- Ndz Anthony
- April 19, 2023
Data ingestion is a critical component of any data pipeline, and as professionals in this field, we understand the complexities involved in efficiently loading vast amounts of data into a data warehouse.
After years of working with various data ingestion tools, I can attest to the value of Snowpipe as a powerful and sophisticated solution.
In this in-depth guide, we will examine Snowpipe, a serverless data ingestion service that significantly enhances the process of loading data into Snowflake data warehouses.
We will discuss the underlying architecture, the mechanics of its operation, and provide a detailed walkthrough for setting up Snowpipe.
Furthermore, we’ll investigate how snowflake compares with other data ingestion tools.
Let us now embark on a comprehensive exploration of Snowpipe and uncover how it can revolutionize your data ingestion processes.
What is Snowpipe?
If you’ve ever dealt with large-scale data ingestion, you know it can be quite a challenge. That’s where Snowpipe comes to the rescue! In a nutshell, Snowpipe is a serverless data ingestion service designed specifically for Snowflake, the popular cloud-based data warehousing platform.
It allows you to load data effortlessly into your Snowflake data warehouse with minimal management and maintenance.
Here are some key features and benefits of Snowpipe:
Serverless Architecture
With Snowpipe, there’s no need to worry about provisioning or managing the infrastructure. It automatically scales to handle the workload, which means you can focus on what truly matters — your data.
Continuous Data Loading
Snowpipe enables near real-time data ingestion, allowing you to load data as soon as it becomes available. This ensures that your data warehouse remains up-to-date with the latest information.
Pay-as-you-go Pricing
Snowpipe follows a consumption-based pricing model, meaning you only pay for the resources you actually use. This cost-effective approach ensures you’re not wasting money on unnecessary resources.
Flexibility and Ease of Use
Snowpipe supports various data formats, such as CSV, JSON, Avro, ORC, and Parquet, making it versatile and accommodating to your specific data needs. Plus, with its simple setup and configuration, you’ll be up and running in no time.
Secure and Reliable
Snowpipe takes security seriously, providing end-to-end encryption and robust access controls. Moreover, it offers high durability and resilience, ensuring your data is safe and accessible whenever you need it.
In essence, Snowpipe is designed to make your data ingestion process a breeze, saving you time, effort, and resources. You now have a basic understanding of what Snowpipe is, let’s move on to the next section and explore how it works its magic.
How Does SnowpipeWork?
To truly appreciate the power of Snowpipe, it’s essential to understand its inner workings. In this section, we’ll explore the mechanics of Snowpipe and how it manages to streamline the data ingestion process.
At its core, Snowpipe relies on a few key components and processes:
- Stage: First, you’ll need to stage your data files in a location that Snowpipe can access. This is typically done in a cloud storage service like Amazon S3, Google Cloud Storage, or Microsoft Azure Blob Storage. Staging the files allows Snowpipe to efficiently retrieve and load the data into Snowflake.
- Auto-Ingest: With the Auto-Ingest feature, Snowpipe monitors your cloud storage for new files and automatically initiates the data loading process as soon as new data is detected. This ensures that your Snowflake data warehouse remains current with minimal manual intervention.
- Snowpipe REST API: Alternatively, you can also use the Snowpipe REST API to manually trigger data loads by providing a list of files to be ingested. This approach gives you more control over when and how the data is loaded into Snowflake.
- Data Loading Pipeline: Once the data loading process is initiated, either via Auto-Ingest or the REST API, Snowpipe retrieves the files from the staging area and processes them in parallel. It leverages Snowflake’s powerful virtual warehouses to handle the actual data loading, efficiently transforming and inserting the data into your target tables.
- Load History: After the data has been loaded, Snowpipe provides a comprehensive load history, allowing you to review the status of each load operation and troubleshoot any issues that may arise.
How to Get Started withSnowpipe
You’ve made it this far, and now you’re ready to jump into the exciting world of Snowpipe! In this section, we’ll walk you through the process of setting up and configuring Snowpipe to solve your data ingestion challenges.
