Name: Why Customer-Facing Analytics Breaks—and How to Build It Right
Start: 2025-08-15T17:18:00Z
End: 2025-08-15T17:18:58.000Z
Location: BrightTALK

CelerData Cloud is the fastest, secure analytical engine that powers customer-facing and AI-driven analytics at scale, delivering consistently reliable and unbeatable performance with a future-proof architecture—ensuring real-time access to open data without ingestion delays or costly data pipelines.

CelerData is used worldwide by market-leading brands including Coinbase, Pinterest, Demandbase, and Expedia to generate critical new insights for these data-driven companies.

StarRocks 3.5 brings major upgrades across system reliability, ingestion, performance, security, and more. In this video, we’ll break down what’s new, what’s improved, and what they mean for engineers using StarRocks in production.

Introducing StarRocks 3.5

This session offers a practical guide to unlocking the full potential of Apache Iceberg. From Iceberg v3 highlights to essential techniques—vectorized execution, smarter metadata handling, and intelligent caching—watch the video to pick up tips, tricks, and best practices for building a faster, well-governed lakehouse.

Bringing Data Warehouse Performance to Apache Iceberg

In this session, Yingjun Wu (CEO, RisingWave) and Sida Shen (PM, CelerData) will take you through building a high-performance streaming architecture using Apache Iceberg. They'll share practical tips and real-world examples, with a live demo of Nimtable, an Apache-licensed tool to monitor and manage the health of your Iceberg tables. You'll get to see how to track compaction status, metadata issues, layout problems, and more.

Streaming Apache Iceberg Essentials: Mutable data, Small Files, Schema Evolution, And More

Fanatics, a global leader in licensed sports merchandise, serves millions of fans across dozens of sports leagues and brands. Behind the scenes, their data infrastructure supports massive scale and complexity—with over 120K datasets, 10 domains, and billions of events processed daily.

In this session, Bhanu Cherukumille (Director of Data Engineering) and Oleksiy Rezchykov (Staff Engineer) share how they transformed a fragmented, tool-heavy analytics stack into a unified, real-time platform using StarRocks, Apache Iceberg, Superset, and Kafka.

See the steps they took to get results
- Dramatically improved query performance enabling real-time analytics
- Significant cost savings compared to our previous infrastructure
- Increased data adoption across departments, fueling data-driven decisions
- Enhanced data reliability and consistency through our Medallion implementation

Building Cost-effective Analytics Stack at Fanatics: StarRocks, Apache Iceberg & More

Ben Gamble, Field CTO at Ververica (the original creators of Apache Flink), and Sida Shen, Product Manager at CelerData, break down the essentials of real-time stream processing, sharing insights into its benefits, challenges, and practical ways to get started.

Take a closer look at VERA: The Path to Cloud-Native Apache Flink, and how it’s designed to support today’s real-time data needs. The session concludes with a live demo that showcases how CelerData (managed StarRocks) and Ververica (managed Flink) integrate to form a powerful data stack. If you're interested in real-time data processing and its practical applications, this is a perfect place to start!

Stream Processing Must Haves

Petri Zhang from Tencent shares how the team uses StarRocks to power Retrieval-Augmented Generation (RAG) with hybrid search capabilities. He walks through their journey of building a billion-scale image repository that supports both reverse image search and traditional BI dashboards—all on a single, unified platform. Petri also dives into the technical challenges they encountered and explains why Tencent ultimately chose StarRocks over other vector databases.

StarRocks X Tencent - Introducing Vector Similarity Search

Ever feel like managing data lakehouses is a juggling act of flexibility, scalability, and costs? Struggling with slow queries in your data lakehouse? Explore five top lakehouse architectures that show how enhancing query performance can save on compute, streamline your setup, strengthen governance, and speed up development.

5 Brilliant Lakehouse Architectures from Tencent, WeChat, and More

In this deep-dive session, StarRocks contributor Sida Shen explores the most common performance challenges faced by Apache Iceberg users—and how to overcome them. With new native integrations between StarRocks and Iceberg, you can now achieve fast, high-concurrency analytics directly on your data lake—without copying data into a proprietary warehouse.

Apache Iceberg + StarRocks: Your Recipe for Superior Lakehouse Performance

Eric Sun, Head of Data Platform and Datastores at Coinbase, shares how his team is pushing the limits of data lakehouse performance. Alongside Sida Shen from CelerData, he discusses why proprietary data warehouses aren’t the answer for fast queries, highlights the latest innovations in query engines, and walks through how Coinbase uses StarRocks, Delta Lake, and Unity Catalog to power real-time analytics at scale.

