Loading Data into Snowflake: 4 Best Methods (2023)

In recent years,Snowflakegained strengthCloudbasiertes data warehouseSpace. Today, more and more companies are using Snowflake to increase operational efficiency, understand their customers, understand what products work and what don't, and what products people are interested in.

This article introduces Snowflake and describes 4 methods to do itLoading data into Snowflake. These methods differ from each other in terms of the approach used and their use cases. Read on to decide which method is best for you!

What is a snowflake?

SnowflakeIt is the leading cloud-based data warehouse that has grown steadily and become popular in recent years. Snowflake offers a scalable cloud-based platform for businesses and developers and supports advanced data analytics. There are several data warehouses available, but Snowflake's architecture and data sharing capabilities are unique. Snowflake's architecture allows storage and compute to scale independently, allowing customers to consume and pay for storage and compute separately.

The best feature of Snowflake is that it offers separate data storage and calculation options. Snowflake is designed to ensure that users require minimal effort or interaction to perform performance or maintenance related activities. The minimum and maximum pool size, as well as scaling, are done automatically in this area at a very high speed.

For more information on Snowflake, visitHere.

Methods for loading data into Snowflake

Depending on how much data you want to upload and how often you upload it, you might prefer one of the following methods to upload data to Snowflake:

• Method 1: Using SQL commands to load data into Snowflake
• Method 2 – Use Snowpipe to load data into Snowflake
• Method 3 – Use the web interface to upload data to Snowflake
• Method 4: use Hevo data to load buckets other than Snowflake

Method 1: Using SQL commands to load data into Snowflake

This post describes the process of bulk loading data into Snowflake usingSnowSQL client. With SQL, you can bulk load data from any delimited plain text file, e.g. B. CSV files separated by commas. You can also bulk load semi-structured data from JSON, AVRO, Parquet, or ORC files. However, this post focuses on uploading CSV files.

Bulk loading occurs in 2 phases:

• file preparation
• loading the data

1) Preparation of files

With Snowflake, you can deploy files to internal locations called stages. Each table and user has a stage. Snowflake also supports the creation of named stages, e.g. B. demo_scenario. Scaling can be done as follows:

• First, upload your data files to a location where Snowflake can access your files. This is called preparing your files.
• You then load your data into your spreadsheets from these prepared files.

Internal stages allow for convenient and secure storage of data files without the need for external resources. However, if your data files are already deployed to a supported cloud location, such as GCS or S3, you can skip the deployment and upload them directly from these external locations, simply provide the URLs of the locations, as well as the credentials if the site it is protected. . You can also create named stages that point to your remote location.

2) Loading the data

Loading data into Snowflake requires a running virtual datastore. The store extracts data from each file and inserts it into the table as rows. The size of the data store can affect upload performance. When uploading a large number of files or large files, you can choose a larger data store.

Now you will learn to use them.SnowSQL-SQL clientto load CSV files from a local computer into a table called Contacts in the demo database demo_db. CSV files are easier to import into database systems like Snowflake because they can represent relational data in a plain text file.

They use an internal named stage to store the files before uploading them. The following steps are now required to load data into Snowflake:

Step 1 - Use the demo_db database

Last Login: Sun Jun 30 15:31:25 ttys011 Superuser-MacBook-Pro:Documents hevodata$ snowsql -a bulk_data_loadUser: johndoePassword:* SnowSQL * V1.1.65Type SQL statement or !helpjohndoe#(no store )@(no database) .(no schema)>USE DATABASE demo_db;+----------------------------- -------- -- --------------------------+| status ||----------------------------------------------- ------------- -----------------|| Declaration executed successfully. |+------------------------------------------------ -------- ----+1 row(s) produced. Elapsed time: 0.219s

Step 2: Create the contact table

Use the following SQL command to create the contact table:

johndoe#(out of stock)@(DEMO_DB.PUBLIC)>CREATE OR REPLACE TABLE CONTACTS ( IDENTIFICATION NUMBER (38, 0) First name STRING, Last name STRING, Company STRING, Email STRING, Work phone STRING, Mobile phone STRING , Address STRING , City ​STRING, zip code NUMBER(38, 0));+------------------------------- --- ------------------+| status ||----------------------------------------------- ------------- -----------------|| CONTACTS table created successfully. |+------------------------------------------------ -------- ----+1 row(s) produced. Elapsed time: 0.335s

