April 04, 2024
Leveraging Snowflake for Data Engineering
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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Key Takeaways
- Snowflake is a cloud-native data platform that simplifies data storage, processing, sharing, and analytics at scale.
- Its architecture helps organizations build scalable, high-performance data pipelines while reducing infrastructure complexity.
- Snowflake supports both ETL and ELT approaches, as well as advanced data transformation capabilities through Snowpark, Streams, and Tasks.
- Industries such as finance, manufacturing, retail, healthcare, and telecommunications can use Snowflake to improve data accessibility and AI readiness.
- Combining Snowflake with modern data engineering practices enables organizations to process growing data volumes more efficiently and support data-driven decision-making.
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.
To fully leverage these capabilities, many organizations work with specialized Snowflake consulting services that help design scalable architectures, optimize data pipelines, and integrate Snowflake with existing cloud and analytics ecosystems. They may also use one of the top DataOps platforms to automate pipeline deployment, testing, monitoring, and collaboration across data teams.
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 .
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.
Organizations evaluating Snowflake alongside Databricks for AI, analytics, and data engineering workloads can compare both platforms in our guide to Databricks vs Snowflake, covering their strengths, trade-offs, and ideal enterprise use cases.
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. However, the right platform still depends on the organization’s cloud environment, workloads, operating model, and existing technology stack, which is why it is worth understanding the difference between Redshift and Snowflake before making a final architectural decision.
References
[1] Seagate.com. The Digitization of the World from Edge to Core. URL: https://www.seagate.com/files/www-content/our-story/trends/files/idc-seagate-dataage-whitepaper.pdf. Accessed on March 27, 2024
[2] Snowflake.com. ETL Vs ELT. URL: https://www.snowflake.com/guides/etl-vs-elt/. Accessed on March 27, 2024
[3].Marketsandmarkets.com. Smart Manufacturing Market. URL: https://www.marketsandmarkets.com/Market-Reports/smart-manufacturing-market-105448439.html March 27, 2024
FAQ
What is the difference between Snowflake Dynamic Tables and Streams with Tasks?
Dynamic Tables use a declarative approach: a data engineer defines the desired result with a SELECT query and specifies the acceptable target lag, while Snowflake manages dependencies and refreshes the data automatically. Streams and Tasks provide greater procedural control — Streams record row-level changes, while Tasks determine when and how those changes are processed.
Dynamic Tables are therefore better suited to pipelines in which the main requirement is maintaining a continuously updated result without manually building orchestration logic. Streams and Tasks are more appropriate when the workflow requires stored procedures, conditional processing, calls to external services, complex branching, or precise control over individual processing steps.
How does Snowflake control access to sensitive data?
Snowflake can protect data at multiple levels. Row access policies determine which records a user is allowed to see, while masking policies dynamically replace or obscure sensitive column values depending on the user’s role and permissions.
These policies can be applied centrally rather than separately in every analytical query. Snowflake also supports tag-based masking, which allows organizations to classify sensitive columns and associate security policies with particular tags. This is especially useful in environments containing personal, financial, healthcare, or other regulated information.
How can companies prevent unexpected Snowflake costs?
Snowflake costs depend mainly on compute consumption, so cost control should start with virtual warehouse configuration. Auto-suspend stops a warehouse after a defined period of inactivity, while auto-resume starts it again when a new workload appears. This reduces the risk of paying for idle compute resources.
Organizations can also create resource monitors that track credit consumption and trigger notifications or suspend warehouses when predefined thresholds are reached. Snowflake budgets provide broader oversight, including costs generated by selected serverless features. However, resource monitors should not be treated as complete protection against every type of cloud-services charge, because their enforcement primarily applies to warehouse usage.
How does Snowflake handle many users and simultaneous analytical queries?
Snowflake can separate different workloads into independent virtual warehouses, allowing data loading, transformation, business intelligence, and data science processes to use different compute resources while accessing the same stored data.
For workloads with changing query concurrency, multi-cluster warehouses can automatically add or remove clusters within configured limits. This reduces competition for resources between users, although it primarily improves concurrency rather than the execution speed of one particularly complex query. Workload separation is therefore often as important as increasing warehouse size.
Does Amazon Redshift also separate storage and compute?
Yes. Modern Redshift architectures using RA3 nodes store warehouse data in Redshift Managed Storage, which is backed by Amazon S3. This allows storage to grow independently from the local compute capacity of the cluster.
Redshift Serverless goes further by automatically provisioning and scaling data warehouse capacity without requiring the organization to manage a permanently provisioned cluster. Therefore, the statement that only Snowflake separates storage and compute would be an oversimplification. The practical difference lies more in how each platform provisions resources, manages workloads, integrates with its cloud ecosystem, and calculates costs.
How do Snowflake and Redshift manage sudden increases in query concurrency?
Snowflake can use multi-cluster warehouses to add compute clusters when concurrent queries begin to queue. Each workload can also be assigned to a separate warehouse, which helps isolate resource-intensive processes from business intelligence or operational reporting.
Amazon Redshift offers Concurrency Scaling, which automatically adds temporary cluster capacity when the main resources cannot handle increased read or write query demand. Redshift also uses automatic workload management to classify and allocate resources to queries. In Redshift Serverless, automatic workload management is enabled by default.
When should a company use a materialized view instead of a regular data pipeline?
A materialized view stores the result of a query so that repeated analytical requests do not need to recalculate the same joins or aggregations every time. It is useful when users repeatedly query a stable or predictably changing dataset and low query latency is more important than immediate access to every source-level change.
It should not automatically replace a complete data pipeline. Materialized views are less suitable when the process requires multiple transformation stages, complex data quality checks, external actions, procedural logic, or detailed orchestration. In Redshift, materialized views can be refreshed manually and some configurations support incremental updates. In Snowflake, Dynamic Tables may be a more appropriate option when several dependent transformations need to be maintained according to a defined freshness target.
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