Databricks Consulting, Implementation & Migration Services
Kanerika helps enterprises leverage the incredible features of Databricks to enhance their data analytics, governance, and AI ecosystem. Our certified experts design, implement, and optimize Databricks environments that deliver speed, scalability, and insights.
Watch Kanerika Unlock Faster Insights with Databricks
Proven Expertise, Measurable Outcomes
60%
Reduction in infrastructure costs
70%
Shorter ETL
runtime
5x
Faster Data
processing
80%
Improvement in
data accuracy
50%
Faster time to
insights
Comprehensive Suite of Databricks Services
Kanerika delivers end-to-end Databricks consulting, implementation, and migration services. From strategy to deployment and ongoing optimization, we help you every step of the way.
Consulting & Strategy
- Assess your current data landscape and analytics maturity.
- Build a Databricks adoption roadmap with governance and security.
- Define architecture and integration strategies for cloud deployment.
Implementation & Deployment
- Deploy Databricks on Azure, AWS, or Google Cloud with best practices.
- Configure clusters, workspaces, governance, and access controls.
- Integrate Databricks with your data lakes, warehouses, and BI tools.
Data Engineering & Pipeline Development
- Design automated ETL and ELT workflows using Delta Lake.
- Implement medallion architecture with bronze, silver, and gold layers.
- Build streaming and batch pipelines that scale seamlessly.
AI & Machine Learning Implementation
- Build scalable ML pipelines using MLflow and Databricks notebooks.
- Deploy predictive models and AutoML workflows for faster insights.
- Build and deploy production-grade gen AI applications with Mosaic AI.
Data Governance, Security & Compliance
- Implement Unity Catalog for unified data governance and lineage.
- Apply fine-grained, role-based access and permission controls.
- Maintain compliance with the GDPR, HIPAA, and SOC 2 standards.
Managed Services & Continuous Support
- Provide 24×7 monitoring, alerts, and issue resolution.
- Manage platform updates, patches, and version upgrades.
- Deliver proactive performance and cost optimization.
MIGRATION SOLUTIONS
We specialize in migrating large-scale Informatica ETL workloads into Databricks. This is perfect for companies moving away from proprietary ETL toward a future-proof, cloud-native data engineering platform.
Assessment
Scan all Informatica mappings, workflows, and metadata
Provide a migration roadmap with time and effort estimates
Identify dependencies, reusable components, and transformation
Conversion
Convert Informatica transformations into Spark-native Databricks pipelines
Translate mappings and logic into PySpark notebooks
Maintain functional equivalence across all converted processes
Validation
Run automated tests to confirm accuracy and performance
Verify end-to-end workflows and data flow consistency
Document validation reports for traceability
Transition
Execute cutover with minimal business downtime
Set up real-time monitoring and alerting for early issue detection
Provide rollback and contingency support during production move
Enablement
Build a modern data engineering setup with Databricks and Delta Lake
Integrate MLflow for machine learning lifecycle management
Train teams on new workflows, pipelines, and monitoring tools
Why Choose Kanerika for Databricks Solutions
As a certified Databicks partner, Kanerika enables enterprises to adopt, deploy, and scale Databricks with confidence.
Proven Databricks Expertise
Deep experience in Databricks architecture, governance, optimization, and performance tuning.
End-to-End Implementation
Full lifecycle coverage including consulting, setup, training, and ongoing platform support.
Seamless Migration Experience
Successful migration of legacy systems and ETL platforms into modern Databricks environments
Strong Data Governance & Security
Secure operations aligned with ISO 27701, ISO 27001, SOC II, and compliance frameworks.
Optimized Performance & Cost Efficiency
Continuous tuning and resource optimization to reduce costs and maximize performance.
Long-Term Business Impact
Focused on faster insights, lower ownership costs, and smarter data-driven decisions
Getting Started
Step 1
Free Consultation
Talk to our experts about your data challenges. We’ll assess your current setup and identify opportunities.
Step 2
Proof of Concept
We build a small pilot to demonstrate value. See results before committing to full implementation.
Step 3
Full Implementation
Once you’re confident, we execute the complete solution with minimal disruption to your operations.
Get Started Today
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Frequently Asked Questions (FAQs)
01What is Databricks and how does it work for enterprise data management?
A unified data and AI platform built on Apache Spark. It processes large datasets, supports real-time analytics, and enables machine learning workflows in one environment. Enterprises use it to eliminate data silos and accelerate insights.
02How long does Databricks implementation take for enterprise organizations?
Standard enterprise deployments take 4–8 weeks. This includes workspace setup, cluster configuration, governance implementation, and integration with existing data sources. Kanerika’s accelerators reduce timelines while maintaining compliance.
