Learn how to integrate SAP data into Databricks with this comprehensive blog. Discover the essential components of the SAP ecosystem, including SAP HANA, S/4HANA, and BTP, and explore proven integration methods using SparkJDBC and Azure Data Factory. Perfect for data engineers and architects looking to combine SAP's enterprise management capabilities with Databricks' advanced analytics.

Josue Bogran interviews Jeremy Lewallen from Databricks’ Performance Team, exploring benchmarks, storage cost optimization, rightsizing SQL Serverless Compute, and common compute mistakes. Discover why Databricks continually enhances performance, tips for using the latest DBRs, and how their innovations provide a fast, efficient, and developer-friendly data platform.

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 vs. Snowflake vs. Fabric evaluates key aspects like toolsets, partner ecosystems, flexibility, ease of use, and overall business value. We break down each platform's strengths and weaknesses to guide you in choosing the best for your data strategy.

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.

This blog compares Microsoft Fabric and Databricks, focusing on pricing, features, governance, and scalability. It concludes that while Fabric suits smaller businesses, Databricks excels for medium to large enterprises due to its flexibility, innovation, and advanced data capabilities.

This article provides an initial analysis of Microsoft Fabric, comparing it with Databricks. It discusses why Fabric may not meet enterprise needs and explores potential integrations between Fabric and Databricks, highlighting how they can complement each other in data projects.

Transitioning to Databricks reduced costs by $19K/month and streamlined data operations. Learn how Databricks' unified platform simplifies data engineering and boosts efficiency in our latest blog.

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 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)