Solution · 03
Apache Iceberg テーブル
オープンでスケーラブル、ハイパフォーマンスなデータレイクアーキテクチャ。
効率的なストレージ、バージョニング、クエリ性能を備えたモダンなデータレイクハウス機能を実現します。
METADATA
PARQUET
SNAPSHOT
MANIFEST
Open
Apache Iceberg native format
1
Engine for lake + lakehouse + ops
0
Lock-in to a query vendor
PB+
Scale tested in production
Why this matters
複雑さなしのレイクハウス性能
Apache Iceberg テーブルは大規模データレイクに構造と信頼性をもたらします。MonkDB はネイティブかつリアルタイムな統合でこれを強化します。
大規模データレイクに構造をもたらします
What you get
What MonkDB makes possible for apache iceberg テーブル
0101 / 04
Efficient handling of large analytical datasets
Petabyte-scale tables with high-performance scans.
0202 / 04
Schema evolution and version control
Safe schema changes and time travel built into the table format.
0303 / 04
High-performance querying across massive data volumes
Predicate pushdown, partition pruning, and vectorized execution.
0404 / 04
Seamless integration with real-time data
Iceberg tables coexist with streaming and operational data in one engine.
How it works
Three steps, one continuous loop
STORE
1Open lakehouse, no movement
Data lives in your object store as Iceberg tables. MonkDB reads it natively.
EVOLVE
2Schema and partition evolution
Time-travel, version history, and zero-downtime schema changes are first-class.
SERVE
3Query alongside live state
Historical lake data joins live tables in one SQL surface. No federation hop.
We kept our existing lake and added MonkDB on top. The same Iceberg tables now serve operational queries and analytics from one engine.
Outcome in numbers
- 0Data migration required
- 5×Faster lake queries
- PBProduction scale
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