BigQuery is a powerful tool for data analytics and business intelligence, but it isn't very easy to get the most out of it without some help from ETL (extract, transform, load). In this blog post, we'll go over some of the best ETL solutions for BigQuery to help you make sense of your company's data.

Table of Contents

  1. What is BigQuery and How Does It Work?
  2. BigQuery for Data Analytics
  3. BigQuery for Business Intelligence
  4. ETL Tools Help You Get the Most out of BigQuery
  6. Talend
  7. Alooma
  8. Apache Airflow
  9. Hevo Data
  10. Apache Spark
  11. IBM Datastage
  12. Apache Nifi
  13. Benefits of using
  14. Conclusion

What is BigQuery and How Does It Work?

BigQuery is an enterprise data warehouse that helps users uncover insights within their business data. Here are some of the things BigQuery does:

  • Stores data in Google's infrastructure
  • Offers a REST API for easy integration with existing applications and tools
  • Provides super-fast performance by using data processing machinery built into the Google Cloud Platform
  • Gives you access to a massive data storage and processing system that can handle the most complex of queries

BigQuery for Data Analytics

Data analytics is an integral part of any successful business. With the ability to analyze data from internal and external sources, companies can gain valuable insights into their customers' habits. BigQuery is a powerful tool for data analytics, as it provides fast access to critical business information.

BigQuery for Business Intelligence

Business Intelligence (BI) tools allow companies to monitor the performance of various departments in their organization by providing reports on how they're doing compared to other teams or on their progress in achieving pre-established goals.

Some big data solutions specifically help users understand and manage all types of data collected from a company's multiple data sources. These tools also deliver real-time analytics, so managers can make decisions regarding sales targets or client needs based on up-to-date information rather than on guesswork.

ETL Tools Help You Get the Most out of BigQuery 

When you use BigQuery, it's essential to have the right tools for the job. Businesses often end up spending more time preparing data than analyzing it because they don't have access to all of their internal and external sources within one platform. ETL products take care of much-needed tasks, including:

  • Data cleansing and validation
  • Schema mapping
  • Managing database schema updates over time as new data comes into play — allows companies to stay competitive, even when faced with an ever-changing marketplace
ETL tools also let users who aren't skilled in data analytics leverage big data through easy-to-use dashboards that enable them to see the metrics they need in real-time.

Some of the best ETL tools for BigQuery are: is a comprehensive ETL tool designed to work with BigQuery, but you can also use it with other data warehouse platforms. It comes with an easy-to-use interface and permits users to create complex pipelines using a simple drag-and-drop feature. 


Talend is a popular big data tool that works with all leading data management platforms, including Hadoop, NoSQL, and SQL. It includes an ETL module that you can also use to integrate data warehouses like BigQuery and other cloud-based applications, such as Salesforce and Google Analytics.


Alooma works very well with BigQuery and other data warehouse platforms. Its unique approach to integrating different sources of information makes it another popular choice.

Apache Airflow

Apache Airflow is an open-source project still under active development. It writes Apache Airflow operators for BigQuery so users who already have experience working with SQL databases and writing code in Python, Java, or C++ can create their own pipelines without having to deal too much with the actual code. Coding can be a time-consuming task, although it lets developers take complete control of all stages of the ETL process.

Hevo Data

Hevo Data works explicitly with data warehouses like BigQuery. It allows users to easily create complex data pipelines by defining their workflows using simple YAML files. Hevo's platform includes an extensive documentation library and access to webinars, as well as a chat assistant that can help you whenever you need it.

Apache Spark

While Apache Spark is known mainly for its data analytics capabilities, it has a BigQuery connector available through the Data Source API so you can query your datasets. It also features an ETL module you can use in conjunction with other Apache services such as Sqoop and Flume.

IBM Datastage

Datastage fits nicely with BigQuery and other significant data warehouse platforms. It's been around for a while now. However, it still has the power to compete with newer tools on the market because of its ability to integrate information from multiple sources into one place. This lets you analyze the metrics you need without having too much to do with the coding.

Apache NiFi

Apache Nifi is another open-source project still under development. It's a dataflow engine that you can use with BigQuery to create pipelines for moving information. It allows users who aren't experienced coders to import, process, and export their data without knowing a more technical language or fussing with the code's inner workings.

Benefits of Using

Some of the key benefits of using include:

  • An easy-to-use interface
  • The ability to create complex pipelines with simple drag-and-drop tools rather than having to write any code, which can take up a lot of time — even for experienced developers who have worked on similar projects in the past
  • It comes with an extensive documentation library, as well as webinars, tutorials, and videos, so you don't have to worry about getting stuck when trying out new functions or integrations
  • Its many integrations can connect with other tools, platforms, and databases
  • The ability to cleanse your data to make it easier for downstream applications like BigQuery and others to process the information
  • It can transform flat files into a wide variety of formats, including JSON, XML, and even Avro, one of Google's file types made exclusively for its cloud-based platform
  • You can use it on-premise or in the Cloud depending on what works best for your business needs and when integrating with existing sources such as Hadoop clusters, using map-reduce processes through Java APIs if necessary.


In conclusion, big data is still a relatively new industry that's just now making its way into the mainstream spotlight. It can be challenging for companies that are just getting started with big data pipelines to find and implement solutions that work best for their needs without worrying about high costs and other limitations.

Thankfully, there are modern tools like those listed above, which all provide unique benefits of their own as well as an easy learning curve — even if you don't have much experience working on similar projects.

It doesn't take long at all to get your pipeline up and running, and fully functional, so you can process any data quickly and efficiently — no matter how large the volume may be. If you would like to learn more about how can help you with your big data needs, schedule a call to discuss our 7-day demo of the platform today.