Snowflake Spark Connector: What It Means for Big Data

The Snowflake Spark Connector is modernizing big data by offering better collaboration and scalable insights for data-driven teams. Learn how the Snowflake Connector for Spark is changing the modern tech landscape.
January 23, 2023
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Snowflake Spark Connector: What It Means for Big Data - Snowflake Spark Connector - Hakkoda

Now, more than ever, it’s imperative that companies be able to analyze data quickly and effectively, with no spillover workload spent managing and fine-tuning metrics that should be instantly accessible. For many companies, streamlining and scaling data ingestion is made easier by tools like Apache Spark, an open-source, unified computing engine used for big data workloads, making it the tool of choice for developers and data scientists. 

In the past, the list of cloud data providers capable of supporting Spark workloads was slim, but Snowflake, one of the largest and most agile cloud data companies, has recently entered the market with a connector built specifically for Spark users.

Snowflake enters the Apache Spark ecosystem with the Snowflake Spark Connector. This connector allows Spark to read from and write data to Snowflake, making it easy to connect to complex Spark workloads. When you establish a connection with Snowflake Spark Connector, Spark treats Snowflake as if it were any other data source. And when you use the Snowflake Spark Connector to load data into Snowflake, the connector takes care of all the heavy lifting.

Evolving the Landscape of Big Data

Over one thousand organizations use Spark for fast, large batch data processing or for solving complex data problems in real-time with machine learning capabilities. From healthcare providers to retail, finance, and energy industries, Apache Spark solves data and predictive analytics problems, allowing technology to drive businesses and business decisions. 

For instance, healthcare organizations are using Spark to analyze patient records along with past clinical data. This identifies which patients are likely to face health issues after being discharged from the clinic, helping hospitals prevent re-admittance, and saving on costs for both the hospitals and patients. 

The financial industry often relies on Apache Spark to access and analyze social media profiles, call recordings, complaint logs, emails, forum discussions, etc., to get a panoramic view of their customer and gain insights that help them make the right business decisions. Snowflake takes this functionality a step further by modernizing data with deeper analytics, presenting it in ways we never imagined. Healthcare and financial organizations are looking to Snowflake as an answer to data silos, limited visibility and collaboration, decentralized governance, and security concerns. 

Simplifying Complex Tasks

The benefits of layering Apache Spark with Snowflake technology are myriad. For example, healthcare providers often leverage Apache Spark to analyze data and predict or prevent readmittance. By supplementing this functionality with the Snowflake Healthcare & Life Sciences Data Cloud, healthcare companies are able to access an integrated, cross-cloud data platform that eliminates data silos and enables organizations to centralize, integrate, and exchange critical and sensitive data at scale. 

Snowflake offers healthcare providers the ability to share and access data across user functions at unprecedented scale. And with enhanced security functions, businesses are finally able to marry the traditionally antithetical goals of enhanced privacy with increased user accessibility, meeting compliance requirements and industry regulations. 

By connecting Snowflake and Spark, companies can undertake complex tasks, like going beyond the data that predicts readmittance by analyzing data that can actually prevent readmittance in the first place. By enabling healthcare organizations with data around nonmedical factors called “social determinants of health” (that statistics say determine around 80% of health outcomes), companies can start to determine risk and improve care management. 

Snowflake is also looking at upcoming trends, such as analyzing unstructured data (think images and x-rays not able to be structured in a table). This will significantly impact the big data ecosystem as software such as Snowflake sheds the traditional technology restraints.

Hakkoda’s work to create a smooth and seamless integration between FHIR data and Snowflake is yet another example of the flexibility and control Snowflake offers for unique industry use cases, and particularly for healthcare providers ready to offer better care to their patients and increase cost savings. 

Why the Snowflake Spark Connector for Transforming Data 

While Apache Spark is a widely used platform, there are some drawbacks. Snowflake aims to bridge the gap with its Snowflake Spark Connector. By migrating Spark workloads to Snowflake, organizations can take advantage of the Snowflake platform’s near-zero maintenance, data-sharing capabilities, and built-in governance while improving security and data-driven insights. 

Snowflake is also cost-effective. Spark operates on an open-source platform designed for fast, interactive computation that runs in memory, enabling machine learning to run quickly. However, keeping data in memory is expensive since Spark requires a lot of RAM. Snowflake provides a modern data architecture entirely on the cloud, allowing for cost-efficient processing of big data. 

Another key benefit of the Snowflake Spark Connector is enhanced team collaboration. While Spark is not suited for a multi-user environment, Snowflake allows teams to collaborate on the same data on a common platform that natively supports everyone’s programming language. The Snowflake Spark Connector powers everyone from stakeholders and management to collaborate on their workflow and easily build a quality product. 

How The Snowflake Spark Connector Empowers Organizations

Managing large data becomes crucial for organizations looking to expand their businesses. However, this is often a difficult balance to achieve. By combining the key benefits of Spark (open source platform, lightening fast, flexibility, fault tolerance, and machine learning) with those of Snowflake (real-time data streams, no silos, team collaboration, and better analysis of customer behavior and product usage), you can get the value of both platforms and enhance performance.

With better analytics, users gain access to meaningful insights across the organization, leading to more data-driven decision-making. This is a crucial first step toward bettering partner relationships, optimizing pricing, lowering operational expenses, increasing sales effectiveness, and more.

How Hakkoda Can Help

Spark can be a powerful tool in the right situation, but working with Spark isn’t always easy. Snowflake is one of the most user-friendly and secure data solutions accessible, and connecting Spark to Snowflake enables a wider audience of users and supports a larger community. 

At Hakkoda, our Snow Pro certified and Apache Spark expert data teams help your organization migrate to a modern data stack, using the latest technology to make your migration seamless and stress-free. Our cost effective, scalable teams  guide you throughout the entire process and recommend the best possible route for your data innovation journey. 

Talk to one of our experts today!

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