A plain text file containing a list of data separated by commas is known as a Comma Separated Values (CSV) file. Data is regularly transferred between apps using these files. Databases support CSV files on a regular basis. BigQuery, Google’s data warehouse as a service, combines data storage and analytics in one package. BigQuery allows you to conduct real-time SQL queries on billions of records.
Stakeholders are always looking, to find faster and better ways, to get all their data from all their sources, into BigQuery. A very popular and semantically easy data format these days is CSV. It can store data from databases, clickstreams, browsing trails, social media interactions, page views, and a multitude of sources.
In this article, you will see 4 ways to move data from CSV to BigQuery. Read along to select the method that works best for your business!
What is Google BigQuery?
BigQuery, Google’s data warehouse as a service, provides the twin functionality of data storage and analytics. BigQuery allows you to run SQL queries over billions of rows in real-time. Since it’s serverless and fully managed, it’s one of the most popular data warehouses. As it can ingest petabytes of data, the incoming data can be in various shapes and sizes.
BigQuery supports a BI engine to provide a high-speed memory analysis service. This will enable you to analyze large and complex datasets while maintaining high concurrency. Moreover, BigQuery integrates well with tools such as Google Data Studio and Looker. It also leverages Machine Learning to help Data Scientists and Data Analysts in building and operating various models.
Key Features of Google BigQuery
- User Friendly: With only a few clicks, you can begin storing and analyzing your data in BigQuery. There’s no need to set up clusters, determine storage capacity, or select compression and encryption options. You can effortlessly set up your cloud Data Warehouse with an intuitive user interface that includes step-by-step instructions.
- Real-Time Analytics: BigQuery distributes any number of resources optimally to give the best performance and outcomes, allowing you to generate business reports as needed.
- On-Demand Storage Scaling: As your data needs grow, you can rest assured that it will scale automatically as needed. It’s built on Google’s Colossus (Global Storage System) and saves data in a columnar format, allowing users to work directly on compressed data without needing to decompress files on the fly.
- BigQuery ML: Using ordinary SQL commands, you may successfully create and develop data models in BigQuery ML. This eliminates the need for technical knowledge of Machine Learning and allows your Data Analysts to evaluate ML models directly.
- Optimization Tools: Google’s BigQuery segmentation and clustering algorithms might assist you in getting faster results from your queries. You can also change the default datasets and table expiration settings for better storage costs and utilization.
To learn more about Google BigQuery, visit here.
Why Move Data from CSV to BigQuery?
Analyzing and handling a large amount of data can be cumbersome in CSV files. BigQuery import CSV will help you smoothen your Analysis processes and gain the following features of BigQuery:
- Real-Time Analytics
- On-Demand Storage Scaling
- BigQuery ML
- Optimization Tools
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Methods to Load Data from CSV to BigQuery
You can upload CSV to Bigquery using any of the following methods:
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Method 1: CSV to BigQuery Using the Command Line Interface
The bq load command creates or updates a table and loads data in a single step.
E.g. Assuming you have a dataset named mydb and there exists a table named mytable in it.
bq load mydb.mytable mysource.txt name:string,count:integer
Explanation of the bq load command arguments:
source: mysource.txt: [if necessary, include the full path to the file]
schema: name:string, count:integer ..... [Repeat for all columns in the CSV to be mapped into Bigquery columns]
To check if the table has been populated, you can run the following command:
bq show mydb.mytable
Sample output will be:
Last modified Schema Total Rows Total Bytes Expiration
----------------- ------------------- ------------------- ------------
22 Aug 15:31:00 |- name: string 352 6456 --
|- count: integer
The manual process has the obvious limitations of scalability, portability, and susceptibility to error.
Method 2: CSV to BigQuery Using Hevo Data
Hevo Data, a No-code Data Pipeline, helps you automate the CSV to BigQuery data transfer process in a completely hassle-free & automated manner. Hevo is fully managed and completely automates the process of not only loading data from your desired source but also enriching the data and transforming it into an analysis-ready form without having to write a single line of code. Hevo takes care of all your data preprocessing needs required to import CSV to BigQuery Integrations and lets you focus on key business activities.
