7 Best BigQuery ETL Tools

Google Cloud Platform has a robust architecture with a petabyte-scale data warehouse known as BigQuery to perform analytics on the data. Many organizations are moving their on-premise systems to BigQuery because of its exceptional performance over data.

In this blog, you will learn about the best BigQuery ETL tools in the market that you can use to load data into BigQuery.

Table of Contents

• What is BigQuery?
• What is ETL?
• 7 Best ETL Tools 
  • Google Cloud Platform Data Flow
  • Google Cloud Data Fusion
  • Hevo Data
  • Apache Spark
  • Talend
  • IBM Datastage
  • Apache NiFi
• Conclusion

What is BigQuery?

BigQuery is a serverless, scalable cloud-based data warehouse provided by Google Cloud Platform. It is a fully managed warehouse that allows users to perform ETL on the data with the help of SQL queries. You can unleash the power of SQL and the performance and scalability feature of Google Cloud Platform to perform ad-hoc analytics on your data. BigQuery can load a massive amount of data in near real-time.

Key Features of BigQuery

Some of the key features of Google BigQuery are listed below:

1. Scalable Architecture: BigQuery offers a petabyte scalable architecture, and is straightforward to scale as per needs. 
2. Faster Processing: BigQuery can execute SQL queries over petabytes of data in seconds. You can run analysis over millions of rows without worrying about scalability.
3. Fully Managed: BigQuery is a fully managed and serverless architecture. It automatically manages the up-scale or down-scale of the cluster.
4. Security: BigQuery provides the safety of sensitive data when data is in in-flight as well as at rest. The tables and the data are compressed and encrypted to ensure the utmost security.
5. Real-time data ingestion: BigQuery can perform real-time data analysis, thereby making it famous across all the IoT and Transaction platforms.
6. Fault Tolerance: BigQuery offers replication that replicates data across multiple zones or multiple regions. It ensures consistent data availability when the region/zones go down.

What is ETL?

ETL is an abbreviation for Extract, Transform, and Loading. With the introduction of cloud technologies, many organizations are trying to perform ETL to migrate their data. They often have data storage as an RDBMS or legacy system which lacks performance, scalability, and fault-tolerant systems. Hence, to get all these features, organizations are migrating data to cloud technologies, like the Google cloud platform.

In a typical industrial ETL scenario, data is first ‘Extracted’ from legacy sources by using connectors. Then it is ‘Transformed’ by applying calculations like filter, aggregation, ranking, business transformation, etc., to derive outcomes, and then it is ‘Loaded’ onto the target systems. Below schematics will give a better understanding of ETL flow.

7 Best BigQuery ETL tools

1. Google Cloud Platform Data Flow

Google Cloud Platform Data Flow is a cloud-based data processing system with the capability to process batch and real-time data. It is a serverless and cost-effective solution to process data.

Key Features of Google Cloud Platform Data Flow

Some of the key features of Google Cloud Platform Data Flow are listed below:

1. Google Cloud Platform Data Flow has an excellent autoscaling facility that automatically detects the number of workers required to execute the job based on the data volume.
2. It offers several useful pre-built transformations that can be plugged into existing ETL logic, and you can also create custom functions to integrate into the flow.
3. Google Cloud Platform Data Flow has an SQL engine that lets you use the power of SQL to query the data.
4. With DataFlow, you can join the streaming data from Pub/Sub and perform transformations and then load the data to BigQuery for further analytics.
5. Data flow provides encryption keys, VPC, private IP’s and other security measures to carry out ETL processed securely.

Google Cloud Platform Data Flow Pricing

DataFlow is billed per second use of the workers for batch and streaming data. GCP offers free credit worth $300 to try their services. To get details about pricing, you can check their official documentation here.

2. Google Cloud Data Fusion

Google Cloud Platform’s Cloud Data Fusion is the newly introduced, powerful, and fully managed data engineering product. It helps users to build dynamic and effective ETL pipelines to migrate the data from source to target by carrying out transformations in between.

