Snowflake Tutorial – Snowflake is a leading cloud-based data warehouse offering SaaS(Software as a Service). It is available on Azure, Google Cloud, and AWS platforms and has roots in the SQL platform. In contrast with the native database solutions, Snowflake offers a faster, flexible, and easy-to-use data warehouse.
Snowflake takes advantage of offering certain cloud paradigms in such a way as to handle the storage using blob storage. Furthermore, to position itself as a data warehouse without management, which means that it is easy to install, configure, and administer without a lot of technical knowledge.
In this Snowflake Tutorial, we’ll learn the following concepts.
- What is the Snowflake?
- What does Snowflake do?
- Why is Snowflake so popular?
- How to load Snowflake?
- How to connect to Snowflake?
- What is Snowflake Architecture?
- What are the Snowflake benefits?
- What ETL tools are there to use with Snowflake?
- How is Snowflake different from AWS?
- How do I start learning Snowflake?
- Does Snowflake use SQL?
- Is it easy to learn Snowflakes?
- What are the on-demand Snowflake certifications?
What is the Snowflake?
Snowflake is a data warehouse that enables storing data, processing data, and analytics solutions in a faster and more authentic way when compared with native offerings. It provides all the functionalities and features of an enterprise analytic database along with several additional features and capabilities.
What does Snowflake do?
Answer: Snowflake is a (Software as a Service)SaaS platform that loads, analyses, and reports on massive volumes of data. It requires no hardware to deploy and can be done in the cloud within minutes. It is the perfect solution for not only data warehousing but also for data lakes, data engineering, and developing data applications. Also, it obeys the Pay-Per-Use mechanism, thus an affordable solution.
Why is Snowflake so popular?
Snowflake is a cloud-agnostic that comes with seamless and unlimited scalability across various cloud platforms, including AWS, Azure, and Google. It is so popular because it comes with simplified data processing, which enables users to blend, analyze, and transform data across distinct data structures with a single language. It offers reliable, scalable, and dynamic computing power with charges purely based on your usage. Also, it empowers users to analyze a wide range of data structures including, JSON, CSVs, Parquet, XML, Avro, and blend them parallel using SQL language.
How to load Snowflake? – Snowflake Tutorial
Snowflake loads data based on the following concepts and tasks to import into the database tables.
- SnowSQL is for bulk loading
- Snowpipe is to automate the bulk loading of data
- WebUI is for limited data
- Third-party tools is to bulk load data from external sources.
- SnowSQL for bulk loading
Loading of bulk data is performed in two stages, the first one is staging files, and the final one is loading data.
Staging files: It refers to uploading the data files to locations from where the Snowflake can access them. The next thing is to load data from stage files to tables. Here, Snowflake allows users to stage files on internal locations known as stages. The internal stage offers secure data storage without using any external locations.
Loading data: The virtual data warehouse is required for loading data to Snowflake, and then the warehouse extracts data from each file & inserts it in the table in the form of rows.
- Snowpipe to automate bulk loading of data
Snowflake to load bulk data use Snowpipes which are specifically for staged files in distinct external locations. The Snowpipes uses the COPY command with several additional features to automate the process and uses the external computing resources to load data continuously, thus eliminating virtual warehouse requirements.
- Web Interface
This is one of the best options to load data to Snowflake. It is loading data through the Web Interface in a simple yet effective way. Here, you need to select the table which you want to choose and click the load button. It simplifies the loading process by combining both the staging and loading data at a time and deletes the staged files automatically after loading.
- Third-party tools – ETL/ELT
This is the perfect option to load bulk data loads. It processes the data using third-party tools like ETL/ELT, which provides an ever-expanding scope for the application ecosystem and services to load data from distinct external data sources.
Related Article: Snowflake Interview Questions And Answers
What is Snowflake Architecture? – Snowflake Tutorial
Snowflake’s architecture is a traditional hybrid shared-disk database architecture. Similar to other shared-disk architectures, Snowflakes includes a respiratory for centralized data management with persisted data, which can be accessed from all the compute-nodes involved in the platform. But, in the same manner, the Snowflakes process the architecture of shared-nothing. It processes the queries with MPP(Massive Parallel Processing) clusters to compute each node in the cluster, stores a data set locally, and allocates a portion for it. This approach is responsible for managing the data simply using the shared-disk architecture along with performance and other features of the shared-nothing architecture.
The typical Snowflake’s architecture contains three key layers:
- Database Storage
- Query Processing
- Cloud Services
Database Storage: When the user loads data to the Snowflake, it organizations the data into internally optimized and compresses that into the columnar format. It stores this data in cloud storage. Snowflakes manage all their aspects according to the data stored. The typical storage sequence includes the organization, structure, file size, data compression, metadata, data statistics, and other data storage aspects, which Snowflake handles. The objects of data that are stored by Snowflake are neither visible nor accessible by the users and are only accessible through the operations of SQL query running through Snowflake.
Query Processing: The Query execution performs layer processing. However, Snowflake processes the queries through virtual warehouses. Here, each warehouse is an MPP computing cluster, which is composed using multiple nodes allocated by the Snowflake cloud provider. Furthermore, each virtual warehouse is an independent compute cluster, which does not share compute resources with other data warehouses. It results in no impact on the virtual warehouse performance.
Cloud Services: This is a collected layer of services, which coordinates with the activities across Snowflake. These services will tie together all of the various Snowflake components to process the requests of users, from login to query dispatch. This cloud services layer also executes on compute instances that are provisioned by the Snowflake from cloud providers. This layer manages the following services.
