Do you want a quick and simple way to transfer data from GCS to Google BigQuery? If yes, then you can migrate your data with an efficient ETL tool: Daton.
Modern companies need to utilize their data to stay ahead of increasing competition and make data-driven decisions. Data from different tools used in automating operations require fast and secured storage. Unfortunately, the cost to build and maintain a scalable, fast, and secure physical storage solution is usually too high. So, cloud storage solutions like Google Cloud Storage are becoming popular. They provide secure virtual storage solutions at no upfront cost.
Multiple data silos need to be consolidated to get a complete sense of the business. But manual data integration is complex, inaccurate, and time-consuming. As a result, data-savvy companies are reducing the time & effort of reporting and analyzing their multiple data silos by integrating these massive volumes of data present in different sheets, CSV files, and cloud storage to data warehouses like Google BigQuery.
Why integrate GCS to Google BigQuery
Nowadays, enterprises use cloud storage solutions like Google Cloud Storage (GCP) to consolidate their data. These storage solutions promote collaboration for teams working in several offices across the globe. The data automatically gets backed up by secure servers, reducing data theft and loss. Tally data from GCS with customer behavior, billing, sales, and inventory data to simplify data analysis and reporting. However, manual data consolidation takes much time to execute manually, and the reports are often inaccurate. Thus, data-savvy companies resort to ETL tools like Daton to replicate data from GCS to Google BigQuery. It is a highly automated ETL Tool that easily migrates data from several data sources to cloud data warehouses without coding.
GCS Overview
Google Cloud Storage is a reliable platform for secure storage options, powerful computation features and integrated data analytics products. Integrated G-Suite will allow users to collaborate on projects through Hangouts, Calendar, Drive and Google Docs. GCP data centres worldwide consist of physical assets like computers and hard drives for smooth distribution of resources preventing any failure or latency reduction. GCP makes users adapt to a serverless environment with minimum infrastructure. The GCP “AppEngine” helps to provide automatic resources, app hosting and monitoring.
Google Bigquery Overview
Google BigQuery is the first serverless data warehouse service that was available in the market. A database administrator architects the schema and optimize the partitions for performance and cost in a Google BigQuery environment. This cloud service automatically scales to fulfil any demands of a query. Google BigQuery service offers an excellent pricing model based on the amount of data processed by incoming queries, not on the storage or the compute capacity for processing queries. The best part about using Google BigQuery is that you can instantly load data to the service as soon as you start using it. The primary requirements are a mechanism to load data into the data warehouse and the ability to write SQL queries.
How to Replicate GCS to Google BigQuery
There are two ways in which you can replicate GCS to Google BigQuery.
Build Your data pipeline – Building an in-house data pipeline needs a lot of experience, time and manpower with higher chances of errors. You need to extract data using GCS APIs & then connect it properly with the Google BigQuery data warehouse.
Use Daton to integrate GCS & Google BigQuery – Using Daton to integrate GCS & Google BigQuery is the fastest & easiest way to save your time and efforts. Leveraging an eCommerce data pipeline like Daton simplifies and accelerates the time to build automated reporting to a great extent.
Configuring data replication on Daton only takes a few minutes and a few clicks. Analysts do not have to write any code or manage any infrastructure, yet they can get access to their Amazon ads data in a few hours. Daton’s simple and easy to use interface allows analysts and developers to use UI elements to configure data replication from GCS data into Google BigQuery.
Daton takes care of:
- Authentication
- Rate limits,
- Table creation, deletion & reloads
- Refreshing access tokens,
- Sampling,
- Historical data load,
- Incremental data load,
- Notifications
and many more important functions for data analysts to focus on analysis rather than worry about the data replication process.
Steps to Integrate GCS with Daton
- Sign in to Daton
- Select GCS from Integrations page
- Provide Integration Name, Replication Frequency, and History. Integration name would be used in creating tables for the integration and cannot be changed later
- You will be redirected to GCS login for authorizing Daton to extract data periodically
- Post successful authentication, you will be prompted to choose from the list of available GCS Ad accounts
- Select required tables from the available list of tables
- Then select all required fields for each table
- Submit the integration
Sign up for a trial of Daton Today
Here are more reasons to explore Daton for GCS to Google BigQuery Integration
- Faster Integration of GCS to Google BigQuery – GCS to Google BigQuery is one of the integrations Daton can handle very fast and seamlessly. By following few steps, you can easily connect GCS to Google BigQuery.
