Evaluating Databricks' cost control features reveals strengths in granular tracking and user-friendly budgeting, but highlights areas for improvement in automated alerts and comprehensive expense tracking. Explore our insights on Databricks' budgeting tools.

Here we present Databricks Summit 2024 18 biggest announcements, including Generative AI tools, AI-powered BI dashboards, open-source Unity Catalog, no-code data ingestion, serverless compute, secure data collaboration, built-in AI functions for data warehousing, and the all-new Delta Lake 4.0. Discover how these advancements can unlock the full potential of your data. Get the details and see how SunnyData can help - read now!

Discover how to manage pipeline costs effectively with Databricks Workflows. This article offers practical tips to reduce total cost of ownership without sacrificing performance, and provides insights into understanding your costs better. Learn strategies like using job compute and spot instances, setting warnings and timeouts, leveraging task dependencies, and implementing autoscaling and tagging. Optimize your resource usage and get the most out of your Databricks environment. Read on for actionable advice to streamline your data processes.

Disappointed by the high failure rate of AI initiatives? You're not alone. This post unveils the secrets to planning and executing a successful AI project. We detail our proven methodology for ensuring AI success, focusing on identifying high-ROI use cases and navigating the Proof-of-Concept stage effectively. Learn how to choose the right supplier, avoid common pitfalls, and ensure your AI project transitions smoothly from pilot to production, delivering real business results and a competitive edge.

This comprehensive guide delves into the essential features of Unity Catalog by Databricks, highlighting its role in enhancing security, automating data documentation, and streamlining ML and AI governance. Learn practical tips on integrating this powerful tool into your data strategy to boost productivity and ensure compliance. Whether you're scaling up or enhancing data discoverability, this article is your roadmap to leveraging data for innovation while maintaining robust security.

Inn this blog we address whether Unity Catalog can replace existing enterprise catalogs when integrated with Databricks. We clarify that while Unity Catalog excels at centralizing governance and enhancing data management within Databricks, it complements rather than replaces established catalogs like Alation or Collibra if they already add significant value.

With extensive experience in data solutions, our CEO Kai notes that Unity Catalog is indispensable for managing permissions and access across data, ML, and AI assets effectively. However, for broader governance needs, using it alongside other data catalogs ensures comprehensive management across all data systems.

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.

Building on our exploration of Generative AI applications, this article dives deeper. We'll uncover the technical details: what makes it work and what challenges it faces. This equips you to understand how projects are built, their hurdles, and the best practices to overcome them.

The democratization of AI birthed fantastic products, but also downsides. Information overload, a talent gap, and poorly executed projects highlight the need for "know-how." This article empowers you to navigate this new landscape.

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.