03Which cloud platforms support Databricks deployment?
Databricks runs on Azure, AWS, and Google Cloud Platform. We help you choose the right cloud based on your existing infrastructure, security requirements, and cost objectives.
04What is the difference between Databricks and traditional data warehouses?
Databricks combines data lake flexibility with warehouse performance. It handles structured and unstructured data, supports streaming, and integrates ML natively. Traditional warehouses are limited to structured data and batch processing.
05Can Databricks integrate with existing business intelligence tools?
Yes. Databricks connects with Power BI, Tableau, Looker, and other BI platforms. We configure secure connections and optimize queries for fast dashboard performance.
06What size datasets can Databricks handle efficiently?
Databricks scales from gigabytes to petabytes. Its distributed Spark architecture handles massive datasets with auto-scaling clusters that adjust compute resources based on workload demands.
07How does Databricks lakehouse architecture differ from data lakes?
A lakehouse combines data lake storage with warehouse reliability. Delta Lake adds ACID transactions, schema enforcement, and versioning. This eliminates data quality issues common in traditional lakes.
08What programming languages does Databricks support?
Databricks supports Python, SQL, Scala, R, and Java. Teams can use their preferred language within notebooks for data engineering, analytics, and machine learning workflows.
09Does Databricks require specialized infrastructure or hardware?
No. Databricks is fully cloud-based and serverless. You only need a cloud account (Azure, AWS, or GCP). Infrastructure provisioning, scaling, and maintenance are automated.
10What is the typical ROI timeline for Databricks implementation?
Most enterprises see measurable ROI within 3–6 months. Benefits include faster query performance, reduced infrastructure costs, shorter ETL runtimes, and improved data accuracy.
11How do you migrate Informatica workflows to Databricks?
We scan Informatica mappings and metadata, convert transformations to PySpark, validate logic and data, then execute cutover. Our automated framework handles 95% of conversion work while preserving business rules.
12What legacy ETL tools can be migrated to Databricks?
We migrate from Informatica, SSIS, Azure Data Factory, Talend, DataStage, and custom ETL scripts. Each migration includes automated conversion, validation, and performance optimization.
13How much downtime is required for ETL migration to Databricks?
Minimal. We execute parallel runs during transition, validate outputs, then switch over during low-traffic windows. Most cutovers complete in hours with zero data loss.
14What is the success rate of Informatica to Databricks migrations?
Our migration success rate exceeds 98%. Automated validation compares source and target data at every step. Rollback plans ensure business continuity if issues arise.
15Can you migrate on-premises data warehouses to Databricks?
Yes. We migrate from Teradata, Oracle, SQL Server, and other on-premises warehouses. Migration includes data transfer, schema conversion, query optimization, and performance tuning.
16How do you ensure data integrity during migration?
We run automated reconciliation tests comparing row counts, data types, and business logic outputs. Validation reports document accuracy at table, column, and record levels.
17What happens to custom ETL code during migration?
Custom code is analyzed and converted to Spark-optimized PySpark or SQL. We preserve business logic while improving performance through parallel processing and distributed computing.
18How long does a typical Informatica to Databricks migration take?
Timeline depends on complexity and volume. Most migrations complete in 8–16 weeks, including assessment, conversion, validation, and cutover phases.
19What post-migration support do you provide?
We offer 24×7 monitoring, issue resolution, performance tuning, and knowledge transfer. Support continues until your team is fully confident managing the new environment.
20Can you migrate real-time streaming pipelines to Databricks?
Yes. We convert batch ETL to streaming pipelines using Databricks structured streaming and Delta Lake. This enables real-time data ingestion and instant analytics.
21What is Delta Lake and why should enterprises use it?
Delta Lake adds reliability to data lakes through ACID transactions, schema enforcement, and time travel. It prevents data corruption and enables rollback, making analytics trustworthy.
22How does medallion architecture improve data quality?
Medallion architecture organizes data into Bronze (raw), Silver (cleaned), and Gold (aggregated) layers. Each layer adds validation and transformation, ensuring analytics teams access quality data.
23Can Databricks handle both batch and streaming data?
Yes. Databricks processes batch and streaming workloads using the same pipelines. Structured streaming enables real-time ingestion while maintaining consistency with batch processes.
24What are the benefits of automated ETL pipelines in Databricks?
Automated pipelines reduce manual effort, eliminate human error, and ensure consistent data delivery. They handle scheduling, error handling, and monitoring without intervention.
25How do you optimize Databricks pipeline performance?
We analyze query plans, optimize partition strategies, implement caching, and tune Spark configurations. This reduces processing time and lowers compute costs.
