This guide dives into the technical steps required to migrate from Amazon Redshift to Databricks. Covering everything from discovery and data evaluation to security protocols and cost estimation, it offers detailed, practical strategies for managing dependencies, optimizing queries, and planning for future scalability within Databricks’ robust ecosystem.

This blog introduces the strategic benefits and challenges of migrating from Amazon Redshift to Databricks. It covers Redshift’s legacy limitations, Databricks' advantages, and critical migration factors. The article provides an overview of key planning steps, including architecture considerations and phased migration strategies, setting the stage for technical execution in the upcoming part two.

This blog explores the migration process of Databricks from one cloud provider (GCP) to another (Azure or AWS). It emphasizes using tools like Terraform for seamless migration, best practices for handling resources, data, and configurations, and discusses strategic reasons for switching cloud platforms.

Databricks is a top choice for startups seeking an all-in-one data platform for analytics, AI, and data management. Its cloud-native, scalable, and cost-efficient architecture allows startups to begin small, grow, and avoid complex migrations as needs evolve.

In this blog, we outline the essential steps for migrating ETL workloads and EDW from Snowflake to Databricks. From data migration to report modernization, we break down five key phases for a seamless and efficient transition to Databricks.

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.

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.