- Infrastructure management
- Metadata management
- Query parsing and optimization
- Access control
How to connect to Snowflake? – Snowflake Tutorial
As we have already mentioned, Snowflakes allows multiple ways to connect to its service. Users can connect to Snowflake in the following ways.
- Through the web-based interface by the user from which you can manage all the aspects from which you can access the Snowflake.
- Using Command-line Clients like SnowSQL, which can access and manage all the aspects of data using Snowflake.
- Through ODBC and JDBC drivers, which are used by other applications (like Tableau) for connecting with Snowflake.
- Traditional connectors (including Python, Spark) can be used for developing applications to connect with Snowflake.
- Finally, using Third-party connectors, which are used for connecting with applications, including ETL tools like Informatica and Business Intelligence tools like ThoughtSpot to Snowflake.
What are the Snowflake benefits? – Snowflake Tutorial
Snowflake is a specialized construction for the cloud, which is designed for addressing several problems that arise in the traditional hardware-based warehouses for data, including data transformation problems, scalability issues, and failures or delays arising due to higher volumes of queries. The following are the five popular ways how a Snowflake data warehouse can provide to your business.
Storage and structured and semistructured data support: You can use the Snowflake combined structure and semistructured data for analyzing and loading data to the cloud database without the requirement of transforming or converting the data into the actual schema first. It automatically optimizes the data, which is stored and queried.
Speed and Accuracy: Snowflake’s elastic cloud nature means to load data faster as per your requirement and running higher volumes of queries that you can scale up with a virtual warehouse and take advantage of additional computational resources. Later, you can also downscale the data in the virtual warehouses and pay for the only time you used it.
Accessibility and Concurrency: With native data warehouses with a huge number of users and use cases, it is difficult to handle due to the concurrency issues with delays and failures. However, Snowflake addresses these concurrency issues with its specialized multicluster architecture. With this, the queries from one virtual warehouse will never after the other warehouse queries, and each virtual warehouse can be scaled up or down according to your requirement. Data scientists and data analysts can get their data when they need it without waiting for other processing tasks and loading time to complete.
Seamless data sharing: Snowflake’s architecture enables data sharing among its users. It also allows the businesses to share data seamlessly with any other consumers of data (however, they do not need to be a Snowflake customer) through the reader accounts, which can be directly created from the user interface. This function also allows the provider to create the Snowflake account and manage it according to the customer’s requirements.
Security and availability: The snowflake data warehouse is distributed across the available zones of a particular platform on which it runs, including Azure or AWS. It is designed for continuously operating and tolerating the network failures and component failures with very less impact on the customers. It is a SOC 2 Type II certified one, which additionally provides levels of security, including PHI data support for HIPPA customers and the available network communication encryption.
Connect your ecosystem: Suppose you are having a diverse ecosystem of data or an IoT solutions database. The one thing you required is a cloud-based data warehouse offering scalability, infinite expansion, and ease of use. The users need to have data integration solutions, which can be used for cloud operations. Using stitch to extract and data loading makes migration easy and simple for users to run the data transformations, which are stored within the Snowflake.
What ETL tools do you use with Snowflake? – Snowflake Tutorial
A large number of best ETL tools can be used with Snowflake, listed as follows:
- Hevo Data
- Apache Airflow
How is Snowflake different from AWS?
Snowflake’s computing, storage, and managing are completely separate, with the same cost charged for storing the data in S3. In comparison, AWS does not support this kind of mechanism. However, AWS attempts to address this drawback by introducing the Redshift spectrum, allowing the data in queries directly on S3. But, AWS is not as seamless as the Snowflake.
How do I start learning Snowflake?
You can learn Snowflake through an expert online platform like Tutorials Mania or through joining an Snowflake Training institute. You can select your learning path and become Snowflake certified in days, and no prior experience is required.
Does Snowflake use SQL?
Snowflake is a data warehouse and data platform that supports ANSI (the most popular standardized version of SQL). This means that all the most common operations can be used in Snowflake.
What is the future of Snowflake? – Snowflake Tutorial
According to industry experts, Snowflake remains the dominant data warehouse manager for the foreseeable future. Of course, it will probably not turn profitable over the next years and should achieve massive growth. Furthermore, the increases will not likely justify the triple-digit stock’s sales multiples.
Is it worth learning snowflakes?
If you are seeking an affordable and easier approach to learning data warehousing without vendor lock-in, then the Snowflake is your best choice. If you have huge workloads and analytics functionalities, you may want to choose Snowflake with AWS, Microsoft, or Google.
What are the on-demand Snowflake Certifications?
The Snowflake certification certificate illustrates your proficiency and eligibility to apply for core expertise to implement and migrate to Snowflake. A certified Snowflake professional will have a perfect understanding of Snowflake like a cloud warehouse with the knowledge of designing and managing secure and scalable Snowflake deliverables for leading the business solutions.
The on-demand Snowflake certifications include:
1) SnowPro Core Certification
2) SnowPro Advanced: Architect
Snowflake Tutorial Endpoints
Snowflake is one of the effective and leading Cloud data warehousing tools that overcomes various drawbacks of traditional solutions. It is gaining popularity everyday Command-line single day and is incorporating into various business industries with its performance and forecast to the future growth.
Hope You Enjoyed This Snowflake Tutorial. Feel Free To Comment If You Have Any Doubts.