Key Features of Data Fusion

Some of the key features of Data Fusion are listed below:

1. Cloud Data Fusion shifts the focus from code development and provides an intuitive user interface to users to quickly develop the data pipeline in a drag and drop manner.
2. Cloud Data Fusion comes with a set of pre-built transformations that you can use to build your pipeline. It also provides you to develop custom transformations by using programming languages.
3. It is an open-source tool and built on top of CDAP. Hence, significant communities are always working on developing new sets of tools and transformations. 
4. It offers you to develop internal libraries to store the custom connectors or transformations that you have developed and can be shared, validated, and re-used across the organization.
5. With IAM, VPC, Private IPs, it provides enterprise-grade security to your data.
6. Cloud Data Fusion has a Comprehensive Integration toolkit that allows you to connect to several legacy sources to perform code-free transformations and load into BigQuery or any other target platform.

Cloud Data Flow Pricing

Cloud Data Flow has two pricing modules named Basic and Enterprise. The basic version starts with $1.80 per data instance per hour, whereas the Enterprise version costs $4.20 per data instance per hour. To get complete detail about pricing, you can check the official documentation here.

3. Hevo Data

Hevo Data, a No-code Data Pipeline helps you to replicate data from any data source with zero maintenance. You can get started with Hevo’s 14-day Free Trial and instantly move data from 150+ pre-built integrations comprising a wide range of SaaS apps and databases. Using Hevo, you can precisely control pipeline schedules down to the minute.

Get Started with Hevo for Free

Hevo not only loads the data onto the desired Data Warehouse but also enriches the data and transforms it into an analysis-ready form without having to write a single line of code. Its fault-tolerant architecture ensures that the data is handled in a secure, consistent manner with zero data loss.

Check out what makes Hevo amazing:

• Near Real-Time Replication -: Get access to near real-time replication on All Plans. Near Real-time via pipeline prioritization for Database Sources. For SaaS Sources, near real-time replication depend on API call limits.
• In-built Transformations – Format your data on the fly with Hevo’s preload transformations using either the drag-and-drop interface, or our nifty python interface. Generate analysis-ready data in your warehouse using Hevo’s Postload Transformation.
• Monitoring and Observability-: Monitor pipeline health with intuitive dashboards that reveal every stat of pipeline and data flow. Bring real-time visibility into your ETL with Alerts and Activity Logs.
• Reliability at Scale -: With Hevo, you get a world-class fault-tolerant architecture that scales with zero data loss and low latency.
• 24×7 Customer Support – With Hevo you get more than just a platform, you get a partner for your pipelines. Discover peace with round-the-clock “Live Chat” within the platform. What’s more, you get 24×7 support even during the 14-day free trial.

Hevo Data provides Transparent Pricing to bring complete visibility to your ETL spend. You can also choose a plan based on your business needs.

Hevo Pricing

Stay in control with spend alerts and configurable credit limits for unforeseen spikes in the data flow. Simplify your Data Analysis with Hevo today!

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4. Apache Spark

Apache Spark is an open-source lightning-fast in-memory computation framework that can be installed with the existing Hadoop ecosystem as well as standalone. Many distributions like Cloudera, Databricks, and Google Cloud Platform adopts Apache Spark in their framework for data computation.

Key Features of Apache Spark

Some key features of Apache Spark are listed below:

1. Apache Spark performs in-memory computations and is based on the fundamentals of Hadoop MapReduce. Due to its in-memory computation, it is 100x faster than Hadoop MapReduce.
2. Apache Spark distributes the data across executors and processes them in parallel to provide excellent performance. It can handle large data volumes at ease. 
3. Apache Spark can effectively connect with legacy databases using JDBC connectors to extract the data and transform them in memory and then load them to the target.
4. Apache Spark can use BigQuery as a source or target to perform ETL by using the BigQuery connector.
5. Apache spark is completely functional programming, and hence the user needs to be compliant with programming languages.
6. Apache Spark works on both batch and real-time data.

