Hadoop Migration to Databricks on Cloud with Our Automated Migration Product Suite
Existing Challenges with Hadoop System
- Year-on-year (Y-o-Y) data volume growth and new use cases requiring the provisioning of additional infrastructure.
- Complex operations and maintenance of clusters due to the host of multiple technologies and dependencies.
- There are frequent downtimes with aging systems.
- Patch management, bug fixes, and version upgrades at the infrastructural and software levels cause significant downtimes and operational barriers.
- Increasing Y-o-Y TOC for new infrastructure, licenses, data centers, and maintenance.
- Lack of enterprise-grade data governance and security tools.
- Complex distributed architecture poses challenges for implementing new technology solutions.
- Multiple technologies used to solve similar use cases create deployment and maintenance issues.
- Need for a separate DR solution and data node reliability issues with aging.
- Scalability of storage, compute, and memory requires new infrastructure, causing implementation delays.
- Integration with modern tools for ETL, visualization, and reporting always poses challenges.
- Complexity in fine-grained access controls at column level and multi-tenancy.
- Delays in the implementation of new use cases for AI, ML, analytics, and other required additional infrastructure.
- Missed SLAs and delayed reporting due to frequent cluster issues cause business and cost impacts.
- The solution is not simple enough for business teams to understand for self-service.
- Multiple data silos are created over time in most implementations.
- Fine-grained auditing of access, data, and monitoring is complex to implement.
How migrating to Databricks Solves these Challenges
- Databricks leverages cloud infrastructure to provide unlimited infrastructure capacity.
- One tool for complete ETL Pipelines, data analytics, instant data visualization, orchestration, and scheduling.
- Databricks platform is backed by a very high level of uptime guarantee from the cloud platform.
- Automated and scheduled patch management, upgrades, and fixes without user intervention.
- Complete control over infrastructure and software expenses with detailed cost breakdown, cost forecasting, and ‘pay what you use’ model. No additional cost for data center, power, etc.
- The platform provides capabilities for data governance, lineage tracking, monitoring, and auditing.
- Simpler architecture (databricks) for the implementation of business use cases.
- Easy migration of an existing codebase and integration with a variety of legacy and new technologies.
- Simpler DR implementations are backed by cloud platforms, guaranteeing data availability across regions.
- Capability to immediately scale up and scale down cluster capacity at the job level, project level.
- Simpler integration with other vendor tools and also backed by cloud platform integration capabilities.
- Better security, authorization, and governance controls at the lowest grain of data.
- Databricks enable new clusters, infrastructure, and technologies in minutes for new use case implementation.
- Integrated ML, AI, and Analytics Capabilities are also available with databricks.
- High cluster uptime backed by a cloud platform.
- The ability to scale up/down ensures the required processing and storage capacity is always available.
- Simple tools like jupyter notebooks for ml and ai, instant data visualization, time travel, and many other capabilities can assist business users with a minimal learning curve.
- Address multiple data silos in the course of the databricks migration/modernization program and build governance controls to prevent data duplication.
Our Partnership with Databricks
Datametica’s partnership with Databricks highlights our commitment to delivering excellence to our joint customers. Our collaboration, as a DPP-certified partner, is designed to provide top-tier expertise and support. Here are the key highlights of our partnership:
A strategic alliance at the global level with Databricks allows us to deliver services in all parts of the world
Providing clients with a fully compliant migration product suite that makes migration faster, lower costs and lower risks
Staffed by certified Databricks architects, data engineers and associates
Leveraging Databricks’ unique capabilities in hyper-scale cloud data warehousing, analytics, and machine learning
- Enables us to collaborate with Databricks professional services team to successfully deliver large scale migrations
How does Datametica’s Birds migrate data to Databricks
eagle
the planner
Architecting an end-to-end migration strategy, with workload discovery, assessment, planning, and cloud optimization.
raven
the transformer
Automated & Optimized code (SQL/Scripts/ETL) conversion to the cloud-native technologies.
pelican
the validator
Automating data validation, reconciliation, and comparison of datasets across two heterogeneous data stores.
How Datametica migrates Hadoop to Databricks
- Automated legacy data warehouse assessment and discovery
- Identify workload (metadata, data, and code) dependencies
- Create a detailed plan for migration to Databricks
- Guaranteed 100% automated code conversion to Databricks native and supporting technologies
- Create patterns for target platform
- Auto-generate the pattern for newer migration
- Query Editing for optimized fixes and performance tuning
- Automated data validation and reconciliation
- Data validation at the minutest level (cell-level)
- Data matching between Databricks and on-premise systems
- Once everything is set up, we provide the training and support to keep customers going with the new Databricks data warehous
How to Get Started
Hadoop on-premise data warehouse assessment is done by Eagle – Data Migration Planning Tool using SQL logs and metadata extracted from Hadoop. This helps in analyzing access patterns, all system activities, and artifacts that are needed to plan the cloud migration strategy.
The process is then taken over by Raven – Automated Workload Conversion Tool. It is used to automate and optimize workload (ETL/SQL/Scripts) conversion to Databricks. After the code conversion is done, Pelican – Automated Data Validation Tool comes into the picture to automate the data validation process between Hadoop and Databricks datasets to ensure confidence before the Hadoop system is decommissioned.
This engagement also results in:
Migration Strategy
Migration Plan
Effort Estimation
Solution Architect
Technical Architect
Our Other Solutions
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Netezza to Databricks
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Teradata to Databricks
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Oracle to Databricks
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FAQs
What are the benefits of Hadoop Migration to Databricks?
Some of the common benefits of migrating Hadoop to Databricks are:
- No aging hardware lock-in
- Elastic scaling
- Gain faster insights
- Reduce acquisition cost
- No data reliability issues
- Flexible clusters
- Enhanced productivity
What exactly is the Delta table in Databricks?
Delta Live Tables (DLT) simplifies the creation and management of dependable data pipelines that produce high-quality data on Delta Lake. With declarative pipeline development, automated data testing, and deep visibility for monitoring and recovery, DLT helps data engineering teams simplify ETL development and management.
