Best for: Enterprise users who would like to make Domo their main data cloud provider for data extraction and integration.

All 6 tools are strong competitors for being the right choice. So how do you pick a winner?

How to choose the right data extraction tool for your organization

Keep these 5 criteria in mind when choosing the best data extraction tool for your company:

1. Pricing. Figure out if the total tool price (fees, licenses, …) outweighs the opportunity cost of building data extractions yourself (time waste, maintenance, bug chasing, …)
2. Data source coverage. Not all tools offer the same number and types of data extraction connectors. Check which sources are covered by the tool and match them against your data needs.
3. Universal extractors. Verify the tool offers features or connectors that can be used for extracting data from any source in case no pre-built extractors are available.
4. Target audience. Is the tool developed for engineers (who would benefit from low-code extractors), business experts (who need no-code extractors), or - preferably - both?
5. Ease of use. How intuitive and user-friendly is the tool? Do you need to pick up a new language or get certified before you can access its inner workings?

The above criteria cover the most common “gotchas” when picking the best extraction tool. But there are also more subtle considerations to keep in mind.

Pro tips for the data engineer

Additionally, if you’re a data engineer, also check if the chosen tool offers:

1. Scalability. Does the tool use CDC, batch processing, and parallelization to speed up extraction and can scale with increases in data volume, speed, and variety?
2. Alerts and monitoring. Can you set up observability with the data extraction tool to keep an eye on pesky bugs and assert data quality?
3. Feature extendibility. Does the tool offer more than just extraction features?

Choose Keboola and extract data faster at a fraction of the costs

Keboola is a crowd favorite for a reason.

It has a fair-pricing model that doesn’t require mental gymnastics for you to understand how much your consumption is going to cost you.

Over 250+ connectors practically guarantee you will find all the extractors you will ever need - but just in case - Keboola also has a Generic Extractor that can collect data from any API-like source. This means you will never have to waste precious time on manually writing code for yet another data source the team brought up.

Keboola offers a feature-rich and intuitive platform for all your ETL needs.

Oh, and did we mention the always-free tier with no credit card requirements?

Try Keboola today and start extracting data for free.

Data extraction vs web scraping (was this blog not what you were looking for?) 

Data extraction is often confused with web scraping. Data extraction refers to all the methods of collecting data, be it via web page parser that transforms an HTML website into a more friendly format (a.k.a, web scraping), calling API endpoints to collect data from a SaaS application, creating webhooks that extract data when an event is triggered, or any other method.

This article will focus on tools that automate data collection in general. Looking for web scraping tools? Check these solutions:

1. Import.io - great for e-commerce. It parses web pages for product rankings, descriptions, and reviews. 
2. Octoparse - strong tool for web parsing in general. Offers features such as click-to-parse, automated IP proxies to hide your identity, etc.
3. Parsehub - cloud-based IP parser that allows you to click-and-select points of interest on a website and download the information into JSON, CSV, Excel, or Google Sheets files.
4. OutWithub - is a bit old school, but it offers many interesting features not found in other providers, like brand monitoring, social media parsing, etc.
5. Web Scraper - implemented as a Chrome extension or Firefox add-on, so you can scrape while you browse the internet.
6. Mailparser - focuses on extracting information from emails.
7. DocParser - web-based document parser that uses technology like OCR to extract information from documents like invoices, purchase orders, bank statements, etc.

No matter the web parser you choose, Keboola can help you automate web data extraction beyond the first data collection phase. 

Check how we did it on an actual business use case for scraping competition.