Businesses need a centralized pool of data for proper and in-depth analysis to make informed decisions. Data extraction assists businesses in extracting information from various sources, which ultimately helps businesses perform better analytics in today's data-driven landscape.
In this article, we’ll understand what data extraction is, the different types of data extraction, and the top 10 tools to consider for performing data extraction.
What is Data Extraction?
Data extraction involves gathering diverse data from various sources. This process enables data aggregation, processing, and refinement, facilitating storage in a centralized location for analytics or backup requirements. This storage can be on-site, in the cloud, or a combination of both.
Data extraction serves as the initial phase in both ETL (extract, transform, load) and ELT (extract, load, transform) workflows, which, in turn, form integral components of comprehensive data integration strategies.
Data extraction offers swift insights, aiding timely actions for business queries.
Types of Data Extraction
Data extraction utilizes automation technologies to simplify manual data entry, and it can be categorized into three main types:
Full Extraction
When transferring data from one system to another, a full extraction involves pulling all available data from the original source and sending it to the destination. This method is ideal for populating a large system for the first time, ensuring a comprehensive one-time collection of all necessary data. While it may have potential pitfalls and overhead costs, it simplifies future processes and ensures accuracy when analyzing critical data.
Incremental Stream Extraction
Incremental stream extraction involves capturing only the data that has changed since the last extraction. This approach enhances efficiency by saving time, bandwidth, computing resources, and storage space, as redundant data is not transferred during each complete record extraction. The advantage lies in keeping systems continuously updated with minimal effort, contributing to the accuracy of data sharing.
Incremental Batch Extraction
Incremental batch extraction is employed when dealing with large datasets that cannot be processed in one go. This method entails breaking the datasets into smaller chunks and extracting data from them separately, allowing greater efficiency in handling extensive information. Instead of waiting for a single large file to complete processing, multiple smaller files can be processed quickly. This approach minimizes resource strain and facilitates easier batch management.
What are Data Extraction Tools?
Data extraction tools are designed to extract raw data from different sources such as databases, websites, documents, or APIs, and transfer it to another location. The extracted data can be replicated or copied to a destination, like a data warehouse or storage system. These tools can handle various types of data, whether structured, unstructured, or poorly organized. Once the data is collected, it can be processed and refined for analysis or storage.
These tools play a vital role in data management and analytics by simplifying the gathering and organizing of large volumes of data from diverse sources. They help businesses and organizations streamline their data collection processes and make informed decisions based on accurate and up-to-date information.
Why Do You Need Data Extraction Tools?
Efficient Data Retrieval
Data extraction tools streamline data retrieval from various sources, automating repetitive tasks and conserving time and resources.
Improved Data Accuracy
These tools ensure precise data extraction, minimizing manual errors and standardizing extraction processes to reduce discrepancies.
Real-time Insights
Data extraction tools facilitate real-time data retrieval, empowering timely decision-making. Businesses gain access to current information for enhanced analysis and planning.
Data Integration
These tools seamlessly integrate data from diverse sources, ensuring consistency across systems and databases.
Scalability
Data extraction tools are scalable, proficient in managing large data volumes, and adaptable to evolving business needs.
Web scraping
Data extraction tools play a crucial role in streamlining the process of collecting data from websites, databases, and other digital platforms. Web scraping is another use case that data extraction tools solve, enabling users to effortlessly extract relevant data from websites for analysis, research, or monitoring purposes.
10 Data Extraction Tools
While you perform data extraction, your tools are directly related to the output and efficiency. Here are some of the best data extraction tools to ensure maximum efficiency.
#1 Airbyte
Airbyte stands out as a prominent platform streamlining the data extraction process. It presents a centralized hub equipped with an extensive library of pre-built connectors, facilitating the extraction and delivery of data to various destinations such as data warehouses, data lakes, and databases.
Some key features of Airbyte are:
- Airbyte operates as an open-source data integration platform.
- With over 300 pre-built connectors, Airbyte provides you with a wide range of selection. It facilitates seamless data extraction from diverse sources.
- The versatile data extraction tool supports both incremental and full extraction methods.
- You can craft custom connectors tailored to your specific data environments and use cases.
