Have you identified the optimal ETL (extract, transform, and load) tools for BigQuery to streamline your data handling and construction of data pipelines? This question can be difficult to answer, as it's uncommon to find a single tool that stands out as the best for every task. The key consideration is to select the tools that align well with your needs and those of your team. In the process of selecting the most suitable ETL tools for BigQuery within your organization, begin by evaluating your current data infrastructure and pinpointing any obstacles.
- Do you encounter difficulties when attempting to design intricate workflows in a more visual manner?
- Are teams without engineering expertise capable of utilizing ETL tools effectively?
- Is it possible for you to automate data coordination while implementing top-tier monitoring mechanisms for your ETL workflow?
- Does your chosen ETL tool for BigQuery adhere to security standards and compliance requirements?
At Shipyard, our experience with BigQuery ETL spans a considerable duration, prompting us to explore a variety of tools to ascertain the most dependable options for diverse ETL requirements across companies. Given that some tools share similar functionalities, we have highlighted distinguishing factors to assist you in aligning tool features with your specific needs.
8 BigQuery ETL tools to consider
1. Shipyard
Shipyard serves as a contemporary cloud-native orchestration platform that seamlessly integrates tools, streamlines processes, and establishes a robust data foundation. The platform's adaptable workflow automation features facilitate the creation and refinement of workflows for a broad range of applications. For example, its user-friendly low-code templates enable both technical and non-technical teams to rapidly tailor data processes and execute ETL tasks.
By integrating with GitHub, Shipyard provides continuous version management, streamlined deployment, and current code. Additionally, Shipyard delivers dependable monitoring accompanied by real-time alerts, allowing you to promptly detect and resolve pressing data pipeline challenges before they adversely affect your enterprise. The platform's compatibility with a multitude of data sources enables you to swiftly extract, transform, and load data into your BigQuery warehouse.
Top use case:
Shipyard is designed for individuals seeking adaptability and expandability in their data pipelines. As an essential BigQuery ETL tool, it facilitates team coordination while empowering them to effortlessly scale and tailor their data workflows. Complemented by its array of integrations, user-friendly data manipulation, graphical user interface, and dedicated customer assistance, Shipyard stands as the sole BigQuery ETL tool required for effective data orchestration.
Pros:
- Shipyard provides a rapid setup process and a user-friendly, straightforward interface, facilitating adoption by both seasoned and novice users.
- The platform enables the creation of sophisticated workflow automations through its low-code templates and graphical interface.
- Shipyard is compatible with a diverse range of data sources, including Fivetran, dbt Cloud, Airtable, Amazon S3, spreadsheets, among others.
- It boasts powerful reporting features, allowing you to identify inefficiencies and promptly implement process updates or enhancements. For example, you can monitor the status, duration, and resource consumption of each workflow and task.
- Shipyard delivers precise real-time alerts for critical disruptions while ensuring secure data handling with no data loss.
- The platform allows you to swiftly implement new logic in your data pipelines and scales effectively as your data volume increases.
- Comprehensive documentation and Changelog provide an extensive knowledge repository to assist users in gaining a deeper understanding of the platform.
- Shipyard also provides chat-based assistance and enables users to schedule direct calls with the customer support team.
- Users have access to the API for bulk updating and creating workflows.
- Logs can be exported or stored externally.
- Shipyard features built-in credential management.
Cons:
- The platform lacks ready-made connectors for importing data from software-as-a-service (SaaS) applications.
- It is not possible to host Shipyard independently on your own infrastructure.
Pricing:
- Shipyard offers a free plan—which is great for users who want to test out the platform’s capabilities before switching to it completely.
- Its basic paid plan starts at $50/month and works on a pay-per-use model. As your organization grows and usage increases, the price varies. You can calculate the exact pricing plan for your team here.
2. Matillion
Matillion is a cloud-native ETL platform that facilitates the transfer of data from over 70 data sources to data warehouses such as Snowflake, Amazon Redshift, Google BigQuery, and more. The platform is relatively simple to configure and features an intuitive user interface, making it a convenient choice for data engineers.
