Getting Started - Apache Iceberg

Explore how data warehouses centralize analytics, store structured, query-ready data, and support business decisions, while recognizing ETL challenges, maintenance costs, and evolving needs that fuel interest in iceberg.

Explore data lakes as a storage approach that keeps data in its native, unstructured form, highlighting cost benefits, simplified management, downstream challenges, and the hybrid idea of data lake houses.

Explore data lake houses, merging data warehouses and data lakes to store unstructured data with ACID transactions and rich metadata, enabling historical snapshots and scalable, cost-effective data management.

Discover how Iceberg revolutionizes data lakes with atomic transactions, time travel, and snapshots, using manifest files and metadata to ensure data correctness and efficient lakehouse management.

Explore Apache Iceberg core concepts, including metadata management, schema evolution, and partitioning strategies, with snapshot-based versioning and querying for data integrity and efficient access.

Explore how iceberg architecture uses a catalog to reference the metadata, enabling atomic changes and ACID compliance while coordinating metadata, manifest, and snapshot layers for Parquet, ORC, and Avro data.

Discover how Iceberg improves query performance by structuring data for efficient access and loading only relevant segments, while optimizing metadata management and delivering ACID transactions in cloud environments.

Learn how to create an Apache Iceberg table named aircraft in Amazon S3, with the initial metadata snapshot S0 and a manifest list, enabling schema evolution and state management.

insert records into an Iceberg table in S3, creating a parquet data file, updating manifests and metadata, and establishing snapshots S0 and S1 for versioned cloud storage.

Explore how Apache Iceberg integrates with Spark, Flink, Presto, and Trino, using Spark catalog and Spark session catalog, with external catalogs like Hive or Hadoop, for high performance data management.

Explore how Apache Iceberg enhances data consistency and accuracy in data lakes, addressing acid limitations, integrating with Amazon S3, Google Cloud Storage, and Azure Blob Storage, enabling analytics and queries.

Install and verify Docker to accelerate container application development, explore the Docker dashboard, run and test containers and images, and use Docker Compose for multi-container setups.

Install the four commands to connect Dremio with Apache Iceberg on your laptop, then launch a notebook with Docker Compose and practice creating databases, tables, and Iceberg SQL queries.

Install four containers with docker compose up for notebook, mu, menu, and setup; access Jupyter notebook via a token URL and manage buckets and a warehouse in the dream console.

Configure a Nessie source in Dremio to access Apache Iceberg, entering endpoint, authentication, and three properties, then save with the warehouse path to enable SQL queries.

Launch sql queries in Dremio on Apache Iceberg by creating tables, partitioning by last name initial, inserting data, viewing metadata and snapshot, and evolving partitions with alter table.

Master branching and merging in Apache Iceberg by creating and switching to a new ingest branch, inserting records there, and merging changes into the main branch.

upload a csv, auto-detect delimiter and header, extract column names, name the data table, and create an integrated iceberg table ready for querying.

Subscribe to my new YouTube channel for daily free videos on various topics, and thank you for your loyalty; see you soon.