- Airbyte offers an API for simplified integrations with numerous customer-facing applications.
#2 Talend
Talend Data Fabric unifies data integration, data quality, and data governance within a singular, low-code platform that seamlessly interacts with a wide variety of data sources and architectures. With Talend, you can save engineering time by extracting data from 140+ popular sources to your warehouse or database in minutes.
Some of the key features of Talend are:
- Addresses end-to-end data management requirements throughout the organization, encompassing integration and delivery.
- Offers versatile deployment options, including on-premises, cloud, multi-cloud, or hybrid-cloud configurations.
- Delivers consistent and predictable value, prioritizing security and compliance.
- Centralize business data into your cloud data warehouse for fresh, analysis-ready data.
- Stitch pipelines automatically and continuously update, allowing you to focus on insights instead of IT maintenance.
#3 Matillion
Matillion’s cloud-based ETL software seamlessly integrates with a wide range of data sources, efficiently ingests data into leading cloud data platforms for utilization by analytics and business intelligence tools. Along with swift movement, it also facilitates the transformation and orchestration of data pipelines. Matillion serves as the go-to data extraction platform for data teams, enhancing efficiency for both coders and non-coders.
Some of the key features of Matillion are:
- Establish connections to virtually any data source with a broad array of pre-built connectors.
- Construct advanced data pipelines or workflows using an intuitive low-code/no-code GUI.
- Develop your own connectors effortlessly in minutes or access customer-created ones from the Matillion community.
- Shorten the time to see results with quicker deployment.
#4 Integrate.io
Integrate.io offers a comprehensive set of tools that enable businesses to consolidate all their data for a unified source of insights. What sets this tool apart is its exceptional user-friendliness. Even non-technical users can easily build data pipelines with a drag-and-drop editor and many built-in connectors. Businesses can also use Integrate.io to extract data from in-house tools, utilizing its robust expression language, advanced API, and webhooks.
Some of the key features of Integrate.io are:
- Low-code transformation by Integrate.io for swift integration with warehouses or databases.
- Utilize reverse ETL to push warehouse data back to in-house tools, enhancing insights into the customer journey for improved marketing and sales operations.
- Ensure data value with Integrate.io’s observability features, setting up alerts for instant awareness of data issues.
- You can choose from more than 220 low-code transformation possibilities for your data.
#5 Hevo Data
Hevo Data is a no-code, bi-directional data pipeline platform designed specifically for modern ETL, ELT, and Reverse ETL requirements. Tailored to assist data teams, it facilitates the streamlined automation of organization-wide data flows. This leads to significant time savings for engineering tasks per week and enabling faster reporting, analytics, and decision-making processes.
Some of the key features of Hevo are:
- Hevo automatically synchronizes data from over 150 sources, including SQL, NoSQL, and SaaS applications.
- Hevo streamlines data operations by automatically detecting and mapping data schemas to the destination. This eliminates the tedious task of schema management.
- Hevo’s simple and interactive UI ensures an easy learning curve for you, facilitating seamless navigation and operations.
- Efficient bandwidth utilization is achieved through Hevo’s capability to transfer real-time modified data.
#6 Stitch
Stitch is a fully managed, lightweight ETL tool focusing on data extraction from over 130 sources. While lacking some advanced transformation features, it excels in simplicity and accessibility, making it ideal for small to medium-sized businesses. With robust security measures, including SOC 2 and HIPAA compliance, Stitch ensures data integrity throughout the extraction process.
Key Features:
- ETL functionality for data extraction.
- Offers 130+ pre-built integrations.
- Provides enterprise-grade security measures.
#7 Fivetran
Fivetran is an ELT platform offering over 300 built-in connectors for rapid data extraction from diverse sources. It supports real-time data replication and allows custom cloud functions for advanced extraction needs. Although it transforms data after loading, its automation capabilities streamline the extraction process.
Key Features:
- ETL functionality with 300+ built-in connectors.
- Automated schema drift handling and customization.
#8 Improvado
Improvado focuses on extracting data from marketing and sales platforms, offering over 300 pre-built connectors for efficient data pipeline creation. It allows for the definition of universal templates and offers data transformation capabilities for custom reporting metrics.
Key Features:
- ETL functionality specializing in marketing and sales data extraction.
