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.
Get Started with Databricks Consulting Solutions
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.
Databricks Mosaic AI: Build and Deploy Enterprise AI Agents
Mosaic AI combines Agent Bricks, Vector Search, and AI Gateway on one governed stack, so your agents retrieve, reason, and act on real enterprise data. Our certified Databricks engineers handle the full build, from evaluation to production, once your lakehouse is ready.
Agent Bricks
Build task-specific agents fast with Databricks’ no-code Agent Bricks, scored against the CLEARS framework for latency and safety.
Vector Search
Index Delta tables directly, auto-sync on changes, and run hybrid vector-plus-keyword search so agents retrieve accurate context.
Managed MCP
Expose Unity Catalog functions, Genie, and DBSQL as governed tools your agents can call safely and audit end to end.
AI Gateway
Route every model and tool call through one gateway with built-in cost controls, rate limits, and audit logging.
Databricks Unity Catalog: One Governance Layer for Data and AI Agents
Unity Catalog unifies access control, lineage, and audit logging across every workspace, region, and cloud, so one policy applies everywhere data and models are used. Our certified Databricks engineers configure and migrate your catalog, from Hive consolidation to Unity AI Gateway rollout.
Fine-Grained Access Control
Row, column, and attribute-based policies applied once and enforced everywhere, from notebooks to dashboards to agents.
End-to-End Lineage & Audit
Full lineage across tables, models, and dashboards, with every access logged automatically for compliance reviews.
Cross-Cloud, Cross-Workspace Governance
One catalog spanning every workspace, region, and cloud, so policies stay consistent as you scale.
Unity AI Gateway Setup
Govern foundation model access with the same fine-grained policies you use for data, plus spend caps and smart routing.
Migration & Metastore Consolidation
Move legacy Hive metastores and siloed catalogs into a single governed Unity Catalog without disrupting live pipelines.
Compliance-Ready Configuration
Access controls and audit logging configured to align with ISO 27701, ISO 27001, SOC 2, and industry mandates.
Databricks Lakebase: Serverless Postgres Built Into Your Lakehouse
Lakebase runs a fully managed, Postgres-compatible OLTP database on Databricks-managed storage, so operational and analytical data share the same governed foundation without a sync pipeline. Our certified Databricks engineers deploy and tune Lakebase, from architecture through production cutover, once your Delta tables are ready.
Unified OLTP + Analytics
Postgres-compatible tables live on Delta Lake storage, so application data and analytics run on one governed foundation with zero replication lag.
Serverless & Autoscaling
Instances scale compute automatically with traffic, and every new Lakebase project runs autoscaling by default, so you pay only for what you use.
Kanerika Deployment & Tuning
We handle architecture, pilot validation, and production cutover, then tune connection pooling and compute so Lakebase performs under real traffic.
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
MIGRATION SOLUTIONS
How Enterprises Win with Kanerika and Databricks
90% Reduction in Data Lag for a Financial Services Provider
Impact:
- 90% Reduction in data lag
- 96% Reduction in reporting latency
- 100% Increase in BI adoption
Eliminating Data Silos and Modernizing Analytics Infrastructure with Databricks
Impact:
- Zero Downtime, no production interruption
- 100% Legacy Infrastructure Decommissioned
- 100% Centralized governance, lineage, and data access
71% Higher Reporting Accuracy with Informatica to Databricks
Impact:
- 71% Higher Reporting Accuracy
- 38% Reduction in Data Handling Costs
- 64% Faster Decision-Making
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.
Let’s Transform Your Business
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.
16What is Databricks Mosaic AI used for?
Databricks Mosaic AI is the platform for building, evaluating, and deploying production AI agents on your own data. It combines Agent Bricks for no-code agent construction, Mosaic AI Vector Search for retrieval, and AI Gateway for governed model access. Teams use it for RAG applications, support agents, and automated workflows that reason over governed Delta tables.
17How does Mosaic AI Vector Search work?
Mosaic AI Vector Search indexes your Delta tables directly and auto-syncs whenever source data changes, so there’s no separate ETL step. It runs hybrid search, combining dense vector retrieval with keyword matching, then re-ranks results. Because it inherits Unity Catalog governance, every query respects the same access policies as your underlying tables.
18What is Unity Catalog in Databricks?
Unity Catalog is Databricks’ unified governance layer for data, models, notebooks, dashboards, and AI agents. It centralizes access control, lineage tracking, and audit logging across every workspace, region, and cloud through one ANSI SQL interface. Instead of managing permissions per tool, teams define a policy once and it applies everywhere.
19Can Unity Catalog govern AI agents and models, not just data?
Yes. Unity Catalog extends the same fine-grained access policies, row and column filters, and audit logging used for tables to models, functions, and AI agents. The Unity AI Gateway adds governance for foundation model access, letting admins apply spend caps, rate limits, and smart routing on top of standard data permissions.
20What is Databricks Lakebase and how is it different from a regular database?
Lakebase is a fully managed, serverless, Postgres-compatible OLTP database built directly into Databricks. Unlike a standalone Postgres instance, Lakebase tables sync continuously to Delta Lake and register in Unity Catalog automatically, so existing governance and lineage apply to operational data without a separate pipeline connecting the two systems.
21Is Databricks Lakebase generally available?
Yes. Lakebase reached general availability on February 3, 2026, after entering public preview in 2025. It’s built on technology from Neon, is PostgreSQL 16-compatible, and runs across AWS and Azure in 14 regions. Since March 2026, new Lakebase instances are created with autoscaling enabled by default, so compute adjusts automatically with traffic.