Step 3: Fill the table with records

The contacts table should contain records like this;

1, Chris, Harris, BBC Top Gear, harrismonkey@bbctopgearmagazine.com, 606-237-0055, 502-564-8100, Postfach 3320 3 Queensbridge, Northampton, NN4 7BF2, Julie, Clark, American Aerobatics Inc, julieclark@americanaerobatics. com, 530-677-0634, 530-676-3434, 3114 Boeing Rd, Cameron Park, CA 956823, Doug, Danger, MotorCycle Stuntman LA, dougdanger@mcsla.com, 413-239-7198, 508-832-9494, PO Box 131 Brimfield, Massachusetts, 010104, John, Edward, Get Psyched, information@johnedward.net, 631-547-6043, 800-860-7581, PO Box 383Huntington, Nueva York, 117435, Bob, Hope, Bob Hope Comedia , bobhope@bobhope.com, 818-841-2020, 310-990-7444, 3808 W Riverside Dr-100, Burbank, CA 91505

Step 4 – Create an Indoor Scene

Next, you'll create an internal stage called CSV Files.

johndoe#(no store)@(DEMO_DB.PUBLIC)>CREATE STAGE CSV files; +------------------------------------------------- ------------ ---+| status ||----------------------------------------------- ------------- -----------------|| CSVFILES stage created successfully. |+------------------------------------------------ -------- ----+1 row(s) produced. Elapsed time: 0.311s

Step 5: Run a PUT command to prepare the logs in CSV files

johndoe#(without storage)@(DEMO_DB.PUBLIC)>PUT file:///tmp/load/contacts0*.csv @csvfiles;contacts01.csv_c.gz(0.00MB): [######### . #] 100.00 % complete (0.417 s, 0.00 MB/s), contacts02.csv_c.gz (0.00 MB): [#########] 100.00 % complete (0.377 s). , 0.00 MB/s), Contacts03 .csv_c.gz(0.00 MB): [###########] 100.00 % complete (0.391 s, 0.00 MB/s) , Contacts04.csv_c.gz (0.00 MB): [### # ######] 100.00 % complete (0.396 s, 0.00 MB/s), contacts05.csv_c.gz (0.00 MB/s). 00 MB): [### ### ###] 100.00 % complete (0.399 s, 0.00 MB/s), +-----------+----- ------ - -------+------ - ------ +--------------------- ---+| Source | objective | source size | target size | state | |----------------------------------------- -------- ---- -- -------- ------------------------------------ ----|| Contacts01.csv | contacts01.csv.gz | 534 | 420 | LOADED || Contacts02.csv | contacts02.csv.gz | 504 | 402 | LOADED || Contacts03.csv | contacts03.csv.gz | 511 | 407 | LOADED || Contacts04.csv | contacts04.csv.gz | 501 | 399 | LOADED || Contacts05.csv | contacts05.csv.gz | 499 | 396 | UPLOADED |+-----+--------+-------- -----+----- ------------- -----+5 line(s) produced. Elapsed time: 2.111s

Note that:

• This command uses the contacts0*.csv placeholder to upload multiple files.
• o@the icon indicates where the files should be deployed, in this case @csvfiles;
• By default, the PUT command compresses data files using GZIP compression.