- Provides 300+ pre-built connectors for seamless integration.
- Offers customizable dashboards and reports.
#9 Informatica
Informatica caters mainly to large enterprises with its robust ETL/ELT capabilities and thousands of connectors. It provides comprehensive tools for data integration and transformation, supported by a user-friendly interface and extensive documentation.
Key Features:
- Offers both ETL and ELT functionality.
- Thousands of connectors for extensive data integration.
- Enterprise-grade extraction capabilities.
#10 SAS Data Management
SAS Data Management is a comprehensive solution for managing and integrating data from various sources. It allows for seamless access, extraction, transformation, and loading of data into unified environments. Its key strength lies in reusable data management rules, ensuring consistent data quality and governance.
Key Features:
- Provides ETL/ELT capabilities.
- Offers out-of-the-box SQL-based transformations.
- Integrates with any data source, including cloud and legacy systems.
Conclusions
Without top extraction tools, the proper gathering of data might not be of the best standards. These 8 tools can be game changers for your business in today’s day and age. These tools can solve data overload issues and extract material from various sources, eventually simplifying the complex task of extracting valuable information.
In addition to simplifying data extraction, these top tools offer customizable features tailored to specific business needs. These tools can streamline the extraction process, ensuring that the data gathered empowers businesses to make informed decisions swiftly and efficiently.
Data Extraction FAQs
- What are the benefits of using data extraction tools?
Data extraction tools streamline the process of retrieving data from various sources, saving time and effort. They improve data accuracy, facilitate real-time insights, and enhance decision-making capabilities.
- How do I choose the right data extraction tool for my business needs?
Consider factors like data source compatibility, scalability, ease of use, security features, and pricing when selecting a data extraction tool. Evaluate trial versions or demos to ensure they meet your specific business requirements.
- Can data extraction tools handle large volumes of data?
Top data extraction tools are equipped to handle large volumes of data efficiently. They often offer features like parallel processing, data partitioning, and optimized algorithms to ensure fast and reliable extraction.
- Is it possible to schedule automated data extraction processes?
Many data extraction tools support automated scheduling, allowing users to set up regular extraction jobs without manual intervention. This feature enhances productivity and ensures data is always up-to-date.
- How secure is the data extraction process, especially when dealing with sensitive information?
Data extraction tools prioritize data security, offering encryption, access controls, and compliance with regulations like GDPR. Choose tools with robust security measures to safeguard sensitive information throughout the extraction process.
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
What is ETL?
ETL (Extract, Transform, Load) is a process used to extract data from one or more data sources, transform the data to fit a desired format or structure, and then load the transformed data into a target database or data warehouse. ETL is typically used for batch processing and is most commonly associated with traditional data warehouses.
What is ELT?
More recently, ETL has been replaced by ELT (Extract, Load, Transform). ELT Tool is a variation of ETL one that automatically pulls data from even more heterogeneous data sources, loads that data into the target data repository - databases, data warehouses or data lakes - and then performs data transformations at the destination level. ELT provides significant benefits over ETL, such as:
- Faster processing times and loading speed
- Better scalability at a lower cost
- Support of more data sources (including Cloud apps), and of unstructured data
- Ability to have no-code data pipelines
- More flexibility and autonomy for data analysts with lower maintenance
- Better data integrity and reliability, easier identification of data inconsistencies
- Support of many more automations, including automatic schema change migration
Here is our recommendation for the criteria to consider:
- Connector need coverage: does the ETL tool extract data from all the multiple systems you need, should it be any cloud app or Rest API, relational databases or noSQL databases, csv files, etc.? Does it support the destinations you need to export data to - data warehouses, databases, or data lakes?
- Connector extensibility: for all those connectors, are you able to edit them easily in order to add a potentially missing endpoint, or to fix an issue on it if needed?
- Ability to build new connectors: all data integration solutions support a limited number of data sources.
- Support of change data capture: this is especially important for your databases.
- Data integration features and automations: including schema change migration, re-syncing of historical data when needed, scheduling feature
- Efficiency: how easy is the user interface (including graphical interface, API, and CLI if you need them)?
- Integration with the stack: do they integrate well with the other tools you might need - dbt, Airflow, Dagster, Prefect, etc. - ?
