OLake by Datazip invites you to their event

ClickHouse + Apache Iceberg: Read & Write Lakehouse Tables

About this event

Webinar Overview

As ClickHouse crosses a major milestone with experimental write support to Apache Iceberg tables (v24.7+), we're entering a new era where ClickHouse can serve as both a hot and cold data engine. This technical session explores the implications of this breakthrough for modern data lakehouse architectures.

Agenda

  1. The Lakehouse Revolution: Why Write Support Matters
  2. ClickHouse 24.7+ Write Capabilities Deep Dive
  3. Core Features, Support Operations, Current Limitations
  4. Technical Architecture Patterns:
  5. Streaming Ingestion Patterns
  6. Data Preprocessing Patterns
  7. Hybrid Analytics Pattern
  8. Cross Engine Compatibility
  9. Read Ecosystem and Advanced Features
  10. Real-World Lakehouse Use case

Who Should Attend

  1. Data Engineers working with real-time analytics and data lake architectures
  2. Platform Engineers evaluating ClickHouse for lakehouse implementations
  3. Analytics Engineers interested in unified query engines for hot/cold data
  4. Database Administrators managing ClickHouse clusters and exploring Iceberg integration
  5. Architects designing modern data platforms with open table formats

Key Takeaways

By the end of this session, attendees will understand:

  1. How to leverage ClickHouse's experimental Iceberg write support in production-ready scenarios
  2. Technical considerations for implementing unified lakehouse architectures
  3. Performance characteristics and limitations of the current implementation
  4. Integration patterns with existing data infrastructure and tooling
  5. Roadmap insights for future ClickHouse lakehouse capabilities


Hosted by

  • Team member
    T
    Sandeep Devarapalli Co-founder and CEO @ Datazip, Inc.

  • Guest speaker
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    Saurabh Ojha MTS 2 @ Nutanix

    Saurabh brings hands-on experience with lakehouse integrations and database internals. He recently presented on "Hacking Iceberg into Traditional Databases" at Lakehouse Days, demonstrating deep technical knowledge of how Apache Iceberg integrates with both PostgreSQL and ClickHouse. Technical Expertise: 1. PostgreSQL internals and custom Table Access Methods (TAM) 2. ClickHouse native Iceberg support and DataLakeCatalog implementations 3. Experience with pg_mooncake and lakehouse-native analytical engines 4. Background in algorithms, optimization, and distributed systems

  • Team member
    T
    Akshay Sharma DevRel @ Datazip

    Developer Advocate at Datazip, helping engineers and contributors adopt open lakehouse technologies. I manage our contributor community and showcase how OLake delivers the fastest data replication framework to teams building at scale.

  • Team member
    T
    Harsha Kalbalia GTM @ Datazip | Founding Member @ Datazip

    Harsha is a user-first GTM specialist at Datazip, transforming early-stage startups from zero to one. With a knack for technical market strategy and a startup enthusiast's mindset, she bridges the gap between innovative solutions and meaningful market adoption.

  • Guest speaker
    G
    Shiv Jha Staff Engineer @ Nutanix

    Shivji Kumar Jha is a Staff Engineer at Nutanix specializing in distributed data platforms. As the founding engineer of Nutanix's data platform team, Shiv has extensive experience with Apache systems including Pulsar, Druid, and ClickHouse. He's a recognized speaker with 30+ talks at Apache and CNCF conferences and actively contributes to open-source database communities. Technical Background: 1. Deep expertise in ClickHouse deployment and optimization at scale 2. Apache Pulsar committer and contributor to multiple database projects 3. Regular contributor to MySQL, with published work on replication internals 4. Experience running large-scale analytical workloads on cloud-native infrastructure

OLake by Datazip

Fastest way to replicate your data to Apache Iceberg.

OLake is an open-source data ingestion tool available on GitHub, developed by Datazip, Inc. Its primary function is to replicate data from transactional databases and streaming platforms (like PostgreSQL, MySQL, MongoDB, Oracle, and Kafka) into open data lakehouse formats, like Apache Iceberg.