Prerequisites
Before diving into Snowpipe, ensure you have the following:
- An active Snowflake account
- Access to a cloud storage service (Amazon S3, Google Cloud Storage, or Microsoft Azure Blob Storage) where your data files will be staged
- SnowSQL installed on your local machine (optional, but highly recommended for executing SQL commands)
Just follow these steps, and you’ll have your own Snowpipe up and running in no time.
Step 1: Create aStage
First, you need to create a stage in Snowflake to store the data files. This stage will act as the link between Snowpipe and your cloud storage service. To create a stage, execute the following SQL command:
CREATE STAGE my_stage
URL = ‘your_storage_service_url’
CREDENTIALS = (AWS_KEY_ID = ‘your_access_key_id’ AWS_SECRET_KEY = ‘your_secret_access_key’);
Replace the placeholders with the appropriate information for your storage service.
Step 2: Define a FileFormat
Next, specify the file format of the data you’ll be ingesting. This will help Snowpipe understand how to process the files. To create a file format, use the following SQL command:
CREATE FILE FORMAT my_file_format
TYPE = ‘file_type’
FIELD_DELIMITER = ‘delimiter’;
Just replace ‘file_type’ with the appropriate file format (CSV, JSON, etc.) and ‘delimiter’ with the character used to separate fields in your data files.
Step 3: Create aTable
Now, create a table in Snowflake to store the ingested data. The table schema should match the structure of your data files. Execute the following SQL command to create a table:
CREATE TABLE my_table (
column1 data_type,
column2 data_type,
…
);
Replace the column names and data types with the appropriate information for your data.
Step 4: Create theSnowpipe
It’s time to create your Snowpipe! To do this, use the following SQL command:
CREATE PIPE my_pipe
AS COPY INTO my_table
FROM (SELECT $1, $2,… FROM @my_stage/my_file_format);
Substitute the $1, $2,… placeholders with the appropriate column references based on your table schema.
Step 6: IngestData
Finally, you’re ready to start ingesting data! You can either enable Auto-Ingest to automatically load files as they’re added to your cloud storage, or you can manually trigger a data load using the Snowpipe REST API.
For Auto-Ingest, execute the following SQL command:
ALTER PIPE my_pipe SET AUTO_INGEST = TRUE;
For manual ingestion, use the Snowpipe REST API’s ‘insertFiles’ endpoint to specify the files you’d like to load.
Comparing Snowpipe with Other Data Ingestion Solution
When it comes to data ingestion, there’s no one-size-fits-all solution. Snowpipe offers several advantages, but it’s essential to understand how it stacks up against other popular data ingestion tools and services.
To help you choose the best solution for your requirements, we’ll provide a fair comparison of Snowpipe to some other popular options.
Snowpipe vs. Batch Ingestion (using Snowflake COPYcommand)
- Real-time ingestion: Snowpipe enables near real-time data loading, whereas batch ingestion typically occurs at scheduled intervals.
- Resource consumption: Snowpipe’s serverless architecture allows for automatic scaling and optimized resource usage, while batch ingestion might require manual scaling and management of virtual warehouses.
- Complexity: Snowpipe abstracts away many complexities, making it simpler to use. Batch ingestion might require more fine-tuning and in-depth knowledge of Snowflake.
Snowpipe vs. ApacheKafka
- Ease of setup: Snowpipe is purpose-built for Snowflake, making it easier to set up and configure. Apache Kafka requires additional integration efforts to work seamlessly with Snowflake.
- Data processing: Kafka is designed for real-time data streaming and complex event processing, while Snowpipe focuses on data ingestion into Snowflake.
- Scalability: Both solutions offer scalable data processing; however, Kafka may require more infrastructure and management overhead.
Snowpipe vs. Amazon Kinesis DataFirehose
- Platform-specific: Kinesis Data Firehose is designed for AWS users and integrates with Amazon Redshift. Snowpipe is purpose-built for Snowflake, making it more suitable for Snowflake users across different cloud providers.