- Data transformation: Do they enable to easily transform data, and even support complex data transformations? Possibly through an integration with dbt
- Level of support and high availability: how responsive and helpful the support is, what are the average % successful syncs for the connectors you need. The whole point of using ETL solutions is to give back time to your data team.
- Data reliability and scalability: do they have recognizable brands using them? It also shows how scalable and reliable they might be for high-volume data replication.
- Security and trust: there is nothing worse than a data leak for your company, the fine can be astronomical, but the trust broken with your customers can even have more impact. So checking the level of certification (SOC2, ISO) of the tools is paramount. You might want to expand to Europe, so you would need them to be GDPR-compliant too.
1. Airbyte
Airbyte is the leading open-source ELT platform, created in July 2020. Airbyte offers the largest catalog of data connectors—350 and growing—and has 40,000 data engineers using it to transfer data, syncing several PBs per month, as of June 2023. Major users include brands such as Siemens, Calendly, Angellist, and more. Airbyte integrates with dbt for its data transformation, and Airflow/Prefect/Dagster for orchestration. It is also known for its easy-to-use user interface, and has an API and Terraform Provider available.
What's unique about Airbyte?
Their ambition is to commoditize data integration by addressing the long tail of connectors through their growing contributor community. All Airbyte connectors are open-source which makes them very easy to edit. Airbyte also provides a Connector Development Kit to build new connectors from scratch in less than 30 minutes, and a no-code connector builder UI that lets you build one in less than 10 minutes without help from any technical person or any local development environment required..
Airbyte also provides stream-level control and visibility. If a sync fails because of a stream, you can relaunch that stream only. This gives you great visibility and control over your data.
Data professionals can either deploy and self-host Airbyte Open Source, or leverage the cloud-hosted solution Airbyte Cloud where the new pricing model distinguishes databases from APIs and files. Airbyte offers a 99% SLA on Generally Available data pipelines tools, and a 99.9% SLA on the platform.
2. Fivetran
Fivetran is a closed-source, managed ELT service that was created in 2012. Fivetran has about 300 data connectors and over 5,000 customers.
Fivetran offers some ability to edit current connectors and create new ones with Fivetran Functions, but doesn't offer as much flexibility as an open-source tool would.
What's unique about Fivetran?
Being the first ELT solution in the market, they are considered a proven and reliable choice. However, Fivetran charges on monthly active rows (in other words, the number of rows that have been edited or added in a given month), and are often considered very expensive.
Here are more critical insights on the key differentiations between Airbyte and Fivetran
3. Stitch Data
Stitch is a cloud-based platform for ETL that was initially built on top of the open-source ETL tool Singer.io. More than 3,000 companies use it.
Stitch was acquired by Talend, which was acquired by the private equity firm Thoma Bravo, and then by Qlik. These successive acquisitions decreased market interest in the Singer.io open-source community, making most of their open-source data connectors obsolete. Only their top 30 connectors continue to be maintained by the open-source community.
What's unique about Stitch?
Given the lack of quality and reliability in their connectors, and poor support, Stitch has adopted a low-cost approach.
Here are more insights on the differentiations between Airbyte and Stitch, and between Fivetran and Stitch.
Other potential services
Matillion
Matillion is a self-hosted ELT solution, created in 2011. It supports about 100 connectors and provides all extract, load and transform features. Matillion is used by 500+ companies across 40 countries.
What's unique about Matillion?
Being self-hosted means that Matillion ensures your data doesn’t leave your infrastructure and stays on premise. However, you might have to pay for several Matillion instances if you’re multi-cloud. Also, Matillion has verticalized its offer from offering all ELT and more. So Matillion doesn't integrate with other tools such as dbt, Airflow, and more.
Here are more insights on the differentiations between Airbyte and Matillion.
Airflow
Apache Airflow is an open-source workflow management tool. Airflow is not an ETL solution but you can use Airflow operators for data integration jobs. Airflow started in 2014 at Airbnb as a solution to manage the company's workflows. Airflow allows you to author, schedule and monitor workflows as DAG (directed acyclic graphs) written in Python.
What's unique about Airflow?
