Many companies are now strategically using their data assets as the core of their digital transformation efforts. Building a data pipeline can help companies combine information from all their systems to gain insights into their business. A recent study showed that 59.5% of corporations using advanced analytics saw measurable results. Using a warehouse such as Snowflake can help companies leverage this information to drive business strategy. Here are five secrets to integrating Snowflake that will help you get the most out of this platform.
Table of Contents
- What is Snowflake?
- 1 - Integrating Snowflake Enables Flexible Scaling
- 2 - How to Integrate Snowflake for Near Real-Time Data
- 3 - Integrating Snowflake for Loading Large Datasets
- 4 - Integrating Snowflake with Semi-Structured Data
- 5 - ETL Tools Streamline Integrating with Snowflake
What is Snowflake?
Snowflake is a cloud-based data warehouse offered on a software-as-a-service (SaaS) model. The platform is built on top of Amazon Web Services or Microsoft Azure. As a cloud service, there is no hardware or software to install or manage. The service employs a subscription-based pricing structure with compute and storage resources billed independently of each other. Companies only pay for the services they use.
One of the most remarkable features of the tool is that companies can quickly create an unlimited number of virtual warehouses. This allows for the separation of workloads across independent instances against the same data without risk of contention.
1) Integrating Snowflake Enables Flexible Scaling
Subscription-based pricing structures for SaaS are cost-effective and convenient. However, one drawback is that the pricing bundles storage, computing, and resources as one cost. Snowflake, however, offers a much more flexible and cost-effective option. The platform comprises three layers:
- Cloud Services
Each can be scaled independently and billed independently which means companies only pay for the resources used at each layer of the environment.
Information in the platform is stored in multiple micro partitions. The storage layer works on a shared-disk model which simplifies information management. Because of this, companies won’t need to deal with managing data distributed across multiple nodes in a shared-nothing model.
Compute nodes retrieve information from the storage layer for query processing. Snowflake uses “virtual warehouses” to run queries. The benefit is that each warehouse runs in its own compute cluster and does not affect other virtual warehouses. They can also be suspended, restarted, or scaled out without affecting other warehouses in the environment.
This layer handles services such as authentication, security, metadata management, and query optimization.
Integrate.io provides pre-built integration to help companies build pipelines to load data from their source systems into Snowflake. A few of the integrations supported include Google Analytics, IBM DB2, Marketo, and Looker.
2) How to Integrate Snowflake for Near Real-Time Data
Access to real-time data is essential in enabling informed decision-making. Rather than needing to rely on separate streaming mechanisms, the tool can handle streaming through a native connector for Kafka. The connector, named Snowpipe, removes the staging step of the process. The warehouse can consume information directly from Kafka which leads to lower processing times. Each topic represents a stream of rows to insert into a table in the warehouse. Typically, each message contains one row.
Integrate.io enables real-time streaming through streaming ETL. The tool features a vast catalog of integrations that companies can use to bridge information from their systems in near real-time.
3) Integrating Snowflake for Loading Large Datasets
In traditional warehousing platforms, loading large data sets can be time-consuming. Transferring such a large data set puts a strain on infrastructure resources. This robust platform, however, handles this with much better performance than other warehousing platforms.
Improving Load Performance Using Staging
Rather than performing a bulk load directly from the source system, Snowflake stores the information in a “staging” environment. The staging environment could be internal to the company or external via cloud storage. Staging significantly improves performance for bulk loads.
The team at Snowflake conducted a bulk load test to analyze performance for large loads. The team used a table that contained 28.8 billion rows that represented 4.7 TB of data. The tool compressed the size of the data down to 1.3 TB. The most impressive performance was loading a CSV file in 1104 seconds (15.4 TB per hour).
Loading large volumes of information with Integrate.io is quick and efficient. The platform facilitates integrating Snowflake with a variety of systems and can support the transfer of large volumes of information.
4) Integrating Snowflake with Semi-Structured Data
In today’s competitive landscape, business leaders require more than the quantitative information one might get from structured sources such as spreadsheets or relational databases. Companies that want to remain industry leaders must gather insight from additional sources such as emails, websites, and images.
The challenge is that this semi-structured data can be difficult to parse. A unique feature of the platform is that it natively supports this type of information. Formats supported are:
All of these formats can be stored in the warehouse in their native format. Some of the benefits are:
- The data is not constrained to a fixed schema.
- The information is easily portable.
- It supports queries that cannot be expressed with SQL.
Integrate.io integrations support loading each of these semi-structured data types. Companies can easily integrate systems regardless of the type of data.
5) ETL Tools Streamline Integrating with Snowflake
Using a warehouse is ideal for integrating information from disparate sources. However, building a pipeline to integrate that information used to be a time-consuming and tedious task that involved extensive programming.
The rise of no-code/low-code tools has made building pipelines much simpler and much quicker. Extract, Transform, and Load (ETL) tools provide pre-built code blocks known as integrations for a variety of systems. Users simply connect these code blocks to build the pipelines they need to load information to the warehouse.
How Integrate.io Can Help
Are you looking for a robust ETL solution? Give Integrate.io a try. Our no-code/low-code tool features an intuitive user interface that empowers non-technical individuals to build data pipelines with ease. The platform features hundreds of integrations that eliminate the need for custom coding. Reach out to us to try the platform for yourself and see how integrating Snowflake into your pipeline can help you get the most out of your data assets.