Hevo lends itself well to any data cleansing, pre-processing, and transformations before loading them to your data source. Furthermore, Hevo will also take care of periodic reloads and refreshes. Hevo ensures that your BI tool is continually up-to-date with analysis-ready data.
Check out what makes Hevo amazing:
- Highly Interactive UI and Easy Setup: With its simple and interactive UI, Hevo can be set up within minutes. Hevo has a simple 3 step process to connect your data source to your Bigquery warehouse.
- Auto Schema Mapping: Hevo takes away the tedious task of schema management & automatically detects the schema of incoming CSV data and maps it to the BigQuery schema.
- High-Speed Data Loading: Loading compressed CSV data into BigQuery is slower than loading uncompressed data. Hevo can decompress your data before feeding it to BigQuery. Hence your process would be simpler on the source side and will be completed efficiently on the destination side.
- Transformations: Hevo provides preload transformations through Python code. Hevo does this bit for you on its own and converts the data encoding to UTF-8 thus allowing you a hassle-free data transfer.
- Hevo Is Built To Scale: Hevo has native integrations with 150+ data sources across databases, SaaS applications, Streaming services, SDKs. As the number of sources and the volume of your data grows, Hevo scales horizontally, handling millions of records per minute with very little latency.
- Formatting: BigQuery expects the date and time values to be in certain formats and throws up problems if they’re not. Using Hevo, you don’t have to worry about such formatting. Hevo’s robust pipeline will cater to your formatting needs automatically.
- Live Support: The Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
With continuous Real-Time data movement, Hevo allows you to combine your data from multiple data sources and seamlessly load it to BigQuery with a no-code, easy-to-setup interface. Try our 14-day full-feature access free trial!
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Method 3: CSV to BigQuery Using the BigQuery Web UI
You can make use of the simple Web UI of BigQuery and load CSV data using the following steps:
- You can go to your Web console and click “Create table” and then “Create table from”.
- Next, you can specify the CSV file, which will act as a source for your new table.
- The “Source” dropdown will let you select amongst many sources like Cloud storage.
- In “File format”, select CSV.
- Then select a database and give your table a name.
- You can either upload a sample JSON to specify the schema or leave the schema definition to “auto-detect”.
- Some other configurable parameters are field delimiter/skip header rows/number of errors allowed/jagged rows etc.
- Clicking on “Create Table” will now fetch your CSV, ascertain the schema, create the table, and populate it with the CSV data.
Method 4: CSV to BigQuery Using the Web API
A full discussion on the coding is beyond the scope of this article, but broadly speaking, your steps would be as follows:-
- Specify the source URL, dataset name, destination table name, etc.
- Initialize the client that will be used to send requests(can be reused for multiple requests).
- Specify the “Load Job configuration“, and make sure you do not miss essential format options.
- Load the table using API commands, this load job will block until the table is successfully created and loaded or an error occurs.
- Check if the job was successfully completed or if there were some errors.
- Throw appropriate error messages, make changes, and retry the process.
This is the most configurable and flexible option, but also the most error-prone and susceptible to maintenance whenever the source or destination schema changes.
Your program will need some time-tested trials to mature.
Limitations of Moving Data from CSV to BigQuery
- Nesting and repetitive data are not supported in CSV files.
- BOM (byte order mark) characters should be removed. They may result in unanticipated consequences.
- BigQuery will not be able to read the data in parallel if you use gzip compression. It takes longer to import compressed CSV data into BigQuery than it does to load uncompressed data. See Loading compressed and uncompressed data for further information.
- You can’t use the same load job to load compressed and uncompressed files.
- A gzip file can be up to 4 GB in size.
This article provided you with a step-by-step guide on how you can set up CSV to BigQuery connection using 4 different methods. However, there are certain limitations associated with the first three methods. You will need to implement them manually, which will consume your time & resources, and writing custom scripts can be error-prone. Moreover, you need a full working knowledge of the backend tools to successfully implement the in-house Data transfer mechanism. You will also have to regularly map your new CSV files to the BigQuery Data Warehouse.
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Share your thoughts on loading data from CSV to BigQuery in the comments!