Airflow requires you to build data pipelines on top of its orchestration tool. You can leverage Airbyte for the data pipelines and orchestrate them with Airflow, significantly lowering the burden on your data engineering team.
Here are more insights on the differentiations between Airbyte and Airflow.
Talend
Talend is a data integration platform that offers a comprehensive solution for data integration, data management, data quality, and data governance.
What’s unique with Talend?
What sets Talend apart is its open-source architecture with Talend Open Studio, which allows for easy customization and integration with other systems and platforms. However, Talend is not an easy solution to implement and requires a lot of hand-holding, as it is an Enterprise product. Talend doesn't offer any self-serve option.
Pentaho
Pentaho is an ETL and business analytics software that offers a comprehensive platform for data integration, data mining, and business intelligence. It offers ETL, and not ELT and its benefits.
What is unique about Pentaho?
What sets Pentaho data integration apart is its original open-source architecture, which allows for easy customization and integration with other systems and platforms. Additionally, Pentaho provides advanced data analytics and reporting tools, including machine learning and predictive analytics capabilities, to help businesses gain insights and make data-driven decisions.
However, Pentaho is also an Enterprise product, so hard to implement without any self-serve option.
Informatica PowerCenter
Informatica PowerCenter is an ETL tool that supported data profiling, in addition to data cleansing and data transformation processes. It was also implemented in their customers' infrastructure, and is also an Enterprise product, so hard to implement without any self-serve option.
Microsoft SQL Server Integration Services (SSIS)
MS SQL Server Integration Services is the Microsoft alternative from within their Microsoft infrastructure. It offers ETL, and not ELT and its benefits.
Singer
Singer is also worth mentioning as the first open-source JSON-based ETL framework. It was introduced in 2017 by Stitch (which was acquired by Talend in 2018) as a way to offer extendibility to the connectors they had pre-built. Talend has unfortunately stopped investing in Singer’s community and providing maintenance for the Singer’s taps and targets, which are increasingly outdated, as mentioned above.
Rivery
Rivery is another cloud-based ELT solution. Founded in 2018, it presents a verticalized solution by providing built-in data transformation, orchestration and activation capabilities. Rivery offers 150+ connectors, so a lot less than Airbyte. Its pricing approach is usage-based with Rivery pricing unit that are a proxy for platform usage. The pricing unit depends on the connectors you sync from, which makes it hard to estimate.
HevoData
HevoData is another cloud-based ELT solution. Even if it was founded in 2017, it only supports 150 integrations, so a lot less than Airbyte. HevoData provides built-in data transformation capabilities, allowing users to apply transformations, mappings, and enrichments to the data before it reaches the destination. Hevo also provides data activation capabilities by syncing data back to the APIs.
Meltano
Meltano is an open-source orchestrator dedicated to data integration, spined off from Gitlab on top of Singer’s taps and targets. Since 2019, they have been iterating on several approaches. Meltano distinguishes itself with its focus on DataOps and the CLI interface. They offer a SDK to build connectors, but it requires engineering skills and more time to build than Airbyte’s CDK. Meltano doesn’t invest in maintaining the connectors and leave it to the Singer community, and thus doesn’t provide support package with any SLA.
Once you've set up both the source and destination, you need to configure the connection. This includes selecting the data you want to extract - streams and columns, all are selected by default -, the sync frequency, where in the destination you want that data to be loaded, among other options.
And that's it! It is the same process between Airbyte Open Source that you can deploy within 5 minutes, or Airbyte Cloud which you can try here, free for 14-days.
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
Frequently Asked Questions
What is ETL?
ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.
This can be done by building a data pipeline manually, usually a Python script (you can leverage a tool as Apache Airflow for this). This process can take more than a full week of development. Or it can be done in minutes on Airbyte in three easy steps: set it up as a source, choose a destination among 50 available off the shelf, and define which data you want to transfer and how frequently.
The most prominent ETL tools to extract data include: Airbyte, Fivetran, StitchData, Matillion, and Talend Data Integration. These ETL and ELT tools help in extracting data from various sources (APIs, databases, and more), transforming it efficiently, and loading it into a database, data warehouse or data lake, enhancing data management capabilities.
What is ELT?
ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.
Difference between ETL and ELT?
ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.