in Blog

April 04, 2024

Leveraging Snowflake for Data Engineering


Edwin Lisowski

CSO & Co-Founder

Reading time:

6 minutes

As reliance on IoT technologies increases and organizations continue to realize the benefits of leveraging data to make business decisions, the amount of available data will see a tremendous surge. According to the IDC, the combined datasphere is projected to rise from 33 zettabytes in 2018 to 175 zettabytes by 2025. [1] As a consequence of this astronomical growth, data engineers will have greater responsibilities in collecting, curating, and managing this vast influx of information. To this effect, many data engineers are utilizing specially designed tools to help them collect and make effective use of data. Snowflake has emerged as a potential game changer in this effort with its impressive capabilities to handle vast amounts of data.

This article will explore the role of Snowflake in data engineering, including how it can revolutionize data engineering processes and the various industries that could see potential gains from utilizing the platform.

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What is Snowflake?

Snowflake is a cloud-based platform that provides unmatched flexibility when it comes to data management. With the Snowflake Data Cloud, you get seamless access to vast amounts of data, cutting-edge tools, as well as a wide array of applications and services. You can also use the platform to discover and share data, unite data silos, and run various analytical workloads.

Read more: Introduction to big data platforms


How can Snowflake streamline data engineering processes?

Data engineering, as a practice, is the process of creating data management systems. Most of the data collected is utilized in analytics and data science applications, including the development of machine learning models.

To be fully effective, data engineers make use of data pipelines that ingest, process, analyze, and store data. Data pipelines also provide an easy way for organizations to aggregate collected data into a single view where it can be analyzed in real time for effective, data-driven decision-making.

Unfortunately, running an effective data pipeline requires a tremendous amount of resources. As such, any organization working with limited computational resources is bound to experience several bottlenecks in its data pipelines, which may negatively impact data integration and consumption downstream.

Snowflake, on the other hand, provides unmatched performance and scalability, enabling organizations to streamline their data pipelines. It also combines complex analytics, data sharing tasks, and data lakes into an easily manageable service compatible with all major cloud services.

Data transformations in Snowflake

ETL and ELT are two of the most commonly used approaches in data integration. They outline various procedures for preparing data for analysis and further processing in order to provide actionable business insights. [2]

In ETL, data is extracted, transformed, and loaded to a data-sharing platform. Conversely, in ELT, data is first extracted and loaded before it is transformed, making both approaches uniquely suitable for diverse applications.

Both procedures are supported on the platform, which also combines a variety of data integration tools to streamline the process even further.

Besides ETL and ELT tools, Snowflake also offers other possibilities for data engineering and transformation.

They include:

Using incremental views

Incremental views involve creating a real-time transformation pipeline using several stacked views. By breaking down complicated pipelines into smaller phases and writing interim results to a transient table, organizations can effectively make their pipelines easy to test and debug. This approach can also improve the pipeline’s performance.

Using Spark and Java on Snowflake

For quite some time now, organizations have primarily relied on Databricks Clusters to run SparkSQL jobs. But thanks to the recently released Snowpark API, organizations can now leverage simpler tools like Virtual Studio, Scala, Jupyter Notebooks, and .

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The Snowpark API enables Spark DataFrames to be automatically translated and executed as a Snowflake SQL, resulting in a broader range of alternatives to transform data on various deployment environments, without having to deal with the extra expenses and complexities of supporting external clusters.

Using Streams & Tasks

Snowflake Streams offers a highly effective, straightforward means of simple change data capture (CDC) within the platform. When combined with Snowflake Tasks, Stream can facilitate data processing in near real-time.

Essentially, Snowflake Tasks provides a reliable schedule to regularly change newly received data, while Snowflake Stream maintains a stable pointer that records the already processed data. This significantly simplifies data processing operations while simultaneously ensuring that Snowflake automatically controls all computational resources. Ultimately, this can help organizations scale up or down as needed without maintaining a virtual warehouse.

Which sectors can benefit from Snowflake?

Virtually every organization in every sector has some level of data processing requirements. As such, most organizations that rely on leveraging vast amounts of data can benefit from the Snowflake Data Cloud Platform.

Some of the most notable sectors that could significantly benefit from utilizing the platform include:

The financial sector

Snowflake can help banks and other major players in the financial sector build connected data ecosystems, simplifying data access, collaboration, and deployment of AI solutions. Ultimately, this can help organizations in the financial sector to combine their key financial services, data providers, critical service providers, and prominent solution partners into a unified platform, thus facilitating seamless service delivery and enhancing collaboration.

The manufacturing sector

The global smart manufacturing market is expected to grow at a CAGR of 17.2%, reaching $241 billion by 2028, up from $108 billion in 2023. [3] By leveraging Snowflake, organizations in the manufacturing sector can integrate their data with AI-driven solutions to power smart manufacturing, improve supply chain performance, and generate value from connected products.

Additionally, by providing elastic multi-cluster computation and optimized storage capabilities, Snowflake can also enable manufacturers to accommodate vast amounts of data collected from their various operations to provide a comprehensive view of operations and optimize manufacturing practices.

Besides the financial and manufacturing sectors, other industries that could benefit from leveraging Snowflake in their data engineering practices include:

  • Advertising, media and entertainment
  • The public sector
  • Retail and consumer goods
  • The tech sector
  • Healthcare and Lifesciences sectors, and
  • The telecom sector

Final thoughts

Data engineering is the lifeblood of any organization that leverages data to optimize operations and gain insights. However, traditional data engineering tools are significantly limited by computational power and storage capacities, prompting organizations to seek more efficient cloud-based solutions.

In that regard, Snowflake has emerged as a top contender for data engineering applications due to its impressive scalability, flexibility, and integrations with multiple data transformation and processing tools.


[1] The Digitization of the World from Edge to Core. URL: Accessed on March 27, 2024
[2] ETL Vs ELT. URL: Accessed on March 27, 2024
[3] Smart Manufacturing Market. URL: March 27, 2024


Data Engineering