Apache Iceberg: The Definitive Guide: Data Lakehouse Functionality, Performance, and Scalability on the Data Lake (Paperback)
暫譯: Apache Iceberg：權威指南：數據湖屋的功能、性能與可擴展性
Shiran, Tomer, Hughes, Jason, Merced, Alex
買這商品的人也買了...
-
$918Python and HDF5 (Paperback) -
$301特徵工程入門與實踐 (Feature Engineering Made Easy) -
$301精通特徵工程 (Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists) -
$393深度學習的數學 -
$1,710Learn Algorithmic Trading -
InfluxDB 原理與實戰$534$507 -
Machine Learning for Algorithmic Trading, 2/e (Paperback)$2,380$2,261 -
$1,615Python Algorithmic Trading Cookbook: All the recipes you need to implement your own algorithmic trading strategies in Python -
$1,665Kubeflow for Machine Learning: From Lab to Production -
事件流實戰$588$559 -
MongoDB 技術手冊, 3/e (MongoDB: The Definitive Guide: Powerful and Scalable Data Storage, 3/e)$780$616 -
$1,853Algorithmic Short Selling with Python: Refine your algorithmic trading edge, consistently generate investment ideas, and build a robust long/short pro -
從 OS 等級探究：Redis 運作原理程式逐行講解$880$695 -
$1,710Advanced Python Programming : Accelerate your Python programs using proven techniques and design patterns, 2/e (Paperback) -
$1,862Time Series Analysis with Python Cookbook: Practical recipes for exploratory data analysis, data preparation, forecasting, and model evaluation (Paperback) -
跨數據中心機器學習：賦能多雲智能數算融合$528$502 -
$621使用 GitOps 實現 Kubernetes 的持續部署：模式、流程及工具 -
Elasticsearch 數據搜索與分析實戰$599$569 -
Kafka 實戰$539$512 -
$1,663Practical Machine Learning on Databricks: Seamlessly transition ML models and MLOps on Databricks (Paperback) -
$1,898Deep Learning for Time Series Cookbook: Use PyTorch and Python recipes for forecasting, classification, and anomaly detection (Paperback)
相關主題
商品描述
Traditional data architecture patterns are severely limited. To use these patterns, you have to ETL data into each tool--a cost-prohibitive process for making warehouse features available to all of your data. This lack of flexibility forces you to adjust your workflow to the tool your data is locked in, which creates data silos and data drift. This book shows you a better way.
Apache Iceberg provides the capabilities, performance, scalability, and savings that fulfill the promise of an open data lakehouse. By following the lessons in this book, you'll be able to achieve interactive, batch, machine learning, and streaming analytics with this lakehouse. Authors Tomer Shiran, Jason Hughes, and Alex Merced from Dremio guide you through the process.
With this book, you'll learn:
- The architecture of Apache Iceberg tables
- What happens under the hood when you perform operations on Iceberg tables
- How to further optimize Apache Iceberg tables for maximum performance
- How to use Apache Iceberg with popular data engines such as Apache Spark, Apache Flink, and Dremio Sonar
- How Apache Iceberg can be used in streaming and batch ingestion
Discover why Apache Iceberg is a foundational technology for implementing an open data lakehouse.
商品描述(中文翻譯)
傳統數據架構模式受到嚴重限制。使用這些模式時，您必須將數據進行 ETL（提取、轉換、加載）到每個工具中——這是一個成本高昂的過程，無法讓所有數據都能使用數據倉庫的功能。這種缺乏靈活性迫使您調整工作流程以適應鎖定數據的工具，從而產生數據孤島和數據漂移。本書將向您展示更好的方法。
Apache Iceberg 提供了滿足開放數據湖倉承諾的能力、性能、可擴展性和成本效益。通過遵循本書中的課程，您將能夠在這個湖倉中實現互動式、批量、機器學習和流式分析。來自 Dremio 的作者 Tomer Shiran、Jason Hughes 和 Alex Merced 將指導您完成這一過程。
在本書中，您將學到：
- Apache Iceberg 表的架構
- 當您對 Iceberg 表執行操作時，背後發生了什麼
- 如何進一步優化 Apache Iceberg 表以獲得最佳性能
- 如何將 Apache Iceberg 與流行的數據引擎（如 Apache Spark、Apache Flink 和 Dremio Sonar）一起使用
- 如何在流式和批量攝取中使用 Apache Iceberg
了解為什麼 Apache Iceberg 是實現開放數據湖倉的基礎技術。