Apache Spark Pricing

Apache spark is free to use. Users can download Apache spark from here. However, distributions like Cloudera, and Hortonworks charge for the support and you can get detailed pricing here.

5. Talend

Talend is a popular tool to perform ETL on the data by using its pre-built drag and drop palette that contains pre-built transformations.

Key Features of Talend

Some key features of Talend are listed below:

1. Talend has an open studio edition for beginners, which can be used without paying any amount. The Enterprise version is known as Talend Cloud.
2. Talend has multiple integrations like Data Integration, Big Data Integration, Data Preparation, etc.
3. Talend has an interactive space that allows drag and drop of various functions (called palette) which features the various ETL operations.
4. Talend generates Java code at the backend when you build the Talend job. Hence, it requires users to have a basic understanding of programming languages. 
5. Talend has excellent connectivity to BigQuery, and you can easily perform transformations in Talend space and then load the data into BigQuery. 
6. Talend also provides API Services, Data Stewardship, Data Inventory, and B2B.

Talend Pricing

Talend’s base pack starts at $12000 a year and has multiple categories to choose from it. You can get complete information here.

6. IBM DataStage

IBM DataStage is a BI (Business Intelligence) tool and contains an exhaustive list of connectors for integrating trusted data across various enterprise systems. It can be installed on on-premise architecture as well as an on-cloud system to leverage a high-performance parallel framework.

Features of IBM Datastage

Some key features of IBM Datastage are listed below:

1. IBM Datastage has excellent support for Big Data and Hadoop ecosystem to perform parallel ETL on the data.
2. It supports extended metadata management and universal business connectivity.
3. It supports batch data and real-time data transformation.
4. With the help of connectors, it can connect to BigQuery to perform exceptional ETL on the data. 
5. Additional storage or services can be accessed without the need to install new software and hardware.
6. It provides ETL on the data and solves complex big data challenges.

IBM Datastage Pricing

IBM Datastage comes with various pricing options for on-premise and cloud. You can get complete detail here. 

7. Apache NiFi

Apache NiFi is an open-source tool that automates the movement of data from source to target. As it is open-source, contributors are continuously developing the libraries and custom transformations to provide seamless ETL/ELT with the data. 

Key features of Apache NiFi

Some key features of Apache NiFi are listed below:

1. Apache NiFi has a vast library of connectors to connect various sources and also contains pre-built transformations that can be applied to the data on the fly.
2. Apache NiFi uses a BigQuery connector for seamless integration with GCP BigQuery.
3. Apache NiFi creates flow files by chaining the transformations and then executing the jobs.
4. As Apache NiFi is open-source, you can install it anywhere and use it for your ETL purpose. You don’t need to have a BigData ecosystem to perform ETL. 
5. Apache NiFi server launches a web-based interface that allows you to create flow designs, control the data, and monitor the jobs.

Apache NiFi Pricing

BatchIQ provides Apache NiFi and integrates it with Google Cloud Platform Marketplace. More pricing details can be seen here.

Conclusion

In this blog post, we provided you with a list of the best BigQuery ETL tools in the market to perform ETL on BigQuery and its features. BigQuery is the powerful data warehouse offered by Google Cloud Platform.

If you want to use Google Cloud Platform’s in-house ETL tools, then Cloud Data Fusion and Cloud Data Flow are the two main options. But, if you are looking for a fully automated external BigQuery ETL tool, then try Hevo.

Now you can also learn about the best ETL tools that are available in the market. Based on your requirements, you can leverage one of these to boost your productivity through a marked improvement in operational efficiency.

visit our website to explore hevo

Hevo is a No-code Data Pipeline. It supports pre-built data integration from 150+ data sources. You can easily load data from source to BigQuery in minutes without writing any line of code. All these features are available with transparent pricing.

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Tell us about your experience of using the best BigQuery ETL tools in the comment section below.