- Real-time streaming: Both Snowpipe and Kinesis Data Firehose offer near real-time data ingestion capabilities.
- Pricing: Snowpipe follows a consumption-based pricing model, while Kinesis Data Firehose uses a pay-per-GB model, which might impact cost considerations depending on your usage patterns.
Snowpipe vs. Azure DataFactory
- Cloud platform: Azure Data Factory is designed for Azure users and integrates with various Azure services. Snowpipe is specifically built for Snowflake, making it a better choice for Snowflake users.
- Data orchestration: Azure Data Factory is a full-fledged data integration service, offering more extensive capabilities for data transformation and orchestration. Snowpipe focuses primarily on data ingestion into Snowflake.
- Ease of use: Snowpipe provides a simpler, more streamlined approach to data ingestion, while Azure Data Factory might require more configuration and management.
Why Datameer and Snowflake Make a Killer Combo for Your Data Ingestion Needs
So, you’ve seen how Snowpipe can be a game-changer for data ingestion into Snowflake. But what if we told you that there’s an even better way to level up your data game?
That’s right — by combining Datameer with Snowflake, you get a powerhouse duo that’ll rock your data world. Here are some awesome benefits of combining Datameer and Snowflake for your data needs.
- The Two are a Match: Datameer and Snowflake just click together like peanut butter and jelly, creating a seamless and smooth data ingestion process. With Datameer’s top-notch data preparation and transformation skills, Snowpipe’s data-loading prowess gets an extra boost, making your data ready for action in Snowflake.
2. They’re the Ultimate Data Dream Team: Datameer’s killer data integration and transformation features, combined with Snowflake’s cutting-edge data warehousing, give you a complete data solution that covers your entire pipeline — from ingestion and transformation to analysis and insights.
3. Lightning-Fast Insights: Datameer’s user-friendly, no-code interface makes data preparation and transformation a breeze, so you can get your data into Snowflake in no time. Faster data, faster decisions — sounds like a win-win to us!
4. Scale It Up (or Down) with Ease: Snowpipe and Datameer know how to handle the big leagues, effortlessly scaling resources to match your needs. This dynamic duo keeps your data solution running smoothly and cost-effectively, even as your data demands grow.
5. Safety First: Datameer and Snowflake take data security seriously, offering top-of-the-line encryption, access control, and auditing features to keep your precious data safe and sound.
6. A Support Squad You Can Count On: When you join Team Datameer and Snowflake, you’re not just getting an incredible data solution — you’re also gaining access to expert support, thorough documentation, and a vibrant community of fellow data enthusiasts who’ve got your back.
FAQs
How do you start a Snowpipe in a Snowflake? ›
- Step 1: Configure Access Permissions for the S3 Bucket. AWS Access Control Requirements. ...
- Step 2: Create the IAM Role in AWS.
- Step 3: Create a Cloud Storage Integration in Snowflake.
- Step 4: Retrieve the AWS IAM User for your Snowflake Account.
- Step 5: Grant the IAM User Permissions to Access Bucket Objects.
Although Snowpipe is continuous, it's not real-time. Data might not be available for querying until minutes after it's staged. Throughput can also be an issue with Snowpipe. The writes queue up if too much data is pushed through at one time.
How long does load history of Snowpipe last? ›Load History
Stored in the metadata of the pipe for 14 days. Must be requested from Snowflake via a REST endpoint, SQL table function, or ACCOUNT_USAGE view. To avoid reloading files (and duplicating data), we recommend loading data from a specific set of files using either bulk data loading or Snowpipe but not both.
Snowpipe supports loading from internal stages (i.e. Snowflake named stages or table stages, but not user stages) or external stage (Amazon S3, Google Cloud Storage, or Microsoft Azure).
How to learn Snowflake easily? ›- Log into SnowSQL.
- Create Snowflake Objects.
- Stage the Data Files.
- Copy Data into the Target Table.
- Query the Loaded Data.
- Summary and Clean Up.
