Organizations are migrating from their on-premise, legacy Hadoop Data Lake to a more modern data architecture to take advantage of AI to fulfill the long-awaited promise of unlocking business value from semi- and unstructured data. Databricks tends to be the modern platform of choice for Hadoop migrations due to core architectural similarities. Apache Spark has its roots in Hadoop, and its developers founded Databricks. There is a pretty good chance you are using Parquet as your file format in HDFS. They even share the Hive Metastore for data abstraction and discovery.
Teams tasked with migrating from their legacy Hadoop platforms to Databricks face unique and unexpected challenges. since Hadoop is a platform, not just a database. In fact, approaching this as a database migration hides most of the technical challenges and can lead to a fundamental misunderstanding of the scope of the project. This is particularly true when you consider Hive only as a lift-and-shift to Databricks. In many cases, it makes more sense to focus on the data movement rather than the data storage. Imagine an Oozie-first approach to a Hadoop migration.
Change your mindset from a data platform migration to a business process modernization, and read on.
Introducing IngestIQ
IngestIQ leverages cutting-edge AI models available in Databricks to ingest and translate a variety of workflows across the Hadoop ecosystem into an innovative Intermediate Domain Specific Language (iDSL). This AI-centric transformation yields a business-first perspective on data workflows, uncovering the underlying business intents and dataset value. With AI at its core, IngestIQ empowers a human-on-the-loop (HOTL) model to make precise, informed decisions that prioritize modernization and high-impact migratory strategies.
- Traditional tools like Oozie, Airflow, and NiFi often encode complex operational logic rather than business rules, obscuring the true business value. By utilizing AI-driven insights, IngestIQ transforms these workflows into an iDSL that highlights business relevance, enabling stakeholders to make strategic, value-driven decisions. AI enhances the HOTL’s ability to discern critical, redundant, or obsolete jobs, focusing efforts on strategically significant modernization. This prioritization prevents misallocation of resources towards low-impact migrations, optimizing computational and storage costs while emphasizing data security, compliance, and business-critical areas.
Why this matters
- Oozie deployments often encode operational logic, not business intent. Translating to an iDSL makes intent explicit, enabling business owners to triage what matters.
- Human review reduces risk of incorrectly migrating jobs that are no longer needed or that embed obsolete business rules.
- Column-level prioritization prevents over-migration of low-value data and focuses security, lineage, and Unity Catalog efforts where business impact is highest.
- Provides auditable, repeatable decisioning and a clear path from discovery to production cutover in Databricks.
IngestIQ’s AI-Driven Capabilities
- Comprehensive Ingestion & AI-Powered Analysis:
- AI algorithms process diverse inputs from Oozie XML workflows, Apache Airflow DAGs, and Apache NiFi flows. Both static analyses and AI-enhanced runtime assessments map job dependencies, execution metrics, and data lineage .
- Business-First AI Representation with iDSL:
- The iDSL leverages AI to generate concise, business-centric representations of data workflows. This AI-driven translation surfaces transformation intents and dataset significance clearly, ensuring decisions align closely with strategic goals.
- AI-Based Triage & Workflow Optimization:
- IngestIQ uses AI and machine learning classifiers to intelligently identify and optimize redundant, outdated, or misaligned workflows, supported by AI-derived evidence and confidence metrics.
- AI-Enhanced HOTL Interface:
- Equipped with AI-powered dashboards and predictive analytics, the HOTL interface enables stakeholders to navigate prioritized actions efficiently.
- Data-Driven Business-Priority AI Ranking:
- A sophisticated AI model evaluates workflows across multiple criteria—business criticality, usage patterns, technical debt, cost, and compliance pressures. This advanced AI prioritization focuses on the most impactful areas first.
- Automated AI Workflow Generation:
- From AI-optimized iDSL inputs, IngestIQ automates the generation of Spark templates, migration scripts, and compliance documents that seamlessly integrate into CI/CD pipelines for robust, secure implementation.
Example flow (end-to-end)
- Ingest Oozie metadata and execution logs => parse into ASTs and runtime profiles.
- Generate iDSL artifacts representing jobs and transforms, store in Git.
- Run triage models and rules => produce candidate list with evidence and priority scores.
- HOTL reviews, annotates, and approves actions via UI; approvals create commits.
- Approved artifacts trigger code & migration artifact generation (Spark templates, Delta migration scripts, Unity Catalog manifests).
- CI pipeline runs tests (unit, differential), security checks, and human approval gates.
- Deploy to Databricks staging; run parallel validation with Hadoop outputs; upon pass, cutover per schedule.
- Capture telemetry to refine triage models and priority weighting.
Conclusion
The IngestIQ Accelerator provides a pragmatic, auditable bridge between legacy Hadoop operational workflows and business-led modernization. By making intent explicit and placing a human-on-the-loop for final decisions, organizations get the speed and repeatability of automated translation without sacrificing governance or business risk management. Column-level prioritization ensures effort and controls focus on data that matters most—reducing cost, improving security posture, and accelerating value realization on Databricks.
Perficient is a Databricks Elite Partner. Contact us to learn more about how to empower your teams with the right tools, processes, and training to unlock your data’s full potential across your enterprise.
