For starters, Snowflake is a data platform present in the cloud. It is also the first cloud-based data platform to be ever made.
Because it can readily manage both structured and unstructured data, its design facilitates the data professionals to construct data warehouses and cloud data lake-houses.
Because Snowflake is cloud-based, developers can get the best benefit from elasticity and scalability without having to fret about large upfront costs, reliability, or the difficulty they might face while operating the system.
What is the Purpose of Using Snowflake?
When we talk about cloud data warehouses, many different solutions are available, and each has its own set of use cases.
However, it has been observed that more and more consumers prefer to choose or move to Snowflake because of its numerous valuable features and overall simplicity in its usage.
Here are a few reasons people are increasingly using Snowflake DB:
Cloud Agnostic
Snowflake is not restricted to a particular cloud provider. Instead, businesses can witness effortless growth in their data warehouse across many places like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP), which means that businesses do not have to waste time setting up hybrid cloud solutions every time. Learn more about Snowflake from this Snowflake Online Training to get ahead in your career.
Performance and Speed
It is a given fact that Snowflake is designed for speed. It sees its nexus with the underlying architecture designed to conduct analytical queries.
Furthermore, given that the cloud is elastic, one can easily scale up their virtual warehouse to make the best out of extra computational resources if one needs to load data faster or execute a high volume of queries.
User-Friendly UX/UI
Snowflake’s UI/UX is quite simple, even if you are a novice, and it is also packed with features. It’s simple to locate past queries if you’ve run any or even other user queries that are now in the execution process (depending on your access level).
There are also many other remarkable capabilities accessible by button clicks rather than being hidden behind the settings bar and configuration drop-downs that you may not be quite aware of. This makes Snowflake incredibly user-friendly, and it is frequently found that a lot of analysts like working with Snowflake.
Reduction in Administration Overhead
Many organizations do not have a specific need for handling hardware and can take advantage of scaling; cloud services generally require less administration overhead.
Furthermore, since Snowflake is a SaaS setup, upgrades and general administration are the sole responsibility of Snowflake rather than the organization.
Anybody may undertake a major chunk of this administration and scalability with all the necessary permissions without understanding servers, command lines, or even code.
This means you should click around and frequently change the serving sizes. You can suddenly find yourself paying a substantially larger bill because of what you did.
On-Demand Pricing
Snowflake provides the user with on-demand pricing, which means they only have to pay for the quantity of data they store and the compute hours/minutes they consume.
Unlike a standard data warehouse, Snowflake can allow users to configure idle time so they don’t have to pay if the warehouse is inactive.
Supports a Plethora of File Formats
Nowadays, Data is not just available in CSV, XML, pipe-delimited, and TSV formats only. Snowflake, in turn, provides detailed and structured data and a broad range of semi-structured data formats that include Parquet, Avro, and JSON.
What are the different Types of Features the Snowflake Offers?
One more great reason for you to switch to Snowflake is that it offers a plethora of features to its users:
Snowflake Tasks
Snowflakes tasks are individual SQL statements that are specifically put to use for analytics reporting. This accomplishment is achieved by joining or adding rows into a specific report table.
The execution of these jobs takes place at a certain time and ignores any other tasks that take place at the same time. The jobs are executed with the use of cron expressions, and it enables the user to make a choice of their time zone as well as daylight saving time features.
Tasks begin with a fundamental work that, like a tree, can connect to other additional works. Rather than a DAG structure, the tasks get their derivation from a single node to a specific end node destination.
The maximum number of jobs per tree is permitted to be around 1000, which also includes the root task. The user must remember that each assignment must come with a single role.
Snowflake Snowpipes
Whenever you see that Data is available at a stage, you can easily load it in micro-batches using a Snowpipe. This is a quicker option if you compare it with the typical technique of manually running a COPY command.
When it comes to a COPY statement, users put out a definition of the referenced pipe, which is fundamentally a Snowflake object. The most remarkable thing about a Snowpipe is that it can easily and seamlessly handle any form of organized data.
Other notable features of Snowflake include cloud messaging automation and contacting REST APIs. When there is an arrival of fresh data loads, an event notification gets sent to cloud storage.
Snowpipe can copy a file and load it into a queue in the target table after this happens. The client application then makes a specific request to make the REST API available to the public.
This operation gets prompted by a list of many filenames and pipe objects. After this, new Data is loaded from the queue solely based on the parameters defined within the pipe.
Snowflake Streams
A Snowflake stream, for starters, is a data manipulation language recorder. It carries out the task of deletions, insertions, updates, and metadata associated with just about any modification.
This leads to the enablement of actions to be taken by making use of the recorded change data. The Stream does not contain table data, but it does hold several offsets for the source table.
