Systems | Development | Analytics | API | Testing

Customer Data Ingestion at Scale for B2B Platforms

Customer data ingestion is the process of collecting customer records from CRM, ERP, product, support, and file-based sources, validating them, and routing them into the systems that power onboarding, reporting, and activation. For B2B platforms, a good approach is a tenant-safe pipeline that can land history, sync ongoing changes, and deliver trusted records quickly.

Simplifying Modernization with Flexible Acquisition Options

Modern infrastructure transformation should accelerate innovation — not add complexity. As organizations modernize to support mission-critical workloads, hybrid architectures, AI data activation, and third-party environments, they need flexibility, visibility, and trust. That’s why Hitachi Vantara is simplifying infrastructure acquisition and management by delivering an outcome-driven experience for the data center.

Tableau's Cloud-Only Future: What Embedded Analytics Teams Need to Know

Tableau's direction is clear. For embedded analytics teams serving customers with strict governance, data residency, or infrastructure requirements, "cloud only" constrains your product, your market, and your roadmap. In this video, we break down what Tableau Next's Hyperforce launch actually means for ISVs and SaaS vendors building embedded analytics, including: If you're doing real long-range planning, this is the conversation that matters.

How to Add Your First Streaming Transformation with Flink

A streaming transformation is a continuous operation that processes events as they arrive, applies logic in real time, and emits transformed results immediately—without waiting for batch jobs to complete. In Apache Flink, a streaming transformation runs continuously, reacting to each event from a stream. This enables real-time data transformation directly on live data.

Escape the MAR-Tricks! Choose the red pill.

The very system designed to keep you comfortable is the same one keeping you trapped. You pay only for rows that change. Simple, right? But the reality is quite different. MAR mechanics are designed to blow up when you scale. You expect to be paying for real data changes but you end up paying for the whole row, regardless of how little data you move on it. It lures you in with simplicity but the underlying mechanics is an alternate reality. Every connection follows its own pricing curve, the costs stop behaving logically, and forecasting your bill turns into a nightmare.

Zscaler Revolutionizes Cybersecurity Data with Snowflake

Zscaler's Tiffany Blakeney shares how her team replaced fragmented tools and months-long development cycles with Snowflake's all-in-one AI platform. By consolidating all data, APIs, and AI models in one secure platform, Zscaler reduced campaign creation from months to minutes—and more importantly, gained the trustworthy, governed AI foundation a cybersecurity company demands. Learn how they're using Snowflake's integrated AI capabilities to move from POC to production faster than ever while maintaining the security posture critical to their industry.

Integrating RAG and GenAI into Customer 360 Architecture

Traditional Customer 360 architectures were perfectly adequate for the era of quarterly reports and static marketing segments. They successfully pooled data from CRMs, transaction logs, and support platforms to build a unified profile. But for GenAI-powered applications? Yesterday's architecture is a massive bottleneck. Here is why legacy systems are breaking down under the demands of modern AI, and how the architecture is forcing a shift to real-time data.

Confluent Cloud: Making an Apache Kafka Service 10x Better

People often imagine that to provide a cloud service for a piece of open source software is a simple matter of packaging up the open source and putting it in Kubernetes. We knew when we set out to build Confluent Cloud that a true cloud-native offering of Apache Kafka as a service would be much, much more than that.

AlloyDB Lakehouse Federation: Unified access to BigQuery and Google Cloud Lakehouse

Join Paul Ramsey, Product Manager at Google, for a demonstration of AlloyDB’s new Lakehouse Federation capability. Using a fictional financial services firm, Cymbal Investments, we show how analysts can research S&P 500 trends by combining real-time vector search with data in BigQuery and Google Cloud Lakehouse. In this video, you will see: Learn how AlloyDB enables cloud and AI transformation for your data platform.

How to Scale Paid Media Across 5 Channels Without Losing Visibility (Google, Meta, LinkedIn, TikTok)

Agencies hit the same wall every time they try to grow: who is going to actually run the campaigns, and how do you keep visibility across every client and every channel when you do? Ashish Chaturvedi, data analyst of Atidiv, walks through how Atidiv and Databox solve both sides of the problem. Atidiv handles campaign execution across Google Ads, Meta, LinkedIn, TikTok, and email. Databox gives you the visibility layer: one interactive view where you can see spend, revenue, and return across every channel without chasing updates in Slack, email, or spreadsheets.