- Robust Scheduling Options: this allows you to schedule jobs based on their requirements using a simple configuration step.
- Low Effort & Zero Maintenance – Daton automatically takes care of all the data replication processes and infrastructure once you sign up for a Daton account and configure the data sources. There is no infrastructure to manage or no code to write.
- Data consistency guarantee and an incredibly friendly customer support team ensure you can leave the data engineering to Daton and focus instead of analysis and insights!
- Enterprise grade data pipeline at an unbeatable price to help every business become data-driven. Get started with a single integration today for just $10 and scale up as your demands increase.
- Support for all major cloud data warehouses including Snowflake, Google BigQuery, Amazon Redshift, Oracle Autonomous Data Warehouse, PostgreSQL and more.
- Flexible loading options allow to you optimize data loading behavior to maximize storage utilization and easy of querying.
- Enterprise grade encryption gives your peace of mind
- Support for 100+ data sources – In addition to GCS, Daton can extract data from a varied range of sources such as Sales and Marketing applications, Databases, Analytics platforms, Payment platforms and much more. Daton will ensure that you have a way to bring any data to Google BigQuery and generate relevant insights.
For all sources, check our data connectors page.
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- What exactly is data migration from GCS to BigQuery?Data transfer from GCS to BigQuery is a Google Cloud Platform tool that allows you to import data from Google Cloud Storage (GCS) into Google BigQuery, an internet data warehousing and analytics service. It lets you move enormous amounts of information from GCS to BigQuery for investigation, monitoring, and querying. To begin data transfer, you may utilize various techniques, including the BigQuery online Interface, command-line tool, or API. The service supports various data formats, including CSV, JSON, Avro, and Parquet, and you may customize the delimiter and encoding of your data files. GCS to BigQuery data transmission is a low-cost and efficient method of migrating data to BigQuery.
- Is there a charge for transferring data from GCS to BigQuery?Yes, there is a charge for transferring data from GCS to BigQuery. The fee is determined by the amount of data transported as well as the location of your data. Google charges for BigQuery data processing and GCS data storage. The cost is determined by the quantity of data scanned during query execution and the amount of information stored in BigQuery tables. Also, network egress costs may apply if you transmit data from GCS to BigQuery in a different area. By reading the price information on the Cloud Based website, you may evaluate the cost of GCS to BigQuery transmitting data for your use case.
- How can I monitor the progress of the GCS to BigQuery data transmission?The data movement from GCS to BigQuery may be traced using the BigQuery web-based interface or API. Just choose the data to which you are transferring data using the web Interface, and then click the "Job history" tab. This will provide an overview of all the data transfer jobs you've initiated. By clicking on a job, you can see its status, which comprises the start and finish dates and the number of bytes processed. Watching the data transmission status helps you follow the transfer's progress and spot any problems or difficulties.
- How can I troubleshoot data transfer issues from GCS to Google BigQuery?Errors can arise while uploading data from GCS to Google BigQuery for various reasons, including improper setup settings, network difficulties, and file format errors. Review the logs in Google Cloud Console to resolve such problems to determine the root cause. You may also check the GCS bucket and BigQuery dataset rights and access restrictions to confirm the relevant permissions of the transfer service. If the error is due to a file format issue, you can check the files with programs like CSVLint or AvroLint.
- Why should GCS be integrated with Google BigQuery?Businesses now employ cloud storage options such as Google Cloud Storage (GCS) to centralize their data. These storage options foster cooperation for teams operating in multiple offices worldwide. Data breaches and loss are reduced since secure servers automatically back up the data. Combine GCS data with customer behavior, billing, sales, and stock information to ease data analysis and reporting. Nevertheless, human data aggregation takes a long time, and the reports frequently need to be revised. As a result, data-savvy businesses use ETL solutions like Daton to duplicate data from GCS to Google BigQuery. It is a fully automated ETL tool that migrates data from multiple data sources to Cloud data warehouses without coding.
