Unity Catalog from Databricks is revolutionizing how businesses manage their data, providing a unified governance platform that centralizes control over data and AI assets. It enhances productivity, bolsters security, and streamlines compliance by offering a single, searchable repository for all data assets.

The platform automates data documentation with Generative AI, easing the workload on data stewards and enriching data management with semantic searches and interactive visualizations. Additionally, Unity Catalog's Lakehouse Federation integrates data across multiple platforms, ensuring seamless data accessibility. Its advanced data lineage capabilities offer clear visibility into data movements, crucial for compliance and informed decision-making, making it a strategic asset for any data-driven organization.

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.