> For the complete documentation index, see [llms.txt](https://upsolver.gitbook.io/content/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://upsolver.gitbook.io/content/resources/community.md). # Community ## Upsolver Community * Connect with [Upsolver](https://www.linkedin.com/company/upsolver/) on LinkedIn. * Watch our data ingestion videos on [YouTube](https://www.youtube.com/@upsolver8921). * Follow us on Twitter [@Upsolver](https://twitter.com/upsolver). * Engage with [Upsolver](https://www.facebook.com/upsolver/) on Facebook. * Post your questions on the [Upsolver Community](https://upsolvercommunity.slack.com/) Slack channel. *** ## Apache Iceberg Community Newsletter Stay up-to-date with the latest industry news, articles, events, videos, podcasts, and more. Get the community newsletter delivered straight to your inbox every two weeks when you [sign up here](https://chilldatasummit.substack.com/). *** ## Online Events ### :calendar: **November 2024** [Advanced Concepts in Iceberg Table Design](https://www.upsolver.com/resources/webinars/iceberg-table-design) **Live Webinar | Nov 20th | 10am PT / 1pm ET / 5pm GMT** Designing efficient Iceberg tables involves key decisions about partitioning, sorting, and retention to optimize query speed, ingestion latency, and storage costs. These have traditionally required data engineering know-how and expertise to implement and maintain as the number of tables increases and query patterns evolve. In particular to optimal performance are the careful adjustments required to manage high-cardinality columns, data skew, and value density. These factors directly impact read and write efficiency, where even small adjustments can drive significant gains in performance and storage reduction. In this session, we’ll dive into advanced strategies for Iceberg table partitioning and sorting, concluding with an introduction to Upsolver’s Adaptive Clustering – a dynamic solution for table partitioning. **What You’ll Learn:** * Challenges with current approaches to partitioning, sorting, and clustering * Performance and cost impacts of high cardinality and skewed data * Drawbacks of common, best practice, partitioning approaches * How Apache Iceberg improves on these common best practices * How Adaptive Clustering solves these challenges by automating table layout decisions *** ### Event Replays * [From Blueprint to Success: Planning Your Iceberg Lakehouse Project](https://www.upsolver.com/resources/webinars/planning-iceberg-project) * [Bridging the Gap: Building Data Pipelines as a BI Leader](https://www.upsolver.com/resources/webinars/data-business-intelligence) * [Replicating Application Data from PostgreSQL to Iceberg Lakehouse](https://www.upsolver.com/resources/webinars/postgresql-to-iceberg) * [Deep Dive into CDC with Iceberg - Workshop Series](https://www.upsolver.com/resources/webinars/iceberg-cdc?utm_source=documentation\&utm_medium=resources\&utm_campaign=community) * [Getting Started with Snowflake Polaris and Iceberg Tables](https://www.upsolver.com/resources/webinars/snowflake-polaris) * [A Lakehouse future without Tabular. What does it mean for you?](https://www.upsolver.com/resources/webinars/tabular-experts-panel) * [How to Choose a Catalog for Your Iceberg Lakehouse](https://www.upsolver.com/resources/webinars/iceberg-catalogs) * [Iceberg Performance Benchmark Comparison](https://www.upsolver.com/resources/webinars/iceberg-benchmark) * [Migrating Hive Tables to Iceberg - A Hands-on Walkthrough](https://www.upsolver.com/resources/webinars/hive-to-iceberg) * [Data Engineering Architecture: Optimizing For Cost Efficiency](https://www.upsolver.com/resources/webinars/optimizing-cost-efficiency) * [How to Build and Query Your First Iceberg Lakehouse on AWS: Hands-on Tutorial](https://www.upsolver.com/resources/webinars/iceberg-lakehouse-aws) * [Building Iceberg Lakehouse with Spark and Upsolver: Technical Deep Dive](https://www.upsolver.com/resources/webinars/iceberg-spark-upsolver) * [Product Insights at Scale: ZeroETL ingestion from PostgreSQL to Iceberg Lakehouse](https://www.upsolver.com/resources/webinars/postgres-to-iceberg) * [Lakehouse vs. Data Lake: Ideal Uses Cases and Architectural Considerations](https://www.upsolver.com/resources/webinars/lakehouse-vs-data-lake) * [Data Lake to Snowflake Migration: A Hands-On Walkthrough](https://www.upsolver.com/resources/webinars/quality-data-ingestion-in-60) --- # Agent Instructions This documentation is published with GitBook. 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