As a trusted Databricks consulting partner, Kanerika empowers enterprises to unlock the full potential of their data and AI investments. We combine deep expertise in AI and data engineering with the Databricks Intelligence Platform to help businesses modernize data infrastructure, simplify complex workflows, and deliver measurable ROI.
faster AI deployment cycles
faster data engineering cycles
reduction in operational costs
quicker time-to-insight
Our collaboration with Databricks is about more than just technology, it’s about outcomes. By combining our AI expertise with their data intelligence capabilities, we help enterprises to move faster, make smarter decisions, and scale with confidence
Databricks is a cloud-based data intelligence platform that brings together data engineering, analytics, and AI on a unified, scalable Lakehouse architecture. Founded in 2013 by the creators of Apache Spark™, Delta Lake, and MLflow, Databricks combines the strengths of data warehouses and data lakes to deliver an open, modern platform designed for advanced data and AI workloads.
Global Consumers
Annual Revenue
Partners Worldwide
See how Kanerika and Databricks have helped businesses across industries achieve success with advanced data and AI solutions.
Databricks
Databricks
A Databricks consulting partner is a certified firm with specialized expertise in implementing, optimizing, and scaling the Databricks platform. These partners provide end-to-end services including data engineering, machine learning deployment, lakehouse architecture design, and platform migration to help enterprises maximize their data intelligence investments.
Kanerika combines deep technical expertise in AI and data engineering with proven implementation experience across enterprise clients. As a Databricks partner, we deliver tailored solutions for lakehouse architecture, data modernization, and AI-powered analytics that drive measurable business outcomes and accelerate time-to-value.
Databricks consulting partners offer comprehensive services including platform implementation, data pipeline development, Delta Lake optimization, machine learning model deployment, data governance setup, migration from legacy systems, performance tuning, and ongoing managed services to ensure continuous platform optimization and ROI maximization.
Databricks lakehouse architecture unifies data warehousing and data lakes, eliminating silos while supporting both structured and unstructured data. This approach reduces infrastructure complexity, improves data quality, enables real-time analytics, and provides a single source of truth for AI and business intelligence initiatives.
The Databricks Data Intelligence Platform combines data engineering, collaborative data science, machine learning, and business analytics in a unified environment. Built on lakehouse architecture, it enables enterprises to streamline workflows, accelerate AI adoption, and transform raw data into actionable intelligence at scale.
Kanerika provides end-to-end Databricks implementation services, from architecture design and platform setup to data migration and user training. Our experts leverage best practices to configure optimal workspace environments, establish data governance frameworks, and build scalable pipelines that align with your business objectives.
Healthcare, financial services, retail, manufacturing, logistics, and pharmaceuticals benefit significantly from Databricks consulting. Partners like Kanerika deliver industry-specific solutions for regulatory compliance, predictive analytics, customer intelligence, supply chain optimization, and fraud detection using the Databricks platform’s advanced capabilities.
Databricks migration timelines vary based on data volume, system complexity, and business requirements. Simple migrations may complete in 6-8 weeks, while enterprise-scale transformations involving legacy modernization typically require 3-6 months. Experienced Databricks partners optimize this process through proven methodologies and accelerators.
Delta Lake is an open-source storage layer providing ACID transactions, schema enforcement, and time travel capabilities for data lakes. It ensures data reliability, enables concurrent reads and writes, and simplifies data pipeline management, making it essential for building production-grade analytics and AI applications.
Yes, Databricks seamlessly integrates with cloud platforms like AWS, Azure, and GCP, as well as data warehouses, business intelligence tools, and legacy systems. Databricks consulting partners design integration architectures that leverage existing investments while modernizing data capabilities and enabling advanced analytics use cases.
Databricks consulting costs depend on project scope, implementation complexity, data volumes, and ongoing support requirements. Partners typically offer flexible engagement models including fixed-price
projects, time-and-material arrangements, and managed services subscriptions tailored to enterprise budgets and strategic priorities.
Databricks provides MLflow for experiment tracking, model registry, AutoML capabilities, and production deployment pipelines. The platform enables collaborative model development, supports popular ML frameworks like TensorFlow and PyTorch, and scales from experimentation to enterprise-wide AI deployment with built-in governance.
Unity Catalog is Databricks’ unified governance solution for managing data, analytics, and AI assets across clouds. It provides centralized access control, automated lineage tracking, data discovery, and compliance features, enabling enterprises to govern their entire data intelligence platform from a single interface.
