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Practical Enterprise Data Lake Insights : handle data-driven challenges in an Enterprise Big Data Lake /
Use this practical guide to successfully handle the challenges encountered when designing an enterprise data lake and learn industry best practices to resolve issues. When designing an enterprise data lake you often hit a roadblock when you must leave the comfort of the relational world and learn th...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
[Berkeley, CA] :
Apress,
2018.
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
Tabla de Contenidos:
- Intro; Table of Contents; About the Authors; About the Technical Reviewer; Acknowledgments; Foreword; Chapter 1: Introduction to Enterprise Data Lakes; Data explosion: the beginning; Big data ecosystem; Hadoop and MapReduce
- Early days; Evolution of Hadoop; History of Data Lake; Data Lake: the concept; Data lake architecture; Why Data Lake?; Data Lake Characteristics; Data lake vs. Data warehouse; How to achieve success with Data Lake?; Data governance and data operations; Data democratization with data lake; Fast Data
- Life beyond Big Data; Conclusion.
- Chapter 2: Data lake ingestion strategiesWhat is data ingestion?; Understand the data sources; Structured vs. Semi-structured vs. Unstructured data; Data ingestion framework parameters; ETL vs. ELT; Big Data Integration with Data Lake; Hadoop Distributed File System (HDFS); Copy files directly into HDFS; Batched data ingestion; Challenges and design considerations; Design considerations; Commercial ETL tools; Real-time ingestion; CDC design considerations; Example of CDC pipeline: Databus, LinkedIn's open-source solution; Apache Sqoop; Sqoop 1; Sqoop 2; How Sqoop works?
- Sqoop design considerationsNative ingestion utilities; Oracle copyToBDA; Greenplum gphdfs utility; Data transfer from Greenplum to using gpfdist; Ingest unstructured data into Hadoop; Apache Flume; Tiered architecture for convergent flow of events; Features and design considerations; Conclusion; Chapter 3: Capture Streaming Data with Change-Data-Capture; Change Data Capture Concepts; Strategies for Data Capture; Retention and Replay; Retention Period; Types of CDC; Incremental; Bulk; Hybrid; CDC
- Trade-offs; CDC Tools; Challenges; Downstream Propagation; Use Case.
- Centralization of Change DataAnalyzing a Centralized Data Store; Metadata: Data about Data; Structure of Data; Privacy/Sensitivity Information; Special Fields; Data Formats; Delimited Format; Avro File Format; Consumption and Checkpointing; Simple Checkpoint Mechanism; Parallelism; Merging and Consolidation; Design Considerations for Merge and Consolidate; Data Quality; Challenges; Design Aspects; Operational Aspects; Publishing to Kafka; Schema and Data; Sample Schema; Schema Repository; Multiple Topics and Partitioning; Sizing and Scaling; Tools; Conclusion.
- Chapter 4: Data Processing Strategies in Data LakesMapReduce Processing Framework; Motivation: Why MapReduce?; MapReduce V1 Refresher and Design Considerations; Yet Another Resource Negotiator
- YARN; YARN concepts; Hive; Hive
- Quick Refresher; Hive Components; Hive Metastore (a.k.a. HCatalog); Hive
- Design Considerations; Hive LLAP; Apache Pig; Pig Execution Architecture; Apache Spark; Why Spark?; Resilient Distributed Datasets (RDD); RDD Runtime Components; RDD Composition; Datasets and DataFrames; Bucketing, Sorting, and Partitioning; Deployment Modes of Spark Application.