When decisions are data-backed, data is the real decision-maker. This data, which could be critical for enterprises, must be collected, segregated, and analyzed harmoniously to pay off. It changes several hands – from different data sources to pipelines, containers, and machine learning models that process this data to glean relevant insights. Two profiles – data engineers and analysts – play pivotal roles in turning raw data into actionable insights; often, these collide to collaborate. While data engineers work with data input streams to transform raw data into inputs for analysis, data analysts make sense of this data. This is the classic operations-production conundrum, where one needs the other to function, and very soon, one can become the roadblock for the other.
With so much noise around NoOps – the scenario where every under-the-hood need is abstracted and automated to help employees focus on the core job – it is well worth understanding how this operating model helps streamline workflows and foster collaboration. Data engineers create pipelines to source data from different systems, which could be in different formats, to be transformed and stored in a data warehouse. For data analysts, this is an under-the-hood, operational activity that only serves as the starting point for their jobs. The friction happens when data engineers take time to set up, clean the data and make it ready for consumption. This delays the analysis, which could lead to missed opportunities costing millions in revenue in today’s business environment.
Google Cloud offers a breakthrough with its Serverless Spark offering – Google Dataproc Serverless. As the industry’s first auto-scaling offering, Google Dataproc Serverless allows enterprises to abstract the infrastructure setup (machine configuration, RAM, CPU) and allows data engineers to deliver data input to data analysts seamlessly. This helps teams to collaborate better, eradicates infrastructure-related roadblocks, and reduces the time to deliver value to business.
In my previous blogs, I talked about how Google Serverless Spark boosts productivity much better than its peers. This blog illustrates how teams can deploy Google Serverless Spark to analyze data contextualized to industry use cases to glean meaningful insights faster and more seamlessly.
- Banking: Fraud detection and risk analysis are top use cases where data-driven insights play a transformational role. Banking and financial firms must analyze billions of customer behavioral signals through emails, phone calls, and contact center interactions to identify anomalies that could point to fraudulent transactions. This needs to be streamlined to process real-time data, requiring data engineers to gather continuously and parse data from live transactions, often as they occur. Google Serverless Spark helps these teams to design risk and fraud detection models without worrying about the underlying infrastructure that auto-scales as per demand or load and integrates seamlessly with Google Cloud tools such as Vertex AI, BigQuery, Composer, and Dataplex to enable real-time analysis.
- Healthcare & Life Sciences: With the proliferation of connected devices, such as smartwatches and digital therapeutics, healthcare is fast becoming evidence-based and data-driven. To enable this, healthcare professionals need instant access to real-time patient data and real-time analysis to make clinical decisions based on medical imaging and genome data. Serverless Spark with Vertex AI can be used for use cases like medical imaging analysis, genomics data analysis, Natural Language Processing (NLP), and distributed training of machine learning models. Google also offers Dataproc on GCE (Google Compute Engine), which allows teams to exercise greater control over the underlying infrastructure if the data needs to be safeguarded or complies with data security norms, such as PII (Personally Identifiable Information) or PHI (Personal Health Information). This adds an additional secure layer for protected data, such as patient records.
- Supply Chain: Demand forecasting and inventory management are critical indicators for supply chain operations. Signals for these indicators come through varied systems and formats and are contingent on several non-related parameters such as weather, geo-political conditions, and even trade credit. Teams need access to readily available data that can be used to assess current demand and tally it with the existing stock to make decisions related to warehousing, purchasing raw materials, and production. Google Serverless Spark, as a truly NoOps offering, eliminates the need to set up additional computing infrastructure and allows teams to run analysis without incurring additional overhead costs since users are charged only for the duration of the analysis job and not for the duration the infrastructure was up.
- Retail: Recommender systems are revenue generators for retailers, allowing them to map customer preferences with products. This requires analyzing customer behavior, past purchases, and even time spent viewing specific products. The insights help teams to pitch the most-suitable product at the most suitable time to a customer profile, ensuring a high possibility of locking in a sale. Teams need machine learning models constantly fed with real-time or batch-processing data, which must be sourced from various systems. With its compatibility with natural language processing, Google Serverless Spark helps data engineers clean and transform the data into machine-acceptable formats. It enables data engineers to train machine learning models backing these recommender systems. The more iterations of this data are fed into the model, the more it learns and churns out recommendations highly tuned to customer preferences. With its efficiency and speed, Google Serverless Spark allows teams to train the model iteratively, ensuring the model produces accurate output that delivers business value.
When it comes to extracting value from data, speed, and accuracy are critical. Since data engineers and analysts collaborate to bring this about, Google’s Serverless Spark streamlines the process from start to finish, making it easier for the two teams to enable each other.
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