While Matillion provides a visual workspace with drag-and-drop functionality, it does necessitate knowledge of SQL, which can restrict its accessibility, particularly for non-engineering users who may wish to leverage it for specific domain-related tasks.
In summary, Matillion ETL is well-integrated with the BigQuery data warehouse, and its scheduling orchestration capabilities enable the creation of workflows when resources become available.
Top use case:
Matillion serves as a viable choice for individuals seeking to transfer and process data from a variety of data sources—encompassing CRMs, ERPs, and social media platforms—into their preferred data warehouse or data lake.
Pros:
- Matillion offers robust integrations with a wide array of cloud-based applications, eliminating the need for additional expenses on new connectors (a common practice with many other tools).
- Users can perform data transformations either by writing custom SQL queries or by designing transformation components through the graphical user interface (GUI).
- The platform is compatible with over 70 data sources, encompassing databases, CRM systems, ERP solutions, and more.
- Customer support is available through an online ticket submission system as well as via telephone assistance.
- A wealth of online resources is readily accessible, enabling teams to swiftly embark on their data transformation journey.
Cons:
- The platform lacks ready-to-use templates, necessitating that users create everything from the ground up, a process that can be time-consuming.
- Support via live chat is not provided.
- Users do not have the capability to independently introduce a new data source or alter an existing one.
Pricing:
- Data loader is free to use, whereas Matillion ETL comes with a 14-day free trial.
- Matillion ETL has three paid plans: Basic, Advanced, and Enterprise. You can check out their detailed pricing here.
3. Fivetran
Fivetran is a widely recognized ETL tool that facilitates the replication of applications, databases, events, and files into high-performance cloud data warehouses. Its user-friendly setup process, which involves linking data sources to destinations, contributes to its reputation as one of the most streamlined and effective ETL tools for BigQuery.
With fully managed connectors, Fivetran's pipelines are automatically and continuously refreshed, allowing you to concentrate on analytical tasks while eliminating the need for laborious, repetitive activities in the ETL workflow.
Fivetran is capable of extracting data from over 5,000 cloud-based applications and offers the flexibility to swiftly incorporate new data sources. It is compatible with sophisticated data warehouses such as Snowflake, Azure, Amazon Redshift, BigQuery, and Google Cloud, enabling you to effortlessly query your data.
Additional features, including real-time monitoring, proven connectors, alert notifications, and detailed system logs, further equip data analysts and data engineers to construct reliable ETL pipelines using Fivetran.
Top use case:
Fivetran is a fitting choice for BigQuery data ETL, particularly for individuals who are embarking on their ETL journey and seeking a tool that offers a swift setup process and user-friendly experience. Additionally, it's an attractive option for enterprises aiming to transfer data from a multitude of sources into data warehouses without encountering undue complications.
Pros:
- The platform features automated data pipelines with uniform schemas.
- No specialized training or bespoke coding is necessary.
- Users can access all their data using SQL queries.
- The platform empowers users to independently integrate new data sources.
- Comprehensive data replication is provided by default.
- Customer assistance is available through a ticket submission system.
Cons:
- The platform does not provide the ability to deploy or utilize services in an on-premises environment.
- The clarity and comprehensiveness of the product documentation could benefit from enhancements.
- Ascertaining the total expense associated with the platform can be somewhat challenging.
Pricing:
- Fivetran offers a 14-day free trial for each of its paid plans.
- It has four paid pricing plans. You can also request for a custom quote if you’re an enterprise that needs access for unlimited users and usage.
- Fivetran also offers a free tier option. You can check it out here.
4. Stitch
Stitch is a cloud-native ETL platform that facilitates the ingestion of data from a variety of SaaS applications and databases, enabling its transfer into data warehouses and data lakes for analysis using business intelligence (BI) tools. With its straightforward setup process and minimal prerequisites, teams can swiftly launch Stitch and begin the data movement process.
Stitch performs only the transformations necessary to ensure compatibility with the target destination, such as flattening nested data and converting data types as needed. Users can define transformations using Python, Java, SQL, or through a graphical user interface.
With connectors available for over 100 databases and SaaS integrations, Stitch supports a wide range of data warehouses, data sources, and data lake destinations. Additionally, users are afforded the flexibility to create and integrate new data sources into the Stitch platform.
