In this episode of The Digital Shift, we explore Databricks Genie Code, an agentic AI system that automates data engineering, ML workflows, pipeline debugging, and dashboard creation inside the Databricks workspace.

We cover how it uses Unity Catalog for governed, context-aware execution, along with its benefits, limitations, and upcoming usage-based pricing changes.

Want to bring agentic AI into your data workflows?

Connect with Kanerika: https://kanerika.com/contact-us/?utm_source=Podbean&utm_medium=social&utm_campaign=lead

In this episode of The Digital Shift, we compare Databricks and Snowflake in 2026 and how both are evolving for enterprise AI.

We cover Databricks’ strength in lakehouse, ML, and engineering, and Snowflake’s leadership in SQL analytics and warehousing, along with their latest AI capabilities.

Choosing between them depends on skills, cost, and AI goals.

Looking to build the right data platform strategy? Connect with Kanerika: https://kanerika.com/contact-us/?utm_source=Podbean&utm_medium=social&utm_campaign=lead

In this episode, we compare Snowflake and Amazon Redshift to help data leaders choose the right data warehouse platform.

We cover performance, cost, scalability, cloud fit, and workload needs so teams can make a smarter platform decision.

Book a free consultation with Kanerika and find the best-fit path for your data platform: https://kanerika.com/contact-us/?utm_source=Podbean&utm_medium=social&utm_campaign=lead

In this episode of The Digital Shift, we discuss updates shared at the Databricks Data + AI Summit 2026, including LTAP, Lakehouse//RT, OpenSharing, Genie One, CustomerLake, and the Panther acquisition.

We also discuss how these updates point to a single governed lakehouse for AI, analytics, and enterprise workloads.

Want to simplify and modernize your Databricks data and AI stack?

In this episode of The Digital Shift, we discuss the top data engineering companies for 2026 and what enterprises should check before choosing a partner.

We cover how data engineering supports AI, analytics, cloud migration, ETL automation, governance, and real-time insights.

Want to build AI-ready data systems? 

Connect with Kanerika: https://kanerika.com/contact-us/?utm_source=Podbean&utm_medium=social&utm_campaign=lead

In this episode of The Digital Shift, we discuss why enterprises are moving from Alteryx to Microsoft Fabric to reduce licensing costs, improve performance, and bring data engineering, analytics, and AI into one platform.

We also cover how Kanerika’s FLIP accelerator helps automate workflow conversion, reduce manual effort, and speed up migration from legacy Alteryx assets to the Microsoft ecosystem.

Want to move from Alteryx to Microsoft Fabric with less risk and faster execution?

Connect with Kanerika: https://kanerika.com/contact-us/?utm_source=Podbean&utm_medium=social&utm_campaign=lead

In this episode of The Digital Shift, we discuss how Edge AI processes data on devices like sensors, cameras, and machines instead of sending everything to the cloud.

We cover how it supports faster decisions, stronger privacy, lower latency, and more reliable operations across manufacturing, retail, automotive, and industrial workflows.

Curious how Edge AI can make your enterprise smarter and faster?

Connect with Kanerika: https://kanerika.com/contact-us/?utm_source=Podbean&utm_medium=social&utm_campaign=lead

In this episode of The Digital Shift, we discuss how multimodal AI tools are helping enterprises work with text, images, audio, and documents in one system. 

We cover the top models for 2026, key business use cases, and what companies need to manage before scaling multimodal AI. 

Want to build enterprise AI solutions that deliver measurable business value? 

Book a free meeting today: 

In this episode of The Digital Shift, we discuss how LLM gateways help enterprises move AI from prototypes to secure production systems.

We cover how gateways sit between internal apps and AI model providers to control access, manage token costs, route requests, redact PII, and reduce provider risk.

We also discuss why BFSI and healthcare teams need stronger AI security, private deployment, semantic caching, and centralized governance for agentic AI systems.

Want to make enterprise AI more secure, controlled, and production-ready?

Book a free meeting today:

In this episode of The Digital Shift, we discuss how data augmentation helps AI teams improve model accuracy without collecting large amounts of new data.

We cover how controlled changes to images, text, and tabular data improve dataset variety, reduce overfitting, and fix class imbalance.

The episode also explains online vs. offline augmentation, key use cases in healthcare, manufacturing, and finance, and risks such as label errors and data bias.

Want to improve AI model accuracy without collecting more data?

Book a free meeting today: