The only way to respond to today’s fast-changing business and IT landscapes is to predict what’s coming. Enterprises do this by tapping into data troves they continuously gather, parse, maintain, and secure. Insights from this data help them anticipate customer needs, innovate products/services and keep ahead of the curve. Getting this off the ground requires infrastructure such as data warehouses, analytics engines such as Spark or Hadoop, and data teams to deliver timely insights.

The catch? This is expensive, complex to maintain and ultimately dilutes the focus from the core job, i.e., extracting value from data. Spark, a powerful data processing framework, comes with lots of parameters and knobs to tune. For every 10 minutes at the job, a Spark developer codes for four, and spends six minutes on managing infrastructure. That’s a colossal waste of potential, and a huge opportunity gap in a world where the first-mover advantage can make or break. Enterprises that prioritize data-led innovation must unshackle developers from tasks that keep them from focusing on their core jobs – writing code that delivers value.

Enter, Google Cloud Platform (GCP) Dataproc Serverless – a serverless offering that abstracts away management of below-the-line Spark clusters so that enterprises can focus on gleaning business-critical insights. GCP’s serverless Spark allows developers to execute Spark jobs without worrying about provisioning, scaling, or maintaining clusters, while using the interface of their choice, be it BigQuery, Vertex AI, Composer, or Dataplex, without custom integrations.

GCP’s serverless Spark decouples computing from storage, enabling businesses to fire up innovation with –

  • Optimized costs: Traditionally, enterprises pay for the entire time the Spark infrastructure is up, even if it is idle. With Dataproc Serverless – Google’s serverless Spark offering – enterprises pay for the time the job is being executed, dramatically reducing operational costs and resource wastage. These savings can be rerouted to fund developer needs, or toward projects with higher business dividends.
  • Reduced complexity: Big data infrastructure is hard to manage. With GCP Dataproc Serverless, enterprises can abstract infrastructure management and its complexity to focus on the core job at hand. Moreover, since Google integrates several of its cloud solutions with Dataproc Serverless   the learning curve reduces further, and developers can hit the ground running with what they know.
  • Assured security: GCP Dataproc Serverless is compliant with Google’s ‘secure by default’ framework that inherently brings all workloads under a robust security and governance framework, assuring developers that their Spark jobs are secure by design with no SSH, no root access, and with Spark RPC encryption.
  • On-demand scale: GCP Dataproc Serverless comes with auto-tuned capabilities and easy provisioning that frees-up developers from having to manually reconfigure underlying infrastructure every time the compute workload fluctuates.
  • Granular insights: GCP Dataproc Serverless integrates with other GCP data services such as Vertex AI, BigQuery, Composer and Dataplex. It is easier to analyze entire sets of data without worrying about aggregating or querying across different services. Data teams can extract more value from entire datasets than partial ones, delivering more granular insights to businesses that can fire up innovation across product/service portfolios. than partial ones, delivering more granular insights to businesses that can fire up innovation across product/service portfolios.
Test Flight

As with everything cloud, there is no one-size-fits-all. Enterprises must evaluate their data requirements and business goals before going serverless. Google offers three distributed options to run Spark workloads – Dataproc on Google Kubernetes Engine for enterprises that want to improve infrastructure utilization , Spark on Google Compute Engine for more control and options, and serverless Spark for enterprises looking for no-ops. The decision would also depend on how critical the data is, what skills the team has, and what type of business applications or needs it serves.

A seasoned Google implementation partner, Persistent can help you take the right call. Just like we have helped leading enterprises leverage Google offerings to drive innovation.

Watch this space for more!