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Hear from Kanerika's Data & AI Experts

Snowflake is no longer just a cloud data warehouse.In this episode of The Digital Shift by Kanerika, Kanishka Goel speaks with Shaurya Singh Chauhan, Lead Engineer at Kanerika, about how Snowflake is changing in 2026 and what that means for enterprise data teams.They discuss Snowflake’s move toward a broader data and AI platform, including CoWork, CoCo, Cortex AI, Snowpark, Iceberg support, AI agents, and enterprise governance.The conversation covers:• What Snowflake is built for today • What changed at Snowflake Summit 2026 • Snowflake CoWork vs CoCo and who each product is for • Whether Snowflake is still primarily a data warehouse • Snowflake vs Databricks vs Microsoft Fabric for SQL, ETL, and data pipelines • Where dbt, Fivetran, and other external tools fit into the Snowflake stack • How mature Snowflake’s AI capabilities are for enterprise use • Cortex AI, AI agents, Horizon, and AI governance • How enterprises can run Snowflake alongside Databricks and Microsoft Fabric • Where Kanerika supports Snowflake strategy, implementation, migration, and data engineeringIf your organization is evaluating Snowflake for analytics, data engineering, AI, or a multi-platform data strategy, this episode gives you a practical view of where the platform stands in 2026.Learn more about Kanerika: https://kanerika.com/#Snowflake #Snowflake2026 #CortexAI #SnowflakeCoWork #SnowflakeCoCo #DataEngineering #DataAnalytics #ArtificialIntelligence #Databricks #MicrosoftFabric #TheDigitalShift #Kanerika 8:36Snowflake in 2026: How a Data Warehouse Became an AI Platform | Podcast ft. Shaurya Chauhan

Snowflake in 2026: How a Data Warehouse Became an AI Platform | Podcast ft. Shaurya Chauhan

Kanerika Inc. August 7th

AI projects often fail for a reason most companies overlook: people don't adopt them.Organizations invest in AI tools, build pilots, and launch new systems, but many struggle when it comes to real-world usage. The challenge is rarely just the technology. It comes down to trust, workflow integration, employee readiness, and whether AI solves a real business problem.In this episode of The Digital Shift by Kanerika, Kanishka Goel speaks with Varun Saket, Product Manager at Kanerika, about what it takes to make enterprise AI adoption successful.In this conversation, we discuss:• Why many enterprise AI projects fail after implementation • How companies should identify the right AI use cases • Why generic AI tools often fall short in enterprise environments • How custom AI solutions fit into business workflows • Managing employee resistance and fear around AI • Why AI adoption is a leadership and change management challenge • How organizations can measure AI success beyond deployment • What successful AI adoption looks like in day-to-day operationsAI success is not measured by launching a model or deploying a tool. It is measured by whether teams trust it, use it, and achieve better business outcomes.Watch the full episode to learn how enterprises can move from AI experimentation to meaningful adoption.Connect with Kanerika: https://kanerika.com/meet#EnterpriseAI #AIAdoption #GenerativeAI #AIAutomation #DigitalTransformation #ArtificialIntelligence #Kanerika 00:00 Introduction 01:01 Identifying AI Use Cases and Implementation Feasibility 03:36 Custom AI Solutions and Workflow Integration 05:50 AI Adoption and Human Oversight 08:48 Employee Resistance and Job Security Concerns 12:33 AI Adoption Beyond One-Time Training 14:46 Measuring AI Adoption and Business Impact 17:19 How Kanerika Turns AI Fear into Confidence 18:58 What Successful AI Adoption Looks Like 21:15 Advice for Leaders Facing AI Adoption Challenges 23:08 Final Thoughts and Advice on AI Adoption 23:57Why Most AI Initiatives Fail: Lessons From Real Enterprise AI Deployments

Why Most AI Initiatives Fail: Lessons From Real Enterprise AI Deployments

Kanerika Inc. July 29th

0:00 Why Data Platform Migrations Fail 1:08 Manisha's Path from Finance to BA 3:32 Selling Business Analysis Before Migration 6:29 Hidden Reports: 200 Becomes 600 8:35 ETL vs BI vs RPA Migrations 11:42 Pharma, Finance, Manufacturing Priorities 13:41 Undocumented 8-Year-Old Reports 15:19 FLIP Accelerator: Speed and Confidence 17:46 FLIP Catches Reconciliation Dependency 19:43 Cost of Delaying Legacy MigrationWhy do some data migration projects meet their goals while others run over budget, miss deadlines, or fail completely?In this episode, Manisha Chowdari, Head of Business Analysis at Kanerika, explains the main factors that decide the outcome of an enterprise data migration. She discusses poor planning, unclear business requirements, weak data quality, limited stakeholder involvement, testing gaps, and the risks of treating migration as a technical task alone.Learn how business analysis, data governance, phased execution, and clear success measures can reduce migration risk and support better results across cloud, data warehouse, and legacy system modernization projects.This episode is useful for CIOs, CTOs, data leaders, business analysts, and teams planning a large-scale data migration.Planning a data migration? Talk to Kanerika’s experts.Book a meeting: https://kanerika.com/contact-us/ 22:23Why Data Platform Migration Succeeds or Fails | Manisha Chowdari, Head - Business Analysis

Why Data Platform Migration Succeeds or Fails | Manisha Chowdari, Head - Business Analysis

