For years, we've dreamed of having natural conversations with machines. Chatbots were our first attempt, but they relied on scripts and struggled with anything unexpected. Generative AI changes the game. It learns from massive datasets, creating responses on the fly and handling complex ideas. This summary focuses on text applications, but generative AI is making strides in image, video, and audio too. The future of AI is dynamic and full of possibilities.

Funny or not, building a secured, governed and scalable data platform that supports multiple types of use cases along with the data management processes and practices is very similar to building a skyscraper - the higher the building grows and supports more units and people, the complexity increases.

This guide will help you understand the complexities of Databricks, ensuring your data skyscraper stands tall and proud.

In the evolving world of AI and ML, businesses demand efficient, secure ways to deploy and manage AI models. Databricks Model Serving offers a unified solution, enhancing security and streamlining integration. This platform ensures low-latency, scalable model deployment via a REST API, perfectly suited for web and client applications. It smartly scales to demand, using serverless computing to cut costs and improve response times, providing an effective, economical framework for enterprises navigating the complexities of AI model management.

Photon, a C++ native vectorized engine, boosts query performance by optimizing SQL and Spark queries. It aims to speed up SQL workloads and cut costs. This blog will help you understand Photon's role in enhancing Databricks, ensuring you grasp its significance by the end.

In the intricate landscape of migration planning, it is imperative to map processes and prioritize them according to their criticality. This implies a strategic process to determine the sequence in which processes should be migrated according to business.

In addition, organizations will have to define whether to follow a "lift and shift" approach or a "refactor" approach. The good news is that here we will help you choose which option is best for the scenario.

Migrating from a large Hadoop environment to Databricks is a complex and large project. In this blog we will dive into different areas of the migration process and the challenges that the customer should plan in these areas: Administration, Data Migration, Data Processing, Security and Governance, and Data Consumption (tools and processes)

Over the past 10 years of big data analytics and data lakes, Hadoop has proven unscalable, overly complex (both onPremise and cloud versions) and unable to deliver to the business for ease of consumption or meet their innovation aspirations.

Migrating from Hadoop to Databricks will help you scale effectively, simplify your data platform and accelerate innovation with support for analytics, machine learning and AI.

SunnyData raises $2.5M to become a pure-play Databricks system integrator focusing on migrations from Hadoop and EDWS and Data Engineering as a service.