This specific event occurs when the source table’s versioning history is utilized. The Stream can’ mark’ a point when any changes occur, such as the ones you can see in the source table. Streams may also be added and discarded per the requirements, giving the user a lot of flexibility.
When DML statements face a commitment to the table, a new table version comes into being. This procedure provides for the facilitation of minimum modifications from current offsets to the current table version.
When SQL queries put up questions regarding an explicit transaction, the timestamp is looked for the stream advance point. When existing stream statements fill up a new table row, this holds for both CREATE TABLE and DML.
Snowflake Solution with Streams and Tasks
Snowflake’s time travel function is responsible for driving its table stream, providing quite recent row modifications. It works quite similarly to a query in that right after the Data is under consumption, it proceeds ahead and updates metadata for every row.
As stated earlier, task execution using SQL statements and offering specified actions operate via a tree structure. When used at the same time as each other, these functions form an ELT pipeline.
This solution is ready-made, and it also includes Table Stream and Task. When used at the same time, you may capture changes such as the ones listed hereunder:
- Inserts
- Deletes
- Updates
- Metadata
It’s a low-cost option that lets you do away with many third-party tools. Snowflake has a high degree of utilization and current functionality, making it a preferable alternative for many needs of an organization.
Is Snowflake the right fit for you?
Considering why you should utilize Snowflake, you must consider the variety of options and low-cost alternatives available to replace a complete and obsolete system.
You receive all sorts of the security and advantages of a cloud environment, as well as a lot of flexibility and control choices to enable you to design your solutions.
If you’ve explored the option of deployment of cloud-based data solutions, Snowflake could be a perfect fit for your organization. Snowflake also provides that option even if you have a current requirement for a single-tenant solution due to business restrictions.
Snowflake also has the provision for an on-premises data solution that, by making the utilization of the cloud, eliminates the requirement for physical data storage.
You will also be able to apply scalable features that can make the operation of semi-structured data similar to structured data. However, the semi-structured data size is roughly estimated to be 16MB.
Snowflake can facilitate loading and querying data formats, including XML and JSON, and it also makes a support system for independent computing and storage.
However, you cannot import pdf files, audio files, or photos into Snowflake unless and until you convert them to character strings or binary files, which face a restriction of up to 8 MG and 16 MB, respectively.
The ideal file size is 1 GB, although Snowflake support suggests breaking down bigger files for the best and enhanced performance.
Cons of Using Snowflake Data Warehouse
After seeing the benefits, let us have a look at the cons of Snowflake Data Warehouse:
Unstructured Data Support
Snowflake now has an extensive support system of semi-structured and structured data. However, we anticipate adding unstructured data support in the future.
Bulk Data Load
It’s quite difficult for you to move data from SQL Server to Snowflake. Snowflake provides Snowpipe for extensive and continuous data loading; however, it isn’t always the ideal option for your organization.
There is a need, for instance, for a more robust option. The provider must receive continuous data into Snowflake from hundreds of potential sources; everything can get automation.
No constraints on Data
While Snowflake is extremely scalable and enables customers to make payments for exactly what they use, there are no data restrictions in computing or storage.
A lot of different organizations may find it all too simple to overuse their services, only to discover that there is a specific error after billing.
Data Modernization
Businesses have unlimited access to current computing capabilities they did not use before by transferring data from in-house servers to cloud databases.
Snowflake enables companies to better utilize their data by conducting analysis and uncovering insights to influence their choices and operations.
Data Protection and Security
Organizations often keep critical, sensitive data on their computers, making them vulnerable to cybercrimes.
Snowflake data warehouses, for example, provide extensive security capabilities to decrease the risks of data breaches and misappropriation by providing economical and security solutions.
Operational Performance and Cost
To possess the storage capacities and computational power that Snowflake delivers in-house, an enterprise would have the basic requirement to invest in IT equipment and skills.
Snowflake enables these organizations to use their services to the degree they require without having to fret about hardware and maintenance expenditures. For most businesses, the advantages that cloud computing provides surpass the subscription expenses.
Conclusion:
There are a lot of benefits attached to Snowflake, especially for a new organization. It offers many unique solutions and is much more intricate than a traditional data platform. It offers a lot of convenience and capabilities and makes it easy for businesses to operate without their data warehouses.
Author Bio
Meravath Raju is a Digital Marketer, and a passionate writer, who is working with MindMajix, a top global online training provider. He also holds in-depth knowledge of IT and demanding technologies such as Business Intelligence, Salesforce, Cybersecurity, Software Testing, QA, Data analytics, Project Management and ERP tools, etc.