[Coinbase Lakehouse Architecture] Achieving Data Warehouse Performance on a Data Lakehouse

This session takes you deep into the technical side of user-behavior analytics—covering best practices, real-world applications, and lessons from industry leaders like Tencent. You’ll also learn how to run advanced user-behavior analytics directly in StarRocks using just SQL, with techniques like count distinct, lambda functions, and funnel/retention analysis. To top it off, we’ll walk you through a live demo using the StarRocks-powered CelerData Cloud to show you exactly how it’s done!

How to Make User Behavior Analytics Work for You

Check out this webinar to learn how today’s data teams are moving beyond complex real-time pipelines. Going pipeline-free is the new best practice—and this session shows you how to build one using open-source tools. You'll hear what companies like Airbnb are doing, and pick up tips to boost performance and cut down on maintenance.

Go Pipeline-Free With Real-Time Analytics

This engineer-led webinar dives into one of StarRocks’ most powerful features: materialized views. Murphy Wang, the technical lead behind the MV engine, shares practical tips, common pitfalls, and real-world use cases to help you get the most out of materialized views—unlocking faster queries and a more efficient data pipeline. 

Whether you're optimizing BI dashboards or accelerating your lakehouse, this session has you covered.

Materialized Views: Tips, Tricks, and Use Cases

Fast-track your understanding of why StarRocks is leading the way in real-time analytics and as a powerful lakehouse query engine. In this session, you’ll uncover why more and more enterprises are choosing StarRocks. We’ll walk through its standout features, showcase real-world use cases, and explore how your organization can take full advantage of what StarRocks offers.

What Is StarRocks: Features and Use Cases

This webinar shows you how to scale user-facing analytics to thousands—or even tens of thousands—of users without sacrificing performance. You'll hear behind-the-scenes stories from StarRocks users, learn how to avoid common implementation pitfalls, and see why StarRocks is purpose-built for real-time, high-concurrency workloads.

How to Develop User Facing Analytics Projects That Scale

Get the full breakdown on why Trino struggles with modern analytics workloads—and the hidden costs behind it. Discover why proprietary query accelerators fall short, how to eliminate complex ingestion pipelines, and how companies like Trip.com are modernizing with data lakehouse solutions.

Trino Vs StarRocks - How to Get Data Warehouse Performance on the Lakehouse

Dive into this webinar to uncover why Apache Druid is falling behind for modern real-time analytics. Learn about the performance and scalability challenges it faces—and which database technologies are better suited for today’s high-concurrency workloads.

Why Apache Druid Can't Handle Modern Real-Time Analytics and What to Do About It

Check out this video to uncover why denormalization is often required in ClickHouse—and the real costs and challenges it brings, especially in real-time analytics. See how StarRocks removes that burden with fast on-the-fly JOINs, and get a clear breakdown of the technical differences to help you choose the right engine. If ClickHouse is holding you back, this webinar shows a better path forward.

From Denormalization to JOINS: Why ClickHouse Can't Keep Up

If you’re serving analytics to customers, you know the pain: slow queries, stale data, and scaling headaches. We’ll show you how teams have solved these problems with StarRocks—powering dashboards, APIs, and AI-driven insights.

Why Customer-Facing Analytics Breaks—and How to Build It Right

Data Architecture

Data Analytics

Data Best Practices

Database Management

Big Data

Big Data Analytics

Big Data Management

Real Time Analytics

Welcome to the big data and data management community on BrightTALK. Join thousands of data quality engineers, data scientists, database administrators and other professionals to find more information about the hottest topics affecting your data. Subscribe now to learn about efficiently storing, optimizing a complex infrastructure, developing governing policies, ensuring data quality and analyzing data to make better informed decisions. Join the conversation by watching live and on-demand webinars and take the opportunity to interact with top experts and thought leaders in the field.

Big Data and Data Management

As an IT professional, many of the problems you face are multifaceted, complex and don’t lend themselves to simple solutions. The information technology community features useful and free information technology resources. Join to browse thousands of videos and webinars on ITIL best practices, IT security strategy and more presented by leading CTOs, CIOs and other technology experts.

Why Customer-Facing Analytics Breaks—and How to Build It Right

Presented by

About this talk

CelerData