Harnessing and interpreting data isn’t merely an advantage but a fundamental necessity for success across industries. It stands as a driving force revealing critical insights essential for informed decision-making. BigQuery plays a pivotal role in leveraging this information that empowers businesses to shape strategies and streamline operations. To unlock the full potential of BigQuery, organizations rely on various ETL tools designed to optimize data processing and manipulation. These tools simplify the process and improve data quality, accessibility, and analysis capabilities.
In this article, you’ll learn about the ETL process and the popular ETL tools compatible with Google BigQuery. These tools aid in improving the entire process and streamlining the workflow.
What is ETL?
ETL stands for Extract, Transform, and Load. It is the process of extracting data from various sources, transforming it into a form that fits a desired structure or format, and then loading the necessary information into a target database or data warehouse. This process is repeated as new data is added, ensuring accuracy and completeness in the warehouse and maintaining up-to-date information suitable for data mining and reporting. You can automate the ETL process using ETL tools, which are software solutions that simplify data management strategies and improve data quality by offering a standardized method for retrieving, modifying, and ingesting.
What is BigQuery?
BigQuery is a fully managed data warehouse with built-in features like machine learning, geospatial analysis, and business intelligence that allow you to handle large data volumes. Its serverless architecture lets you execute SQL queries without the need to manage any infrastructure.
BigQuery’s scalable, distributed analysis engine enables you to execute queries on terabytes in seconds and petabytes in minutes. You can query data stored in BigQuery or directly from where it resides using external tables. Overall, BigQuery offers a comprehensive platform that adapts to the evolving demands of modern data analytics.
Here are some of the key features of BigQuery:
- With BigQuery, you can analyze and visualize geographical data using geography data types.
- To assess data efficiently, it employs a columnar storage format for storing data. This format enables the system to scan individual columns across the extensive dataset, enhancing query performance.
- Interacting with BigQuery is facilitated through interfaces like the Google Cloud console and command-line tool. In addition, you can use Python, Java, JavaScript, and BigQuery’s REST API and RPC API for transformation and management.
Top 5 BigQuery ETL Tools
Here are the top BigQuery ETL tools with various features and capabilities.
Airbyte
Airbyte is a data integration and replication platform with 350+ built-in connectors that allow you to have seamless data integration from multiple sources to destinations within a few minutes. You can also use its robust scheduling capabilities to automate data extraction at predefined intervals and facilitate data loading with an incremental loading approach. For incremental loading, it also supports CDC, which processes only the latest changes to the dataset, thereby ensuring efficiency in the data extraction process.
Here are some of the key features of Airbyte:
- With Airbytes’s user-friendly Connector Developer Kit (CDK), you can create personalized connectors in 30 minutes. This helps you to connect data sources and destinations that aren’t readily available in the pre-built connectors list.
- You can utilize dbt integration within Airbyte to perform custom transformations seamlessly.
- It maintains data security using strong encryption, audit logs, access control based on roles, and secure data transmission.
- You can choose the specific data streams you want to replicate from all available API streams supported by Airbyte. This implies customizing and prioritizing specific data sources based on your requirements.
Suggested Read: Change Data Capture Tools
Fivetran
Fivetran is a cloud-based platform with over 400+ pre-built source connectors that allow seamless data integration from various sources to destinations. It is an ETL cloud service that assists you in centralizing and transferring information from multiple SaaS sources, databases, data warehouses, and more. Fivetran helps you load substantial amounts of unstructured and semi-structured data using batch processes to data lakes, and it can also ingest structured data into data warehouses.
Here are some of the amazing features of Fivetran:
- Fivetran’s column blocking and hashing feature allows you to protect sensitive data like Personally Identifiable Information (PII), reducing the risk of data loss or cyberattacks.
- To perform intricate transformations in Fivetran, you can integrate it with dbt core, an open-source tool crafted to simplify the task of data transformation.
- Fivetran offers enhanced visibility into your data integration process through visual data lineage graphs. This allows you to quickly monitor the path of data from its source to its destination through various stages, facilitating comprehensive oversight and analysis.