Step 6: Confirm that the CSV files have been tested

To see if the files are ready you can use theLISTDomain.

johndoe#(keystore)@(DEMO_DB.PUBLIC)>LIST @csvfiles;

Step 7: Specify a virtual datastore to use

We can now load the files from the prepared files into the CONTACTS table. First, you specify a virtual store to use.

johndoe#(no store)@(DEMO_DB.PUBLIC)>Use load data from STORE; +------------------------------------------------- ------------ ---+| status ||----------------------------------------------- ------------- -----------------|| Declaration executed successfully. |+------------------------------------------------ -------- ----+1 row(s) produced. Elapsed time: 0.203s

Step 8 – Load the prepared files into a Snowflake table

johndoe#(DATALOAD)@(DEMO_DB.PUBLIC)>COPIAR EN Kontakte; FROM @csvfiles PATTERN = '.*contacts0[1-4].csv.gz' ON_ERROR = 'skip_file';

• EMspecifies where the data from the table will be loaded.
• STANDARDSpecifies the data files to load. In this case we are loading data files with names that contain the numbers from 1 to 4.
• ON_ERRORtells the command what to do when it finds errors in the files.

Snowflake also offers powerful options for handling errors during data loading. For more information on these options, see the Snowflake documentation.

If the upload was successful, you can now query your table using SQL:

johndoe#(DATALOAD)@(DEMO_DB.PUBLIC)>SELECT * FROM contatos LIMIT 10;

Method 2 – Use Snowpipe to load data into Snowflake

you can also usesnow whistlefor bulk uploading of data to Snowflake, particularly from files sent to external storage locations. Snowpipe uses theCOPY OFcommand, but with additional features that allow you to automate this process.

Snowpipe also eliminates the need for a virtual warehouse; instead, it uses external computing resources to continually upload data as files are delivered, and you are only billed for the data actually uploaded.

Method 3 – Use the web interface to upload data to Snowflake

The third option for uploading data to Snowflake is the Data Upload Wizard in the Snowflake web interface.

oIE from the Webyou can just select the table you want to load and clickLOADEDyou can easily load a limited amount of data into Snowflake. The wizard simplifies the upload by combining the preparation and data upload phases in a single operation and automatically deleting all prepared files after upload.

The wizard should only upload a small number of files that contain small amounts of data. For large amounts of data, it's better to use one of the other options.

Method 4: use Hevo data to load buckets other than Snowflake

cube hevois a no-code data pipeline solution that allows you to move dataMore than 100 data sourcesto Snowflake, databases like SQL Server, BI tools, or any destination of your choice in a fully automated and hassle-free manner. Fully managed, Hevo automates not only the process of loading data from the desired source, but also enriching the data and transforming it into an analysis-ready form without writing a single line of code. Its fault tolerant architecture ensures that data is processed securely and consistently without data loss.

Hevo Data takes care of all your data pre-processing needs, allowing you to focus on core business activities and gain highly meaningful insights on how to generate more leads, retain customers and take your business to new heights of profitability. It offers a consistent and reliable solution to manage data in real time and always have the data at the desired destination for analysis.

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The key features of Hevo include:

• Fully automatic:The Hevo platform takes a few minutes to set up and requires minimal maintenance.
• Data transmission in real time:Hevo offers real-time data migration so you always have data ready for analysis.
• 100% complete and accurate data transmission:The solid infrastructure of Hevo guarantees reliable data transmission without data loss.
• Scalable infrastructure:Hevo has built-in integrations for hundreds of sources, allowing you to scale your data infrastructure as needed.
• Vital support:The Hevo team is available 24 hours a day to provide exceptional support to its customers through chat, email and support calls.
• schema management: Hevo eliminates the tedious task of schema management and automatically detects the schema of the incoming data and assigns it to the target schema.

Diploma

The article introduced youSnowflake-Datawarehouse. In addition, he explained 4 methods ofLoading data into SnowflakeStep by Step. Each method has its unique advantages, you should choose one of them according to your needs.

Imagine having to be an expert on all of your company's data sources and trying to hand-code all of your data movement needs. This is quite a Herculean task, but withholayou can retrieve data from hundreds of sources, transform that data on the fly, and then upload it to the destination storage of your choice.

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cube hevowill automate your data transfer process so you can focus on other aspects of your business like analytics, customer management, etc. This platform allows you to transfer data fromMore than 100 multiple fontsfor cloud-based data warehouses like Snowflake, Google BigQuery, Amazon Redshift, etc. It will give you a hassle-free experience and make your work life much easier.

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What is your preferred method ofLoading data into Snowflake? Let us know in the comments.