Continuous Loading Using Snowpipe
This option is designed to load small volumes of data (i.e. micro-batches) and incrementally make them available for analysis. Snowpipe loads data within minutes after files are added to a stage and submitted for ingestion.
One of the main disadvantages of snowflake schema is that it increases the query complexity and the number of joins required to retrieve data from multiple dimension tables.
What is the difference between Snowflake tasks and Snowpipe? ›Snowpipe then copies files into a queue before loading them into an internal staging table(s). Snowflake Streams continuously record subsequent changes to the ingested data (for example, INSERTS or UPDATES), and Tasks automate SQL queries that transform and prepare data for analysis.
What is the difference between bulk load and snowpipe? ›Bulk Load vs Snowpipe
Bulk load requires a warehouse to execute the COPY commands whereas Snowpipe uses Snowflake provisioned resources. Hence bulk load bills each active warehouse per second whereas Snowpipe billing is based on the compute resources used while loading the files.
Accounts are charged based on their actual compute resource usage; in contrast with customer-managed virtual warehouses, which consume credits when active, and may sit idle or be overutilized.
Can Snowpipe be suspended and resumed? ›
At the pipe level, the object owner (or a parent role in a role hierarchy) can set the parameter to pause or resume an individual pipe. An account administrator (user with the ACCOUNTADMIN role) can set this parameter at the account level to pause or resume all pipes in the account.
Is Snowpipe managed by Snowflake? ›Snowpipe is the Continuous Data Ingestion service offered by Snowflake. Snowpipe initiates Data Loading from files the moment they are available in a stage. This allows you to load data from files in micro-batches rather than manually executing COPY statements on a schedule to enable large load batches.
What is the best file size for Snowpipe? ›Snowpipe is typically used to load data that is arriving continuously. File sizing plays an important role in Snowpipe's performance. The recommended file size for data loading is 100-250MB compressed, however, if data is arriving continuously, then try to stage the data within one-minute intervals.
What is the maximum file size for Snowpipe? ›Consider auto-ingest Snowpipe for initial loading as well. It may be best to use a combination of both COPY and Snowpipe to get your initial data in. Use file sizes above 10 MB and preferably in the range of 100 MB to 250 MB; however, Snowflake can support any size file.
What warehouse does Snowpipe use? ›If you compare how warehouse is used in these 2 approaches, you would notice that (1) Snowpipe approach uses Snowflake managed warehouse, which automatically optimize computing resources used in your workload. This can reduce warehouse cost. See below URL for more about Snowpipe cost.
What is Snowflake weakness? ›The cons of using Snowflake include: Lack of synergy: While it can run in the Amazon, Google, and Microsoft public clouds, it isn't a native offering. Each of these public clouds offers its own cloud data warehouse solution: Amazon Redshift, Google BigQuery, and Microsoft Azure SQL Data Warehouse, respectively.
How many days required to learn Snowflake? ›About Snowflake Course
This particular course is a 1 month advanced training program on the Snowflake. Snowflake is the best cloud based data warehousing & analytics tool.
Snowflake supports both ETL and ELT and works with a wide range of data integration tools, including Informatica, Talend, Tableau, Matillion and others.
How do you handle errors in Snowpipe? ›- Step 1: Checking Authentication Issues. The Snowpipe REST endpoints use key pair authentication with JSON Web Token (JWT). ...
- Viewing the COPY History for the Table. ...
- Step 3: Checking the Pipe Status. ...
- Step 4: Validate the Data Files.
Snowpipe is Snowflake's server less, automated ingestion service that allows you to load your continuously generated data into Snowflake automatically. Automated data loads are based on event notifications for cloud storage to notify Snowpipe at the arrival of new data files to load.
What is the difference between copy command and Snowpipe? ›
Snowpipe in Snowflake uses the COPY command but with additional features that let you automate this process. Snowpipe also eliminates the need for a virtual warehouse, instead, it uses external compute resources to continuously load data as files are staged and you are charged only for the actual data loaded.
