Hadoop to Databricks Migration: Why and How to Make the Move

Is your future data strategy getting stuck with Hadoop? Companies are opting for smarter, speedier platforms in response to evolving technologies. Hadoop has served for years by storing large amounts of data. However, due to its high operational costs, performance issues, and complex data processing, moving to a cloud platform like Databricks, Snowflake, or BigQuery can be an alternative. Databricks is a cloud-native platform known for its speed and performance in data storage and processing.

This blog outlines the detailed steps for migrating Hadoop to Databricks in today’s data-driven environment.

Why Migrate from Hadoop to Databricks

Hadoop is an open-source Java framework for the distributed storage and processing of large amounts of data, often referred to as Big Data. It breaks large data into smaller sets, and these blocks are stored across many servers. It uses the Hadoop Distributed File System (HDFS) and stores the data on-premises. Hadoop platforms have not achieved business value and suffer from high maintenance costs, poor performance, and a lack of advanced data science capabilities. So enterprises are looking to transition to more reliable cloud-based platforms. Databricks is a cloud-based platform that stores and processes large amounts of data, supported by data science and machine learning. It uses cloud object storage for storing data. 

It overcomes the challenges of Hadoop efficiently with features such as 

• Unified platform and enhanced collaboration: Databricks provides a common platform for data scientists, engineers, and business analysts to work together and collaborate on data projects. This improves communication and collaboration within teams, which makes it easier to develop and deploy data-driven solutions.
• Modernized Data Architecture: It is based on Databricks Lakehouse architecture, which uses both data warehouses and data lakes for all data types and workloads. A data lake is for storing large amounts of raw data, and a Data warehouse is for structuring and analyzing the data. The Databricks Lakehouse architecture minimizes data redundancy and improves SQL querying capabilities with the Delta Lake SQL endpoint. This architecture integrates data management and accelerates analytics and AI workloads on a cloud-native platform. ‍
• Faster performance and scalability: Databricks is built on Apache Spark and processes the data faster than Hadoop’s disk MapReduce.Auto-scaling and efficient processing features ensure good scalability.‍
• Less infrastructure costs: Databricks is a fully cloud-managed service and eliminates the need for manual infrastructure, which reduces operational costs.‍
• Built-in machine learning and AI support: Databricks has built-in tools for ML, AI, and advanced analytics using Databricks Data Intelligence Platform (DDIP). Databricks are the future of data engineering because it simplifies the way to handle and analyze data. With its cloud-based platform, it can be integrated with AWS, Azure, or Google Cloud, enabling companies to work with a large amount of data very effectively and efficiently. 

Databricks provides us with built-in tools for monitoring jobs, auto-scaling clusters, and reducing cost, so transitioning from Hadoop to Databricks will improve the overall performance of the organization.

Things to Consider Before Migrating from Hadoop to Databricks

Any migration needs a complete analysis of whether it will be successful or not. Some of the key factors to be considered when doing a Hadoop to Databricks migration are

• Architectural changes: Hadoop provides distributed storage through Hadoop Distributed File System(HDFS) and MapReduce. In Hadoop, the storage and processing of data are tightly coupled, whereas in Databricks, its architecture is decoupled and allows us to store and process data independently. With Hadoop, we require DevOps to perform cluster provisioning and tuning manually, and Databricks provides us with fully managed clusters with auto-scale functionality. 
• SQL differences: Hadoop and Databricks have notable differences in SQL syntax when it comes to managing complex data types, and they need to be modified when needed. Hadoop uses Hive(HiveQL) and Impala. They both come packed with functions for handling big data and are designed for batch processing alone. Databricks uses an ANSI SQL-compliant language(Spark SQL) for handling data types for big data that are designed for batch processing and real-time data processing. Spark SQL offers additional capabilities like inline functions for handling more sophisticated data. Stored procedures, functions, and queries need to be converted, adapting the Teradata SQL code.
• Migration costs: We should thoroughly analyze and plan the budget needed for the migration. Utilize the pay-as-you-go pricing offered by Databricks to reduce costs. 
• Ensure compatibility with existing tools: Identify and replace outdated tools with Databricks-compatible or their alternatives.
• Data Security and Compliance: Hadoop needs third-party tools like Ranger, Sentry, or Kerberos for data security and governance, and Databricks provides us with built-in features such as Role-Based Access Control(RBAC) and IAM integrations for data lineage and tracking. 
• Handling large volumes of data: Hadoop often manages terabytes or petabytes of data in various formats, so careful planning is needed to avoid data loss. Start migrating in phases with smaller workloads to avoid risk. Consider using tools like Delta Lake or Databricks Connect to transfer data.
• Downtime Minimization: Critical operations should not be kept on hold due to the conversion of Hadoop to Databricks. Ensure that the downtime is minimal for data migration.
• Skillset and Training: Ensure that the team is knowledgeable in Databricks and has acquired the skills. Adequate training must be provided to the team to ensure that they are capable of delivering high-quality support.