Certified Databricks partners implement comprehensive security measures including encryption at rest and in transit, role-based access controls, network isolation, audit logging, and compliance frameworks. They configure Unity Catalog governance, establish security policies, and ensure adherence to industry regulations like GDPR and HIPAA.
Unlike traditional data warehouses limited to structured data and batch processing, Databricks lakehouse architecture handles structured, semi-structured, and unstructured data with real-time and batch capabilities. It provides superior scalability, lower costs, and native support for AI/ML workloads without data movement.
structured methodology assessing current data landscape, designing target lakehouse architecture, executing phased migration, and optimizing performance. Our approach minimizes disruption while accelerating transformation, incorporating data quality improvement, governance implementation, and stakeholder enablement throughout the engagement.
Databricks SQL Analytics provides business intelligence capabilities directly on the lakehouse, enabling analysts to query data using familiar SQL syntax, build dashboards, and create visualizations. It offers serverless compute, query optimization, and integration with BI tools for fast, cost-effective analytics.
Yes, Databricks excels at real-time streaming analytics through Structured Streaming and Delta Live Tables. Partners configure streaming pipelines that ingest, process, and analyze data continuously from sources like Kafka, enabling use cases such as fraud detection, IoT analytics, and real-time personalization.
Databricks Workflows orchestrate complex data pipelines, ML training, and analytics jobs across multiple tasks. They provide scheduling, dependency management, monitoring, and alerting capabilities that simplify production operations, reduce maintenance overhead, and ensure reliable execution of business-critical data processes.
Kanerika combines technical implementation with change management, providing comprehensive training, documentation, and knowledge transfer. We establish centers of excellence, develop best practice guidelines, and offer ongoing support to ensure teams effectively leverage Databricks capabilities and drive continuous innovation.
Databricks Lakehouse for manufacturing enables predictive maintenance, quality analytics, supply chain optimization, and IoT data processing. Manufacturing-focused Databricks partners design solutions that integrate production systems, analyze sensor data, optimize operations, and improve product quality through advanced analytics and AI.
While Snowflake focuses on cloud data warehousing, Databricks provides a comprehensive lakehouse platform supporting data engineering, advanced analytics, and machine learning. Databricks offers superior AI/ML capabilities, open-source flexibility, and unified governance, making it ideal for organizations pursuing comprehensive data intelligence strategies.
Leading Databricks partners maintain certifications including Databricks Certified Associate Developer, Professional Data Engineer, and Machine Learning Professional credentials. Partners like Kanerika also hold complementary certifications in Microsoft Azure, AWS, and demonstrate proven implementation experience across enterprise clients.
Databricks enables data democratization through SQL Analytics for business users, collaborative notebooks for analysts, and MLflow for data scientists. Unity Catalog
g organizations to empower stakeholders with self-service analytics capabilities.
Databricks AutoML automates machine learning model development by testing multiple algorithms, performing feature engineering, and selecting optimal configurations. It generates reproducible code, provides model explanations, and accelerates time-to-value, enabling teams with varying ML expertise to build production-ready models efficiently.
Yes, Databricks operates across AWS, Azure, and Google Cloud with consistent capabilities. Multi-cloud Databricks consulting partners design architectures that leverage platform-native services, ensure data portability, and implement governance across cloud environments, providing flexibility and avoiding vendor lock-in.
Organizations typically achieve ROI through reduced infrastructure costs, faster time-to-insight, improved data quality, and accelerated AI adoption. Databricks partners help quantify benefits including consolidated tooling, increased analyst productivity, reduced data engineering overhead, and revenue-generating analytics use cases.
Databricks provides comprehensive compliance capabilities through Unity Catalog, including fine-grained access controls, automated lineage, audit logs, and data classification. Partners configure compliance frameworks for regulations like GDPR, CCPA, HIPAA, and industry standards, ensuring enterprises meet regulatory requirements.
Databricks consulting partners offer managed services including platform monitoring, performance optimization, user support, version upgrades, security patches, and cost management. These services ensure continuous platform health, optimal performance, and that organizations maximize their Databricks investment over time.
Evaluate partners based on certified expertise, proven implementation experience, industry knowledge, client references, and support offerings. Look for partners like Kanerika with comprehensive capabilities spanning data engineering, AI/ML, and analytics, plus demonstrated success delivering measurable business outcomes across enterprise engagements.