Top use case:
Stitch's simplicity and ease of use make it an appealing choice for a variety of teams, including engineering groups such as DataOps and non-engineering departments like marketing. Users can effortlessly manage their ETL processes through the platform's user-friendly interface. Given Stitch's extensive array of integrations, it serves as an apt ETL solution for enterprises that require the ingestion of data from a diverse set of sources.
Pros:
- Stitch is a user-friendly tool that can be swiftly configured even by teams without technical expertise.
- The platform's scheduling functionality facilitates the loading of tables at predetermined intervals.
- Users have the autonomy to independently integrate new data sources into the platform.
- In-app chat assistance is accessible to all customers, while enterprise users can take advantage of phone support.
- Stitch provides extensive documentation, and support service level agreements (SLAs) are obtainable.
Cons:
- The platform is deficient in data transformation capabilities.
- Managing substantial datasets can present challenges and potentially affect overall performance.
- The platform does not provide the ability to deploy or utilize services in an on-premises environment.
Pricing:
- Stitch offers a 14-day free trial and custom paid plans depending on scale.
5. Integrate.io
Integrate.io establishes itself as a data warehouse integration platform specifically tailored for ecommerce businesses. Equipped with a ready-made native BigQuery connector, Integrate.io ranks among the leading ETL tools for BigQuery, boasting compatibility with over 200 data sources. The platform provides code-free solutions, enabling data engineers and data analysts to swiftly implement custom transformation jobs that draw from multiple data sources.
Integrate.io features a user-friendly interface, an abundance of preconfigured functions, and a visual editor that accelerates the package creation process. While the platform excels at SQL-based transformations, it may present some complexities when dealing with JSON or other forms of nested data.
Top use case:
Integrate.io is the favored ETL solution for BigQuery among eCommerce enterprises that manage a multitude of data sources and rely heavily on data-driven decision-making.
Pros:
- Integrate.io provides a built-in connector specifically designed for BigQuery.
- The platform features an intuitive drag-and-drop interface, facilitating ease of use for non-technical users seeking to perform data transformations.
- The platform boasts seamless integration capabilities with a wide array of platforms, databases, applications, and data warehouses, encompassing AWS, Microsoft Azure, Oracle, Salesforce, Amazon Redshift, Tableau, and more.
- Integrate.io places a strong emphasis on data security and regulatory compliance. Data undergoes transformation prior to being loaded into BigQuery to ensure adherence to regulations such as GDPR, HIPAA, CCPA, and others.
Cons:
- The process of debugging can be somewhat labor-intensive, as it requires examining the error log to pinpoint the underlying problem.
Pricing:
- You need to schedule a demo via Calendly to get a custom pricing plan based on your needs.
6. Apache Airflow
Apache Airflow is a widely used open-source BigQuery ETL tool that is available at no cost. It provides users with the ability to monitor, schedule, and manage their workflows through a contemporary web application.
At the heart of Apache Airflow lies the concept of a Directed Acyclic Graph (DAG), where tasks are organized in a specific sequence, with upstream and downstream dependencies established between them to delineate the logical progression of their execution. Visual representations of DAGs and task trees offer valuable insights into the operational dynamics of the DAG.
Airflow pipelines are crafted using the Python programming language, which necessitates that users employ standard Python constructs to design workflows and dynamically produce tasks. For experienced data engineers, this is a welcome feature, as Python's versatility enables users to exercise complete control over workflow construction.
Top use case:
Apache Airflow is an ideal choice for data engineers and data analysts who are routinely involved in crafting sophisticated data pipelines. The platform offers a robust set of features that empower users to design, orchestrate, and manage multi-stage workflows with various dependencies and data transformations, making it a preferred tool for handling complex data processing tasks.
Pros:
- Apache Airflow provides outstanding capabilities for constructing intricate data pipelines.
- The platform delivers comprehensive assistance through Slack.
- Airflow has multiple how-to's and troubleshooting steps written by the community throughout the web.
Cons:
- Apache Airflow's user interface may not be the most intuitive, and it can occasionally feel cumbersome to navigate.