Matillion
Matillion is a cloud-based ETL and ELT platform with an extensive inventory of pre-built, out-of-the-box connectors for popular applications and databases. It lets you connect to any data source and swiftly build pipelines to ingest data into your preferred destination. This helps you to create a single source of truth with your data warehouse for effective data analytics workflows.
Here are some of the significant features of Matillion:
- Matillion helps you to effortlessly schedule data pipelines using various parameters such as time or event-based triggers, API calls, or the availability of new data.
- The push-down ELT technology harnesses your data warehouse’s power, processing intricate joins across millions of rows in seconds.
- With the visual and low-code designer, you can perform simple conversions on all your data for seamless integration, enabling quick and smooth processing. However, you have to choose between Python or SQL for complex transformations.
Hevo Data
Hevo Data is a cloud-based ELT platform with around 150+ data source connectors that provide reliable data integration for your growing data needs. The intuitive user interface simplifies the setup and management of your data pipelines for both technical and non-technical users. Using Hevo, you can automate data collection from different applications and databases and load it into a data warehouse. It allows you to use its powerful drag-and-drop transformation feature to enrich your data without any manual coding.
Here are some of the key features of Hevo data:
- Hevo provides flexible data replication options for syncing data between sources and destinations. This enables you to replicate the entire database, specific tables, or individual columns, allowing you to focus solely on relevant information.
- With schema mapper, you have the option to define how data extracted from the source is stored in the destination. This feature automates the mapping between source event types and destination tables.
- By assigning primary keys, Hevo allows you to remove duplicate data while loading to a database destination. If the warehouses have non-enforceable keys, only unique records are uploaded.
Talend
Talend is a cloud-based ETL and ELT data integration platform designed to manage large volumes of data. It seamlessly unifies data from multiple sources into on-premise or cloud-based data warehouses. With Talend, you can quickly construct essential data pipelines to perform ETL. It not only allows you to move and merge data but also helps with governance into a unified, low-code platform compatible with nearly any data source and architecture.
Here are some key features of Talend:
- It provides flexibility across setups like on-premise, cloud, multi-cloud, and hybrid environments, ensuring that Talend can be utilized regardless of your organization’s preferences.
- Delivers consistent value while upholding security and compliance requirements.
- You can prepare data collaboratively with your team members in real-time, ensuring a smooth and streamlined workflow.
How to Import Data into BigQuery in Minutes
BigQuery ETL will provide you with an opportunity to utilize your data for diverse business objectives, from integration and analytics to compliance and performance optimization.
To unlock the full potential of your data, you need to extract it from your preferred source and load it into BigQuery. For this process, we recommend leveraging Airbyte, which helps you migrate data within a few clicks by following these three straightforward steps.
Step 1: Configure Your Source Connector
Log in to the Airbyte account and use the user-friendly interface to set up a source connector from which you intend to extract data.
Step 2: Configure BigQuery as a Destination
- Navigate to the dashboard and click on the Destinations option.
- Type BigQuery in the Search Box of the destination page and click on the connector.
- On the BigQuery destination page, fill in the details such as Project ID, Dataset Location, and Default Dataset ID. Choose the Loading Method between GCS Staging and Standard Inserts. Then click on Set up Destination.
Step 3: Configure the Data Pipeline in Airbyte
After setting both the source and destination, proceed to configure the connection. This step involves choosing the source data (step 1), setting the sync frequency, and specifying the destination (step 2).
These three steps complete the data integration process using Airbyte, and you can initiate analyzing your data in BigQuery. This streamlined approach not only ensures reliable data transfer but also allows you to derive meaningful insights from your consolidated data.
Conclusion
This article outlines the top five ETL tools with diverse solutions catering to data integration needs. Each tool offers unique strengths, from robust features to user-friendly interfaces. Considering factors such as scalability, ease of use, and replication capabilities is crucial based on your requirements for making informed decisions. So choose among these top ETL BigQuery tools to seamlessly align with your organization’s objectives, ensuring a streamlined and efficient data extraction, transformation, and loading process within the Google BigQuery ecosystem.
We recommend using a user-friendly tool like Airbyte, which provides an extensive set of connectors and robust security features to simplify your workflows. Try Airbyte today!
Suggested Reads:
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.