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Streamlining Salesforce Data Integration: A Quick Guide with Dataworkz

Are you grappling with the challenge of consolidating customer data from various sources and seamlessly incorporating it into Salesforce? If so, you’re not alone. The process can be complex, especially for those without a technical background. Fortunately, Dataworkz is here to simplify the entire procedure for business users.   In just two minutes, we’ll guide …

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How to Evaluate Response of a RAG Empowered LLM

Years of relentless advancements in artificial intelligence have led us to a pivotal breakthrough: significantly enhancing the accuracy of Large Language Models (LLMs) and effectively reducing their tendency to generate ‘hallucinated‘ responses.  At the heart of this evolution is Retrieval Augmented Generation (RAG) – a technology widely recognized by experts as a significant step forward …

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How Can Advanced Logical Plan Optimization Mitigate Data Skewness in Spark?

When working with massive datasets in Spark, processing billions of rows distributed across multiple cluster nodes, handling data skewness becomes a complex task. Skewed data can lead to scalability, performance, and resource optimization challenges, making it crucial to find effective solutions. In our previous blog, we explored the concept of data skewness in Spark and …

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What is Data Skewness in Spark and How Does It Impact EL+T System Performance?

Data skewness is a common challenge faced by organizations processing extensive data using Spark’s EL+T (Extract, Load, and Transform) systems.  In this blog, we will explore the concept of data skewness in Spark and understand the significant impact it can have on the performance of EL+T systems. Also read – Is ELT The Ultimate Replacement …

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How can RAG Boost Customer Support in Modern Enterprises?

HubSpot research shows that 93% of customers remain loyal to companies providing excellent customer support, prompting businesses to seek improved service strategies.  AI-powered tools, including chatbots and virtual assistants using Language Model (LLM) technology, are increasingly gaining popularity for creating personalized customer experiences. The success of ChatGPT as a Q&A interface has particularly impressed businesses, …

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How will Retrieval Augmentation Generation (RAG) Shape the Future of Business Intelligence?

Ever wished for a ChatGPT like question and answer interface that could give reliable answers to your business intelligence queries? Probably yes! Conversational AI, powered by Large Language Models(LLMs), after all, shows great potential in making it possible. However, the problem is that these AI-powered chatbots hallucinate more often than we think.  According to findings …

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Cost and Efficiency Benefits of RAG Deployments for Enterprises

Effective analysis and utilization of copious amounts of enterprise data remains a major challenge for most businesses despite significant investments. Fortunately, the emergence of Large Language Models (LLMs) such as ChatGPT has opened up new horizons for businesses looking to enhance their data utilization capabilities. However, as brilliant as LLMs are, they have certain limitations …

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Overcome the Biggest Obstacle to True Customer 360: with MongoDB Atlas and Dataworkz

This blog post discusses the importance of understanding customer data and achieving a comprehensive Customer 360 view. It highlights the significance of customer acquisition costs (CAC) and customer lifetime value (CLV) in growing and retaining customers. The ability to collect, analyze, and leverage customer data across various systems is crucial for optimizing investments in sales, …

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Accelerating RDBMS to JSON migration with no-code transforms

Enterprises around the world are looking to modernize their existing applications. They need to move their business applications to a more cost-effective, scalable, and flexible platform to leverage the true value of their data and achieve their business objectives. Whether their goals are business growth or fending off the competition, application modernization is the primary …

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