- Proficiency in Python is a prerequisite for using the platform.
- Altering pipelines once they are established can pose challenges.
- The platform is accompanied by comprehensive documentation that warrants meticulous examination and consideration to guarantee that your setup aligns with your specific requirements.
Pricing:
- Apache Airflow ETL is an open-source platform, licensed under Apache License Version 2.0, and is free to use.
7. StreamSets
SteamSets is a cloud-centric, comprehensive ETL solution designed to construct advanced data intake channels, supplying consistent information required for analytical purposes. It offers robust built-in connectors for data intake, simplifying and expediting the creation of data pathways. Additionally, it enables real-time data processing for seamless integration into downstream applications in the desired format, as well as configurable monitoring capabilities. The inclusion of integrated parsers simplifies the handling of large and intricate payloads containing key-value pairs, JSON, and XML.
Top use case:
StreamSets serves as an excellent BigQuery ETL solution for businesses and data engineers dealing with an extensive range of file streaming or data input sources, particularly when working with high volumes.
Pros:
- StreamSets offers a user-friendly interface that promotes open-source development for proof of concept and flexibility.
- The modular design allows for easy integration into your existing infrastructure when needed.
- With its drag-and-drop interface, users can effortlessly perform data transformations such as adding, removing, looking up, and typecasting before loading the data into warehouses or other destinations.
- The platform is highly expandable, enabling users to incorporate and modify data sources independently.
- Compatibility with over 50 data sources is provided, including databases and streaming options like MapR and Kafka.
- Assistance is available through an online ticket submission system and telephone support.
Cons:
- In the updated version, users are required to buy extra components (Control Hub), which necessitates the management, patching, and upgrading of an additional 16 databases, increasing the platform's complexity.
- Navigating through logs and error messages to pinpoint problems can be challenging.
- Live chat support for customers is not provided.
Pricing:
- StreamSets offers a 30-day free trial.
- It offers three pricing plans: Free, Professional, and Enterprise. You can check out more details about these pricing plans here.
8. Etleap
Etleap is a well-known ETL tool for BigQuery, which constructs and oversees data pipelines to transform data for Snowflake and BigQuery. A standout feature of this BigQuery data ETL solution is its capacity to connect to multiple databases or sources of the same kind within the licensed connector, simplifying the process for users.
It can accommodate data from a variety of sources such as corporate databases, log files, sensors, message queues, basic file storage, ERP systems, and more.
Additionally, Etleap's user-friendly interface enables users to effortlessly add or modify new data sources with just one click and apply custom transformations.
Top use case:
Etleap provides both simplicity and robust capabilities for data engineers, enabling them to collect data from various sources and stage it for further processing. Once staged, they can utilize prototypes to develop comprehensive analytical models.
Pros:
- Etleap facilitates data transformations via its GUI and custom SQL, making it simple for users to coordinate and plan data pipelines.
- Creating connectors with Etleap is a breeze, as there's no need to delve into coding.
- The platform provides data pipeline monitoring through its user-friendly dashboard.
- With support for over 50 data sources, including SaaS, databases, files, BI tools, and event streams, Etleap has you covered.
- Top-notch in-app customer chat support is also available for assistance.
Cons:
- Users are unable to independently add or modify data sources.
- Etleap falls short in providing comprehensive documentation or a knowledge base for users to acquaint themselves with the platform.
Pricing:
- Etleap offers a 30-day free trial after a demo with the sales team.
- There are no pricing options available on the company website. You have to get in touch with the team or request a demo to learn more.
Final thoughts
With a vast array of BigQuery ETL tools on the market, the sheer number of options can make it daunting and even a bit overwhelming to pick the perfect one for you. That's why it's best to keep it simple.
If you're searching for a user-friendly yet robust BigQuery data ETL tool to streamline your ETL processes, Shipyard might be the right fit for you.
Alternatively, if you're just dipping your toes into the world of ETL tools, Fivetran is a solid choice. Just be aware that it could feel limiting as your data pipeline requirements expand.
Should you have any inquiries about ETL tools or data pipelines, don't hesitate to reach out to our team. We're here to help you pinpoint precisely what you need.