Kanerika Inc. July 20th

0:00 Databricks DAIS 2026 Recap 1:36 LTAP: Transactional Meets Analytical Processing 2:36 What Is Databricks Genie AI 3:30 Genie Zero Ops Autonomous Pipelines 4:25 Genie Ontology Living Context Layer 5:50 Unity AI Gateway Governance 7:03 Kanerika Databricks Bronze PartnerIn this episode of The Digital Shift by Kanerika, host Kanishka Goel speaks with Shaurya Singh Chauhan, Lead Engineer at Kanerika, to break down the technologies shaping the next generation of the Databricks platform.From LTAP (Lake Transactional and Analytical Processing) to Genie ZeroOps, Genie Ontology, Unity AI Gateway, and modern AI governance, this episode explains what these updates mean for organizations building production-ready AI systems.In this episode: - Databricks' vision after DAIS 2026 - LTAP and the future of real-time data architecture - Lakebase and Lakehouse//RT explained - Genie Code, Genie ZeroOps, Genie Ontology, and Genie One - AI operations and autonomous pipeline management - Unity AI Gateway and enterprise AI governance - Attribute-Based Access Control (ABAC) and Unity Catalog updates - Databricks adoption strategies for enterprisesWhether you're evaluating Databricks, planning an AI modernization initiative, or looking to understand the latest enterprise AI trends, this episode provides practical insights from engineers working with the platform every day.🎧 Subscribe to The Digital Shift by Kanerika for conversations on AI, data engineering, analytics, Microsoft Fabric, Databricks, Snowflake, and enterprise technology. 8:53What’s New in Databricks After DAIS 2026? LTAP, Genie ZeroOps & AI Governance Explained

What’s New in Databricks After DAIS 2026? LTAP, Genie ZeroOps & AI Governance Explained

Kanerika Inc. July 3rd

0:00 Meet Amit Chandak, Microsoft MVP 0:57 What Are Fabric Data Agents 1:53 Agent Architecture: SQL and DAX Queries 3:37 Business Users Ask Follow-Up Questions 4:27 Dynamic Measures Replace Bloated Semantic Models 6:44 Karl: Kanerika's Advanced Fabric Data Agent 7:31 Governance, Encryption and Row-Level Security 8:27 Copilot vs Fabric Data Agents Difference 10:22 Start With Paid Fabric CapacityIn this episode, @AmitChandak , @Microsoft MVP and CAO at Kanerika, talks about why Fabric Data Agents matter for modern enterprises and how they fit into the larger @MicrosoftFabric AI analytics, and self-service BI shift.What is covered in this episode: ✅ What Fabric Data Agents are and why they matter for modern enterprises ✅ The core business problems Microsoft Fabric Data Agents solve for data teams and business users ✅ How a Fabric Data Agent processes a natural language question and returns a trusted answer ✅ What happens under the hood in a Microsoft Fabric Data Agent setup ✅ Which departments and business functions are seeing the highest ROI from Fabric Data Agents ✅ KARL, Kanerika’s AI-powered data agent, and how it supports enterprise data analytics ✅ How organizations can use governance, security, and data quality controls to prevent sensitive data exposure ✅ The key difference between Copilot in Fabric and Fabric Data AgentsThis episode is useful for CDOs, CIOs, IT leaders, BI teams, data teams, and business leaders exploring Microsoft Fabric Data Agents, enterprise AI, generative AI in analytics, and AI-driven decision-making.#MicrosoftFabric #FabricDataAgents #MicrosoftFabricDataAgents #DataAgents #EnterpriseAnalytics #AIAnalytics #SelfServiceBI #BusinessIntelligence #DataGovernance #DataSecurity #DataQuality #GenerativeAI #EnterpriseAI #DataStrategy #AnalyticsLeadership #Kanerika #AmitChandak #microsoftmvp 11:56AI That Talks to Your Data — Fabric Data Agents | I Amit Chandak, Microsoft MVP | Kanerika's CAO

AI That Talks to Your Data — Fabric Data Agents | I Amit Chandak, Microsoft MVP | Kanerika's CAO

Kanerika Inc. May 29th

0:00 Why AI Agent Projects Fail in Production 2:32 Agent Bricks for Building AI Agents 3:32 Evaluating Agents: Testing, Governance, Model Flexibility 5:22 Databricks Lakebase: Postgres for AI Agents 7:40 Rearchitecting AI Apps With Lakebase 9:16 Unity Catalog for Data Governance 11:20 How Agent Bricks, Lakebase, Unity Catalog Fit Together 12:42 Where to Start: Data Maturity Assessment 13:39 Migrating Legacy Systems to Databricks 15:40 One Line Takeaway on AI in ProductionIn this episode of The Digital Shift by Kanerika, Kanishka Goel speaks with Shaurya Chauhan, Lead Engineer at Kanerika, about the latest capabilities from @Databricks and what they actually mean in practice.They break down how Agent Bricks, Lakebase, and Unity Catalog fit together, why many AI initiatives fail to move beyond pilots, and what changes when you design for real production use cases.🔍 What’s covered - Databricks latest capabilities: Agent Bricks, Lakebase, Unity Catalog - Why most AI agents don’t make it to production -- What changes in architecture when building real AI systems - How governance and data control impact AI success - What to consider when adopting or migrating to DatabricksLooking to adopt Databricks or scale AI in production? Book a meeting with Kanerika to get expert guidance: https://kanerika.com/contact-us/ 16:44What Do Databricks’ New AI Capabilities Mean? ft. Shaurya Chauhan

What Do Databricks’ New AI Capabilities Mean? ft. Shaurya Chauhan

Kanerika Inc. April 15th

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