How to Migrate from Hadoop to Databricks (Step-by-Step)

A successful migration requires thorough planning, goal setting, and a migration strategy. Addressing the steps below will help you avoid errors and achieve performance when moving to a new base.

1. Planning and Assessment: Analyze the Hadoop environment and identify all the data types, data sets, access patterns, data pipelines, and expected runtime in your existing ecosystem. Hadoop setup may include HDFS, Spark SQL, or MapReduce, and may have dependencies. Determine the complexity of the Hadoop environment, including dependencies and data integration points. Draft a clear picture of what needs to be migrated, whether we are migrating to cut down the costs and enable Artificial Intelligence and Machine Learning, or some other business objectives. 
2. Choose the cloud provider: Databricks can run on AWS, Azure, or Google Cloud; analyze and select the cloud provider based on costs, security services, and available tools in the organization.
3. Standardizing Data Security and Compliance: With the built-in features of RBAC, ensure encryption, audit logs, and data tracking are aligned with data governance and standardized GDPR and HIPAA. Ensure Access controls and role-based permissions are given. 
4. Select the migration approach: Decide which migration approach to follow, whether it is Lift-and-Shift, Replatforming, or Refactoring. Use the Lift-and-Shift approach if you are migrating the data as is and need quick migrations. The Replatforming approach involves adapting workloads for Databricks and Delta Lake. Use the Refactoring migration method if you need maximum performance gains, real-time analytics, and AI/ML capabilities.
5. Data migration: Moving your data from Hadoop to Databricks involves several steps. HDFS data will be extracted using Spark or other ETL tools and moved into cloud storage. Clean, reshape, and transform the data as needed using Spark. Load the data into Delta Lake on Databricks for ACID transactions and data management.
6. Workload migration: Hadoop queries written using MapReduce might not get translated as such into Databricks.Rewrite the logic in Spark SQL and execute it. Use Databricks Notebooks for scheduling and visualizing.
7. Testing: Run workloads in both Hadoop and Databricks in parallel during the transition itself to check the functionality and performance. Use Databricks autoscaling and job scheduling to optimize the cluster configurations and improve the speed by using caching mechanisms like Delta caching and data skipping.
8. Validation: Compare and validate the data from Hadoop and ensure that all the data has been migrated successfully.
9. Stakeholder approval: Get stakeholders' approval by showing the results obtained from the Hadoop to Databricks migration.

What to Do After You Migrate from Hadoop to Databricks

Some post-migration steps to be considered are 

• Ensure compatibility: Verify that all the existing tools and workflows are compatible with Databricks. 
• Monitor and optimize: Continuously monitor the performance and optimize workloads on Databricks.‍
• Training and documentation: This is one of the most important post-migration steps to be considered for future growth. Ensure that the team is educated on the new platform and provide ongoing support.‍
• Decommissioning Hadoop: After successful migration, decommission Hadoop clusters incrementally.

Why choose Entrans for your Hadoop to Databricks Migration

Migration is not about transferring data; it is about reliability and performance. Our team at Entrans specializes in migrating from Hadoop to Databricks services. Our team of experts is dedicated to ensuring security and transparency with a proven track record.

Entrans comes with a wide range of engineers specialized in migration skill sets. 

If you are planning a Hadoop to Databricks migration, our team is here to support and ensure a smooth and efficient transition. Want to know more about it? Book a free consultation call!