How difficult is to learn Snowflake database? ›New Snowflake accounts can be configured to run on any of the top three public cloud providers: AWS, Microsoft Azure, and Google Cloud Platform. Let's now explore some of the reasons that might make you consider Snowflake for your business. Snowflake is super easy to learn and use, with an almost zero admin footprint.
Is Snowflake normalized or denormalized? ›The snowflake schema is a fully normalized data structure. Dimensional hierarchies (such as city > country > region) are stored in separate dimensional tables. On the other hand, star schema dimensions are denormalized. Denormalization refers to the repeating of the same values within a table.
What makes Snowflake better than AWS? ›While both AWS and Snowflake are highly beneficial and well-suited for various applications, Snowflake has the edge over AWS for a few reasons. Snowflake's architecture combines cloud and SQL query engine, while AWS only uses nothing-shared databases, giving Snowflake high-performance speed.
Which is the most powerful role in Snowflake? ›The account administrator (i.e users with the ACCOUNTADMIN system role) role is the most powerful role in the system. This role alone is responsible for configuring parameters at the account level.
Which ETL tool is good for Snowflake? ›Snowflake and ETL Tools
Snowflake supports both transformation during (ETL) or after loading (ELT). Snowflake works with a wide range of data integration tools, including Informatica, Talend, Fivetran, Matillion and others.
Snowpipe - It is used for continuous data load into Snowflake tables. Querying on Snowflake tables for analytics is faster. Storage is consumed as data is maintained in micro partitions. External Tables - It is used for immediate access of data without loading data into Snowflake tables.
How do you use a Snowpipe in a Snowflake? ›- 1) Set up a separate database. ...
- 2) Set up a schema to hold our source data. ...
- 3) Create a Table. ...
- 4) Create the File Format. ...
- 5) Create an external stage pointing to your s3 location. ...
- 6) Review staged files and select data from the files. ...
- 7) Test loading data into the table. ...
- 8) Create the Snowpipe.
Staged File Storage (for Data Loading)
To support bulk loading of data into tables, Snowflake utilizes stages where the files containing the data to be loaded are stored. Snowflake supports both internal stages and external stages.
In addition to resource consumption, an overhead is included in the utilization costs charged for Snowpipe: 0.06 credits per 1000 files notified or listed via event notifications or REST API calls. This overhead is charged regardless if the event notifications or REST API calls resulted in data loaded.
How much does Snowpipe charge for files in queue? ›
Snowpipe charges approximately 0.06 USD per 1000 files queued.
Is Snowflake billing per second or hour? ›Because Snowflake utilizes per-second billing (with a 60-second minimum each time the warehouse starts), warehouses are billed only for the credits they actually consume when they are actively working.
How long does Snowflake keep SnowPipe load history? ›This Account Usage view can be used to query the history of data loaded into Snowflake tables using Snowpipe within the last 365 days (1 year). The view displays the history of data loaded and credits billed for your entire Snowflake account.
Is SnowPipe a serverless service? ›SnowPipe is also called as serverless function. This means that instead of using a virtual warehouse, computing resources are managed by Snowflake itself. In this case, you don't have to worry about virtual warehouses. Suppose you have a specific bucket, and you can manually copy data from that bucket to your table.
How long does it take for Snowflake warehouse to suspend? ›If you enable auto-suspend, we recommend setting it to a low value (e.g. 5 or 10 minutes or less) because Snowflake utilizes per-second billing. This will help keep your warehouses from running (and consuming credits) when not in use. However, the value you set should match the gaps, if any, in your query workload.
Why use Snowflake instead of Azure? ›Snowflake offers native connectivity to multiple BI, data integration, and analytics tools . Azure comes with integration tools such as logic apps, API Management, Service Bus, and Event Grid for connecting to third-party services. Both the user and AWS are responsible for securing data.
Do data engineers use Snowflake? ›With Snowflake, data engineers can spend little to no time managing infrastructure, avoid capacity planning and concurrency handling, and focus on building reliable, enterprise-ready data pipelines.
