­How Snowflake Can Transform the Way Data Engineering Works

Data management has become central to business growth in the present-day world. Business enterprises regularly require the harvesting and storage of new and old data. It has become essential to organize the ever-increasing chunks of data to make informed corporate decisions. The importance of the role of data engineers cannot be overstated here.

Data engineers are the personnel who are responsible for developing data warehouses, algorithms, pipeline architecture, and data validation methods. Cloud platforms like Snowflake make the tasks of data engineers significantly easier. Dive into this article to know more about this data platform that is becoming increasingly popular.

What is Snowflake?

Snowflake is a SaaS data warehousing platform. It is cloud-based and runs smoothly on all three major cloud providers like Azure, AWS, and Google. Snowflake provides a centralized platform that singly manages various aspects of data collection, storage, organization, file size adjustments, compression, statistics, and Metadata.

It is low-maintenance and does not require manual configuration. By affording its users secure access to and regulation of data, Snowflake simplifies the data engineering-related tasks a great deal.

Snowflake is an efficient alternative to traditional data warehouse architecture. The on-premise architecture can be cumbersome, time-taking, and expensive. It demands the development and up-gradation of data mats from time to time to ensure the availability of data for all the users. One also has to put in additional effort to inculcate the semi-structured data into the system. Not to mention, questions often arise regarding the reliability and delivery of data.

However, with Snowflake you can store and analyze diverse data, be it structured or semi-structured, in little time and with little effort.


Snowflake has a multi-cluster infrastructure. Each node of the cluster locally stores a part of the data set. Snowflake architecture can be divided into three layers.

· First layer is the database storage site which contains all the data. The data can be structured and semi-structured in form.

· Compute layer consists of virtual warehouses. The virtual warehouses can retrieve data from databases but work independently of each other. This phenomenon ensures non-disruptive scalability i.e., there is no need to redistribute data if it is being accessed by multiple users.

· Cloud Services layer is the executive of the platform. It utilizes ANSI SQL and coordinates the entire system. Cloud Services layer manages tasks like authentication, infrastructure, Metadata, optimization, and access control.

It can be suggested that Snowflake effectively combines the desirable features of Shared Disc architecture and Shared Nothing architecture under one platform.

Snowflake for data engineering

Data pipelines make the backbone of any data mining and storage system. Data engineers have much to gain from a platform like Snowflake when it comes to building high-functioning pipelines.

It is so because Snowflake provides pipelines that are agile and flexible. They can rapidly ingest any form of raw data from the sources and organize it for its destination.

Batch and continuous pipelines

Snowflake includes both batch and continuous pipelines. The data can be ingested either in bulks or in micro-bulks, and fed into the system gradually. Data engineers can also process data within the system and save the output in a structured form.

Build data pipelines in the language of your choice

Data engineers can build data pipelines in the language of preference with the help of Snowflake. Popular programming languages like Java and Scala are supported by the system.

No more resource contention

Snowflake’s independent clusters make sure that conflicts over shared resources do not arise. Snowflake integrates data while ensuring that its simultaneous access for multiple users is not affected. This feature eventually increases the scalability of the platform as well.

Automatic data processing elements

Snowflake also has an in-built ability to absorb and manage the data processing elements. It handles errors, duplications, scaling, and regulation of workloads on its own. Data pipelines, therefore, exhibit better performance as they rely heavily on automated processes.

Comprehensive Data Collection and Management

Snowflake successfully handles different aspects of data handling like resource management, data protection, configuration, and authentication.

Delivery of Complete and Accurate Data

There are no needless delays in the delivery of data. Neither are there any snags in accessing information. Snowflake steers clear of complex architecture, it is easier to trace the origin of data and establish reliability. In this way, with the help of data engineers, analysts can transform the available data into useful insights for the business in a more accurate and efficient manner.

No Need to Set Up Hardware or Software

As Snowflake is run on public cloud infrastructure, data engineers don’t need to set up physical infrastructure on-premises or offsite. No software installation is either required. Snowflake data warehouse, therefore, is highly easy to put in place and takes zero physical space.

Snowflake lets you Focus on Data Delivery

When working with legacy infrastructure, data engineers spend more time controlling the unlimited amount of incoming data than they do in delivering it. However, by using Snowflake, they can give due attention to data provision instead of infrastructure management.

Data from talend to Snowflake

Talend lets you establish the reliability of your data by enabling you to track its origin and course. Through its integration services, Snowflake allows data engineers to transmit the data from talend to its system seamlessly. There is no need to do additional coding for the process. One can easily utilize data pipelines generated through talend to get the job done. In this way, transmission of data becomes easy.

Talend pipelines are utilized for data ingestion in Snowflake. Once the transmission is done, the course of data processing, organizing, protecting, building, cleansing, and updating of data sets begins. Snowflake is helping the team accelerate its projects by allowing them rapid processing and collection of data.

Final Thoughts

Snowflake can bring numerous benefits to the data engineering sector. It is low on complexity and high on flexibility when it comes to deployment and functioning. It reduces the intricacies of transmitting the raw data from sources to a data warehouse. Therefore, it can be suggested that Snowflake makes a valuable asset for data engineers.

Author bio:

Hassan Sherwani is the Head of Data Analytics and Data Science working at Royal Cyber. He holds a PhD in IT and data analytics and has acquired a decade worth experience in the IT industry, startups and Academia. Hassan is also obtaining hands-on experience in Machine (Deep) learning for energy, retail, banking, law, telecom, and automotive sectors as part of his professional development endeavors.



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