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What You'll Learn
Cloudera has been a major focus of enterprise data management especially for organizations leveraging Hadoop-based on-premises data lakes! But many organizations realize that, while Cloudera’s Hadoop ecosystem was an advancement, the limitations involve: an inflexible infrastructure, rising costs, and slow process of adaptation to modern analytics and AI needs. On the other hand, Databricks has now become the ‘lakehouse’ platform, integrating the best aspects of a data warehouse and a data lake. At the same time, dbt (data build tool) has revolutionized how data transformations are done by bringing modularity, collaboration, and governance into SQL-based workflows. This blog will help understand how to migrate from Cloudera to Databricks using dbt.
We will cover:
- Why organizations are migrating from Cloudera
- What value Databricks, as the new lakehouse platform, offers
- How dbt will play a foundational role in migrating
- How to establish a process to migrate
- How LumenData can quicken the pace of migration
Understanding Legacy: Cloudera
When Hadoop began gaining popularity in the 2000s, Cloudera led the way in providing businesses a way to manage distributed big data systems in a packaged enterprise way. With features such as HDFS for storage, Hive for query execution, and Spark for processing data, the technology helped business operations scale analytics into petabyte-scale data ingestion. But the innovative features of Cloudera’s technology have turned into a major constraint on big data systems today:
Heavy Infrastructure Costs
On-prem Hadoop clusters come with heavy CapEx and OpEx for hardware. Scaling requires more physical servers and complex hardware provisioning.
Inflexible Architecture
Hadoop-based systems were never designed with the modern cloud-first, real-time data requirements in mind.
Slow Innovation Cycles
The company has struggled to innovate at pace with rapid development of cloud-native platforms and AI workload developments.
Gaps in Governance and Security
Enterprises find data lineage, data governance and compliance fragmented in Hadoop. As enterprises attempt to run cloud-native systems to scale adoption, self-service analytics options, or build AI-readiness, many are now questioning Cloudera’s long-term viability.
Why Migrate to Databricks
Databricks offers a Lakehouse Platform that’s built to eliminate the trade-off between a data warehouse and a data lake. Here are several reasons organizations are moving to the Lakehouse standard:
- Unified Lakehouse Architecture - Combines structured and unstructured data on a single platform minimizing siloed data in the enterprise.
- Elastic Scalability - Enables you to split storage and compute for scale and pay for only what you need when you need it.
- Cloud Native Operations - Platform with no ingress fees in a totally managed environment.
- AI/ML Enablement - Natively enables MLflow, Tensorflow, and PyTorch, and works with large language models (LLM) too.
- Performance and Cost Optimization - Photon execution engine provides an order of magnitude increase in query performance.
- Ecosystem Integration - Provides seamless integrations with modern data tools e.g. dbt, Fivetran, Snowflake, and Informatica.
dbt’s Role in Modern Data Workflows
While Databricks takes on the heavy lifting of storage and computation, dbt adds structure, governance, and efficiency to data transformations.
What is dbt?
dbt (data build tool) is a data transformation framework. It is built for the cloud. It allows analysts and engineers to build models in SQL and then automatically handles dependencies, documentation, and testing.
Why dbt with Databricks
- Introduces software engineering best practices like modularity, versioning, CI/CD to analytics.
- Offers Transparency and helps with data transformations.
- Has built-in documentation and data testing capabilities.
- Allows for collaboration between the data engineers, analysts and the business teams.
Migration Strategy: Cloudera to Databricks with dbt
With a strong migration plan in place, you will minimize the risk of downtime, reduce risk, and speed-up time to adoption. Here is a suggested process to follow:
Step 1: Inventory Existing Cloudera Workloads
Inventory existing workloads for Hive, Pig, Spark, and MapReduce. Determine dependencies, data sources, and critical business pipelines.
Step 2: Select Pipelines Ready for Migration
Select business-critical transformations and those with little external dependencies. Separately consider legacy workloads that would require rearchitecture.
Step 3: Map Hadoop Transformations to dbt Models
Use the dbt ref() function to manage dependencies. Include testing and documentation in dbt natively.
Step 4: Migrate and Validate Data
Migrate datasets from HDFS to data lake storage, which could be AWS S3, Azure Data Lake, or GCP Storage. Validate migrated data against the original source for data quality, data accuracy, and data completeness as related to the data schema.
Step 5: Optimize with Best Practices
Use the Photon execution engine to optimize and tune performance, and the incremental models for large datasets, in dbt. Use CI/CD pipelines for automating and deploying the changes.
Build a future-proof data ecosystem with Databricks and dbt
When you migrate to Databricks with dbt, you are positioning yourself to unlock:
- Trained and fine-tuned LLMs on enterprise governed data.
- Real-time customer interaction analytics.
- Real-time customer interaction analytics.
- Unified and open data and AI governance with Databricks Unity Catalog
- Agentic AI workflows.
The LumenData Cloudera-to-Databricks Migration
LumenData specializes in data modernization and AI-enablement offerings. With us:
- You receive a unique 6-12 weeks migration quickstart for Cloudera to dbt transformations.
- You get support from an expert team trained across Informatica, Fivetran, Snowflake, and dbt.
- Your data stack is ready for enterprise AI and generative AI use cases.
Conclusion
Moving from Cloudera to Databricks with dbt will minimize infrastructure costs, enhance governance, facilitate real-time analytics, and prepare your enterprise for AI-led innovation. If you’re an enterprise considering a Cloudera to Databricks migration, this is the moment of truth. Choose dbt as your transformation framework and LumenData as your technology implementation partner and your data platform will be modern, governed, and AI-ready. Reach out to us today.
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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