Why use Snowflake over Oracle? ›Snowflake is designed to be easy to use and manage, while Oracle usually requires a database administrator and manual tuning for performance. For example, when scaling a data warehouse on Snowflake, it can automatically optimize queries for better performance. In contrast, Oracle usually requires manual optimization.
What file format is most performant in Snowflake? ›Loading data into Snowflake is fast and flexible. You get the greatest speed when working with CSV files, but Snowflake's expressiveness in handling semi-structured data allows even complex partitioning schemes for existing ORC and Parquet data sets to be easily ingested into fully structured Snowflake tables.
What is the maximum blob size in Snowflake? ›The maximum length is 8 MB (8,388,608 bytes).
What is the maximum CSV size for Snowflake? ›
The maximum size for each file is set using the MAX_FILE_SIZE copy option. The default value is 16777216 (16 MB) but can be increased to accommodate larger files. The maximum file size supported is 5 GB for Amazon S3, Google Cloud Storage, or Microsoft Azure stages.
What is the difference between Snowpipe and Kinesis? ›Snowpipe vs.
Platform-specific: Kinesis Data Firehose is designed for AWS users and integrates with Amazon Redshift. Snowpipe is purpose-built for Snowflake, making it more suitable for Snowflake users across different cloud providers.
The Snowflake Data Cloud includes a pure cloud, SQL data warehouse from the ground up. Designed with a patented new architecture to handle all aspects of data and analytics, it combines high performance, high concurrency, simplicity, and affordability at levels not possible with other data warehouses.
What Fortune 500 companies use Snowflake? ›Leading cloud computing giants like Microsoft (MSFT), Amazon (AMZN), and Alphabet's Google (GOOGL) today allow their customers to use Snowflake (SNOW) software. About two-fifths of Fortune 500 companies use Snowflake software today.
How do you start a task in a Snowflake? ›When the EXECUTE TASK command triggers a task run, Snowflake verifies that the role with the OWNERSHIP privilege on the task also has the USAGE privilege on the warehouse assigned to the task, as well as the global EXECUTE TASK privilege; if not, an error is produced.
How do I start snow in SQL? ›- Prerequisites.
- Log into SnowSQL.
- Create Snowflake Objects.
- Stage the Data Files.
- Copy Data into the Target Table.
- Query the Loaded Data.
- Summary and Clean Up.
- Step 1: Checking Authentication Issues. The Snowpipe REST endpoints use key pair authentication with JSON Web Token (JWT). ...
- Viewing the COPY History for the Table. ...
- Step 3: Checking the Pipe Status. ...
- Step 4: Validate the Data Files.
The 5 Steps of the Snowflake Method. 1. Choose a premise and write it up in a one-sentence summary. This single sentence will be the foundation for your entire novel's outline.
How do you check a snowflake pipe? ›To determine the current status of a pipe, query the SYSTEM$PIPE_STATUS function.
What is Snowpipe in snowflake? ›Snowpipe is the Continuous Data Ingestion service offered by Snowflake. Snowpipe initiates Data Loading from files the moment they are available in a stage. This allows you to load data from files in micro-batches rather than manually executing COPY statements on a schedule to enable large load batches.
What SQL is needed for Snowflake? ›
Snowflake supports standard SQL, including a subset of ANSI SQL:1999 and the SQL:2003 analytic extensions. Snowflake also supports common variations for a number of commands where those variations do not conflict with each other.
How do I start SnowSQL from command prompt? ›- Open a terminal Window.
- Use the below command to connect to Snowflake. $ snowsql -a <account_identifier> -u <username> Parameter. Description. -a <account_identifier> ...
- Enter the password of the account in interactive password prompt to login.
Snowpipe: Is a service that reads new files placed in a particular S3 Bucket or folder and performs a defined copy command to a table. Tasks: Snowflake tasks are an offering that enables one to execute a single SQL statement or a stored procedure either on a schedule or when a condition is fulfilled.