Cloudera, Dell and NVIDIA bring AI On-Prem
Leon Taiman, Global Practice Lead at Dell Technologies, discusses the strategic partnership between Dell, Cloudera, and NVIDIA to accelerate the adoption of Private AI on-premises for customers.
Open Lakehouse Meetup (ft. Apache Iceberg): Building Scalable Data Platforms
Discover the future of the Data Lakehouse with this deep dive into Apache Iceberg V3 and V4 from the Bengaluru community meetup. Learn how PyIceberg and DuckDB are revolutionizing Python-native data processing by eliminating the need for Spark clusters for 99% of common query sizes. Explore high-performance ingestion benchmarks from Oleg and the Google Dataproc Lightning Engine, achieving over 500k rows/sec through Apache Arrow and C++ vectorization. This session is a masterclass for data engineers on metadata compaction, Rest Catalogs, and building vendor-agnostic data platforms.
Security considerations when choosing an embedded analytics provider
October 2025, Paris. Thieves dressed as construction workers break into the Louvre and steal eight pieces of the French Crown Jewels valued at approximately €88 million. The robbery took place in broad daylight, and lasted less than eight minutes. As of the time of writing, only one of the jewels was recovered, a crown belonging to Napoleon III's wife Eugénie, which the thieves dropped in the street as they fled.
The Embedded BI Advantage: Why On-Premises Analytics Beats Third-Party BI for Data Security
Corporate concerns about entrusting sensitive data to third-party platforms keep many CTOs awake at night. Every time data leaves your infrastructure for a bolted-on BI tool, you're gambling with control. The stakes? Regulatory compliance, competitive advantage, and customer trust. Deploying a business analytics solution while keeping data safe and compliant often involves choosing between BI solutions that are either “bolted-on” or “embedded”.
How our product team uses AI to manage customer feedback at scale
Agentic AI enables you to do what you simply couldn’t before, like making sense of and responding to everything ever said about your product.
BigQuery Migration Service: SQL and data transfer
Following your migration assessment, it is time to execute the transfer of your data and SQL queries into Google Cloud. This video dives into the specific tools and services that simplify migrating workloads from Snowflake, Teradata, Cloudera, and Databricks into BigQuery, Dataproc, and Google Cloud Storage.
Avoid Vendor Lock-in With Cloud-Agnostic BI
Many AI analytics platforms force enterprises into an impossible choice: adopt cloud-only solutions that compromise data governance and security policies or forgo AI capabilities entirely. But there’s a significant problem with that: most companies aren’t 100% cloud-based, and those that are vary between whether they operate in the public cloud, private cloud, or a hybrid environment.
How Column Sets and Query Sets Simplify Analytics
When you’re building analytics for users, you quickly realize something: not every definition belongs on the Model. A lot of business logic sits in an awkward middle ground, too context-specific to hardcode into the Model but too important to leave scattered across one-off formulas. And in most tools, if the logic doesn’t live on the Model, every team ends up rebuilding the same thing over and over again. That’s where Query Sets and Column Sets in ThoughtSpot come in.
Do Identity Intelligence Better with Snowflake and Verato
Healthcare organizations can’t achieve true Patient 360 or power accurate AI and analytics when identity data is fragmented across EHRs, EMRs, CRMs, and countless clinical and operational systems. Check out the full video to see how Verato MDM Cloud delivers industry-leading healthcare identity resolution and master data management (MDM) directly inside Snowflake to unify, enrich, and master patient and provider data with unmatched accuracy.
How to Join Parquet & JSON Files in ThoughtSpot Analyst Studio
Stop manually juggling mismatched data formats! This video demonstrates how to join Parquet and JSON files directly within ThoughtSpot Analyst Studio’s Python Notebook to create a single, enriched dataset. What you will see: This is a must-watch for data professionals looking to unify complex, multi-format data sources and deliver searchable, AI-ready insights in one continuous workflow.