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What You'll Learn
With distributed storage and MapReduce batch processing, Hadoop has been a game changer for businesses for big data processing. But as a modern enterprise, you need better agility, scalability, and efficiency. Here comes Databricks – the world’s first data intelligence platform powered by generative AI.
Is migration from Hadoop to Databricks on your mind? You have come to the right place. In this guide, you will learn about the best steps you could follow to enable a smooth and successful migration from Hadoop to the Databricks Data Intelligence Platform.
Architectural Differences between Hadoop and Databricks
Hadoop is based on a traditional distributed storage and compute model that’s primarily based on Hadoop Distributed File System (HDFS) and MapReduce. You are required to manage clusters manually which can lead to higher administrative overhead. On the other hand, Databricks is a cloud native solution that leverages object storage rather than HDFS. The core processing engine is Apache Spark.
In Hadoop, storage and compute are coupled. This means if you scale compute, you will be required to scale storage and vice versa. Databricks is the opposite of this. Its architecture is decoupled and allows you to scale storage and compute independently. This elasticity helps you to optimize costs and performance based on workload requirements.
Hadoop and Databricks architecture are also different in terms of cluster management and optimization. With Hadoop, you require DevOps resources who will perform manual cluster provisioning, tuning, and maintenance. Databricks, on the other hand, provides you with fully managed clusters with auto-scale functionality. Plus, with Databricks, you get to optimize Spark workloads with Photon Engine, caching, and query optimization techniques.
The Hadoop architecture relies on external, third-party tools like Ranger, Sentry, or Kerberos for data security and governance. But with Databricks, you get built-in security features such as Role-Based Access Control (RBAC), IAM integrations, data lineage tracking, & more. All of this automatically makes Databricks the preferred choice for modern enterprises.
Why Move from Hadoop to Databricks
There’s no denying that Hadoop’s architecture works wonder when it comes to storing and processing big data. However, you may face challenges in scalability and integration with modern, cloud-based tools and technologies. In this section, we’ll quickly cover why moving from Hadoop to Databricks is a wise decision you can make for your organization.
- There is significant administrative overhead associated with Hadoop. You need to pay for maintaining clusters and managing hardware and fine-tuning performance. But as Databricks is a fully managed platform, this issue is automatically resolved. It is serverless, elastic cloud-native data warehouse that scales for users. Infrastructure management is automated, and your team can focus better on deriving value from data rather than maintaining it.
- Another limitation of Hadoop is its inability to provide real-time insights and scale, especially when workloads fluctuate. Databricks offers auto-scaling and optimized Apache Spark performance. You can scale workloads up and down based on your requirements.
- Now we talk about the most promising and attractive benefit of Databricks – its cloud native architecture. Hadoop was originally designed for on-premises deployments. Databricks is cloud-native and will easily integrate with giant cloud providers like AWS, Azure, and Google Cloud. With this, you get to use elastic storage and compute resources.
- Next, Databricks provides a collaborative workspace with notebooks, version control, and real-time collaboration. If you’re a data engineer or data scientist, Databricks can prove to be a great platform to enhance productivity.
- Last but one of the most important benefits of moving from Hadoop to Databricks is that the Databricks Data Intelligence Platform provides you with built-in support for advanced AI/ML capabilities. Hadoop doesn’t have these capabilities. You require additional setup and configurations for AI/ML workloads.
Best Steps for Hadoop to Databricks Migration
Hadoop Environment Assessment
No successful migration happens without the thorough assessment of your current Hadoop environment. Start with data storage. Identify different dataset formats, sizes, and access patterns in your existing ecosystem.
Current data pipelines, Spark and MapReduce jobs, and batch vs real-time workloads are reviewed. Next comes metadata and governance. Data analysts and engineers need to analyze Hive metastore, security policies, and data lineage.
And then comes one of the most critical parts of the first step – aligning migration strategy with business requirements – whether you’re doing this migration for cost savings, enabling artificial intelligence and machine learning, or some other business objective.
Finalize the Right Migration Approach
You have three types of migration options – lift and shift approach, replatforming, and refactoring. In the first one, you will migrate workloads as-is to Databricks. Lift-and-shift is a good option if you are looking for quick migrations.
Replatforming would involve you adapting workloads for Databricks, leveraging Delta Lake for optimized storage and faster queries. Refactoring would require you to redesign applications to fully utilize Databricks’ advanced features such as real-time streaming, ML integration, and serverless architecture.
If you are searching for maximum performance gains, real-time analytics, and AI/ML integration, the refactoring migration method is the best for you.
Standardizing Security, Governance, & Compliance
For secure migration, make sure that role-based access control & fine-grained access policies are implemented. Remember to perform audit logging and data lineage tracking. Aligning with industry standards like GDPR, SOC 2, and other important ones is a must.
Example: If you are from the healthcare sector, HIPAA is one of the most important standards your migration approach should align with. For encryption, access control, and monitoring, leverage Databricks’ built-in security measures. Some of the most advanced security features provided by Databricks are customer-managed keys, private link, serverless egress controls, and more. Databricks’ certifications and standards include CCPA, FedRAMP, GDPR, HITRUST, ISO 27001, ISO 27017, ISO 27701, and others.
Migrating & Transforming Data
Moving your data from Hadoop to Databricks will involve several tasks. HDFS data is extracted into a cloud solution like AWS S3, Azure, or Google Cloud Storage. Legacy formats like ORC, Avro, and Parquet will be converted into Delta Lake Format.
This will enhance your overall data governance and performance. Metadata also needs to be migrated from Hive to Unity Catalog or an equivalent governance framework in Databricks.
Use checksums and automated reconciliation processes to validate data integrity.
Testing & Optimizing Performance
Once the performance is done, it is important to benchmark workloads. This is done to compare performance before and after the migration.
Next thing that’s done here is the optimization of cluster configurations. Use Databricks’ autoscaling and job scheduling to do it.
It’s best to improve query speeds by using caching mechanisms like Delta Caching and data skipping.
If you are looking to balance performance with cloud expenses, implement cost governance strategies, including spot instances and workload prioritization.
User Training & Change Management
This is one of the most important migration steps for Hadoop to Databricks transition. Remember migration is not limited to being a shift from one platform to another!
It requires user adoption and process alignment. We recommend training teams on Databricks best practices and workspace navigation.
It is critical to re-engineer workflows to maximize productivity in the new environment.
Encourage cross-functional collaboration with shared workspaces and real-time notebooks.
Choose LumenData for Databricks Deployment
LumenData is an Approved Delivery Partner for Databricks Professional Services. We help businesses with Data Engineering, Data Science, Master Data Management, Self-Service Reporting, Data Monetization, & more. When you select LumenData as your Databricks Consulting & Implementation partner, you get:
- Customized solutions for Lakehouse consumption, including Data Warehousing, Data Engineering, Data Streaming, Data Monetization, and Data Science, & ML.
- Services for data visualization, graph networks, data reliability & performance on Delta Lake
- Best-in-class accelerators for data warehouse, cost governance, data security, and large language models.
To know more about our offerings related to Databricks, visit here.
About LumenData:
LumenData is a leading provider of Enterprise Data Management, Cloud and Analytics solutions and helps businesses handle data silos, discover their potential, and prepare for end-to-end digital transformation. Founded in 2008, the company is headquartered in Santa Clara, California, with locations in India.
With 150+ Technical and Functional Consultants, LumenData forms strong client partnerships to drive high-quality outcomes. Their work across multiple industries and with prestigious clients like Versant Health, Boston Consulting Group, FDA, Department of Labor, Kroger, Nissan, Autodesk, Bayer, Bausch & Lomb, Citibank, Credit Suisse, Cummins, Gilead, HP, Nintendo, PC Connection, Starbucks, University of Colorado, Weight Watchers, KAO, HealthEdge, Amylyx, Brinks, Clara Analytics, and Royal Caribbean Group, speaks to their capabilities.
For media inquiries, please contact: marketing@lumendata.com.
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