26Can Databricks ingest data from multiple sources simultaneously?
Yes. Databricks connects to databases, APIs, file systems, and streaming sources. We configure multi-source ingestion with parallel processing for faster data availability.
27What data formats does Databricks support?
Databricks reads CSV, JSON, Parquet, Avro, ORC, Delta, and XML. It also handles unstructured data like images, PDFs, and logs for comprehensive analytics.
28How does Databricks handle data pipeline failures?
Databricks includes built-in retry logic, error logging, and alerting. We configure automated recovery workflows and notification systems to minimize downtime.
29Can you build incremental data loading pipelines in Databricks?
Yes. We implement change data capture (CDC) and incremental loading using Delta Lake merge operations. This reduces processing time and resource consumption.
30What is the difference between ETL and ELT in Databricks?
ETL transforms data before loading. ELT loads raw data first, then transforms within Databricks. ELT leverages Spark’s power for faster, more flexible transformations.
31How does Databricks support machine learning workflows?
Databricks integrates MLflow for experiment tracking, model management, and deployment. Data scientists build, train, and deploy models using notebooks with scalable compute.
32What is MLflow and how does it improve ML operations?
MLflow tracks experiments, manages models, and automates deployment. It provides version control, comparison tools, and production deployment pipelines for reliable ML operations.
33Can Databricks deploy generative AI applications?
Yes. Databricks Mosaic AI enables building and deploying gen AI apps. It includes vector databases, LLM integration, and retrieval-augmented generation (RAG) capabilities.
34How do you integrate existing ML frameworks with Databricks?
We connect TensorFlow, PyTorch, Scikit-Learn, XGBoost, and Hugging Face. Data scientists use familiar tools while leveraging Databricks’ scalability and collaboration features.
35What is AutoML in Databricks?
AutoML automates model selection, hyperparameter tuning, and feature engineering. It accelerates ML development by testing multiple algorithms and configurations automatically.
36Can Databricks handle real-time ML model scoring?
Yes. Models deployed through MLflow serve predictions via REST APIs. We configure low-latency endpoints for real-time scoring in production applications.
37How does Databricks support collaborative data science teams?
Notebooks enable real-time collaboration with shared workspaces, version control, and commenting. Teams work together on code, visualizations, and documentation simultaneously.
38What types of ML models can be built on Databricks?
Databricks supports classification, regression, clustering, recommendation systems, time series forecasting, NLP, and computer vision models across industries.
39How do you monitor ML model performance in production?
We implement monitoring dashboards tracking accuracy, latency, and drift. Automated alerts notify teams when models degrade, triggering retraining workflows.
40Can Databricks accelerate AI experimentation cycles?
Yes. Collaborative notebooks, scalable compute, and MLflow reduce experimentation time from weeks to days. Teams iterate faster with unified data access and version control.
41What is Unity Catalog in Databricks?
Unity Catalog provides centralized governance for data, ML models, and analytics. It manages permissions, tracks lineage, and ensures consistent access control across workspaces.
42How does Databricks ensure GDPR compliance?
Databricks supports data residency, encryption, access controls, and audit logging required for GDPR. We configure right-to-be-forgotten workflows and data anonymization processes.
43Is Databricks HIPAA compliant for healthcare data?
Yes. Databricks meets HIPAA requirements with encryption, access controls, and audit trails. We configure PHI handling policies and secure data pipelines for healthcare organizations.
44How do you implement role-based access control in Databricks?
We define roles based on job functions, assign permissions to workspaces and data assets, then integrate with Azure AD, AWS IAM, or Google Identity for centralized management.
45Does Databricks support data encryption?
Yes. All data is encrypted in transit using TLS and at rest using AES-256. We configure customer-managed keys and private endpoints for enhanced security.
46How does Databricks handle audit logging and compliance reporting?
Unity Catalog logs all data access, modifications, and user activities. We configure automated compliance reports for SOC 2, ISO 27001, and regulatory audits.
47Can you track data lineage in Databricks?
Yes. Unity Catalog provides end-to-end lineage from source to consumption. Teams see how data flows through pipelines, transformations, and analytics for transparency.
48How do you secure sensitive data in Databricks environments?
We implement column-level encryption, dynamic data masking, and fine-grained permissions. Sensitive fields are protected while maintaining usability for authorized users.
49What disaster recovery options does Databricks provide?
Databricks supports cross-region replication, automated backups, and point-in-time recovery. We configure disaster recovery plans with defined RTOs and RPOs.
50How does Databricks prevent unauthorized data access?
Multi-layered security includes network isolation, identity federation, MFA, and attribute-based access control. We configure least-privilege policies and monitor access patterns continuously.
