Snowflake is one of the emerging cloud data warehousing tools in the world. After the covid pandemic, there is a huge space for Cloud data warehousing system because it doesn’t have a physical location and convenient managing system in the cloud. Snowflake provides a cloud data warehouse that is so much faster, easy to use, and more flexible. With its standard features, it soon became the #1 leader in data warehousing solutions in the emerging world.
In this Snowflake tutorial we will learn about Snowflake Cloud Data Warehouse tool, Architecture, Unique features, and how to create virtual data warehouses.
So let’s get started
Snowflake Tutorial for Beginners to Advanced Guide
What is Snowflake?
Snowflake is Cloud Data Warehouse Software-as-a-Service (SaaS). It provides all the relative information about data management which includes data storage, data processing, and data analytics in an effective way and easy to use.
Definition of Snowflake Cloud Data Warehouse
Snowflake is a Dedicated SaaS offering service that doesn’t have any physical presence to install, configure or manage the data.
Snowflake usually works on cloud infrastructure. In Snowflake service all the components run and execute in public cloud infrastructures (other than optional command line clients, connectors, and drivers. It can’t run on private cloud infrastructures (hosted or on-premises).
Snowflake is not a kind of packaged software that can be installed by a user. It manages all factors of software updates and installation.
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Snowflake’s architecture is a hybrid of traditional shared-nothing and shared-disk database architectures. In this snowflake tutorial we will learn more about architecture of snowflake.
By using a central data repository Snowflake enables persisted data from all commute nodes in the existing platform.
In this process, Snowflake uses Massively Parallel Processing (MPP) to compute clusters (to store sets of data). By using this architecture model Snowflake became easy to use as a data management system.
Snowflake Architecture contains 3 Key layers
- Cloud Services
- Query Processing
- Database Storage
#1 Cloud Services
After loading the data into Snowflake, Snowflake will reorganize the data into an internally optimized compressed columnar format. Snowflake stores this optimized data in cloud storage.
Snowflake manages all aspects of how this data is stored: Snowflake takes care of organization, file size, structure, compression, metadata, statistics, and other aspects of the data. Data objects stored in Snowflake are not directly visible or inaccessible to customers; They can only be accessed through SQL query operations run by Snowflake.
#2 Query Processing
The execution of the query runs in the processing layer. Snowflake uses “virtual warehouses” to process queries. Each virtual warehouse is an MPP compute cluster, consisting of multiple compute nodes distributed by the cloud provider’s Snowflake.
Each virtual warehouse is a separate compute cluster and does not share compute resources with other virtual warehouses. As a result, each virtual store will not affect the performance of other virtual stores.
#3 Database Storage
The cloud services layer is a collection of services that coordinate activities in Snowflake. These services combine all the different Snowflake components to handle user requests from login to query dispatch. The cloud service layer also runs on compute instances deployed by the cloud provider’s Snowflake.
Snowflake services usually managed in this architecture component are follows
- Cloud Infrastructure management
- Management of Metadata
- Query Optimization and Query Parsing
- Access Control
How to Connect Snowflake Cloud Data Warehouse
In this Snowflake Tutorial we will learn Connecting to Snowflake very clearly with practical explanation
- A web-based user interface from which you can access all aspects of managing and using Snowflake.
- Command-line clients (such as SnowSQL) can also access all aspects of managing and using Snowflake.
- Other applications (such as Tableau) can use ODBC and JDBC drivers to connect to Snowflake.
- It can be used to develop native connectors for applications that connect to Snowflake (e.g. Python, Spark).
- It can be used to connect applications such as ETL tools (such as Informatica) and BI tools (such as ThoughtSpot) to third-party Snowflake connectors.
Snowflake Supported Cloud Platforms
Snowflake is provided as software as a service (SaaS), which runs entirely on cloud infrastructure. This means that all three-tier architectures (storage, computing and cloud services) of Snowflake are fully deployed and managed on the selected cloud platform.
Snowflake cloud data warehousing account can be hosted on any of the below mentioned popular cloud platforms.
- Google Cloud Platform
- Microsoft Azure
- Amazon Web Services
How to Load Data into Snowflake?
There are several ways available to store data into Snowflake
- Internal Stages
- Google Cloud Platform
- Amazon S3
- Azure Blob Storage
Internal Stages for Loading Data into Snowflake
Snowflake maintains the following types of stages on its account:
The user stage is assigned to each user to store files. This type of stage is designed to store files organized and managed by a single user, but which can be loaded into multiple tables. The user phase cannot be changed or deleted.
A table setting can be used for each table created in Snowflake. This type of stage is designed to store files organized and managed by one or more users, but only loaded into a single table. The table phase cannot be modified or deleted.
Note that the table phase is not a separate database object; it is not a database object. Rather, it is an implicit stage associated with the table itself. The table stage does not have its own privileges granted. To temporarily store files in the table notebook, list files, query or delete them in the notebook, you must be the table owner (with OWNERSHIP privileges on the table).
The named inner phase is the database object created in the schema. This type of test can store files that are organized and managed by one or more users and loaded into one or more tables. Since the naming phase is a database object, security access control privileges can be used to control the ability to create, modify, use, or delete them. Use the CREATE STAGE command to create a stage.
Use the PUT command to upload files from the local file system to any type of internal stage.
Bulk vs Continuous Loading
Snowflake provides the main solutions for Data loading into it. It majorly depends on the size of the data and its frequency of loading.
Continuous Loading Using Snowpipe
This process is specially designed for the low volumes of data i.e. micro-batches and proportionally makes them to analyze very carefully. Snow Pipe loads data within a few minutes after the data files are uploaded to a stage and submitted for data ingestion. This ensures to have the up to date results, as soon as the raw means original data available.
Compute resources for Snowpipe
Snowpipe uses computing resources provided by Snowflake (ie, the serverless computing model). The resources provided by these snowflakes will be automatically resized and scaled up or down as needed, and will be charged and itemized per billing per second. Data ingestion is charged based on actual workload.
Simple transformations during a load
The COPY statement in the pipeline definition supports the same COPY conversion options as when loading data in batches.
Additionally, the data pipeline can use Snowpipe to continuously load micro-batches of data into temporary tables for conversion and optimization using Change Data Capture (CDC) information in automated tasks and flows.
Data pipelines for complex transformations in Snowflake Cloud Data Warehouse
The data pipeline can apply complex transformations to loaded data. The workflow generally uses Snowpipe to load the “raw” data into a temporary table and then uses a series of table flows and tasks to transform and optimize the new data for analysis.
Alternatives for Loading Data into Snowflake
It is not always mandatory to load into Snowflake before executing queries.
Data Lake Process (External Tables)
External tables can query existing data stored in external cloud storage for analysis without having to upload it to Snowflake first. The true source of the data remains on external cloud storage. The dataset implemented in Snowflake through the materialized view is read-only.
This solution is particularly useful for accounts that have a large amount of data stored on external cloud storage and only want to view a portion of the data. For example, the most recent data. Users can create materialized views on a subset of this data to improve query performance.
How to Create a Cloud Warehouse in Snowflake
Now we’re going with a Virtual Warehouse to execute essential queries and load sample data. If you want practical experience you need Snowflake credits. For further details visit this link – Snowflake Virtual Warehouse Creation