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Data Engineering with Python

You're reading from   Data Engineering with Python Work with massive datasets to design data models and automate data pipelines using Python

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Product type Paperback
Published in Oct 2020
Publisher Packt
ISBN-13 9781839214189
Length 356 pages
Edition 1st Edition
Languages
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Author (1):
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Paul Crickard Paul Crickard
Author Profile Icon Paul Crickard
Paul Crickard
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Toc

Table of Contents (21) Chapters Close

Preface 1. Section 1: Building Data Pipelines – Extract Transform, and Load
2. Chapter 1: What is Data Engineering? FREE CHAPTER 3. Chapter 2: Building Our Data Engineering Infrastructure 4. Chapter 3: Reading and Writing Files 5. Chapter 4: Working with Databases 6. Chapter 5: Cleaning, Transforming, and Enriching Data 7. Chapter 6: Building a 311 Data Pipeline 8. Section 2:Deploying Data Pipelines in Production
9. Chapter 7: Features of a Production Pipeline 10. Chapter 8: Version Control with the NiFi Registry 11. Chapter 9: Monitoring Data Pipelines 12. Chapter 10: Deploying Data Pipelines 13. Chapter 11: Building a Production Data Pipeline 14. Section 3:Beyond Batch – Building Real-Time Data Pipelines
15. Chapter 12: Building a Kafka Cluster 16. Chapter 13: Streaming Data with Apache Kafka 17. Chapter 14: Data Processing with Apache Spark 18. Chapter 15: Real-Time Edge Data with MiNiFi, Kafka, and Spark 19. Other Books You May Enjoy Appendix

Installing and configuring Elasticsearch

Elasticsearch is a search engine. In this book, you will use it as a NoSQL database. You will move data both to and from Elasticsearch to other locations. To download Elasticsearch, take the following steps:

  1. Use curl to download the files, as shown:
    curl https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.6.0-darwin-x86_64.tar.gz --output elasticsearch.tar.gz
  2. Extract the files using the following command:
    tar xvzf elasticsearch.tar.gz
  3. You can edit the config/elasticsearch.yml file to name your node and cluster. Later in this book, you will set up an Elasticsearch cluster with multiple nodes. For now, I have changed the following properties:
    cluster.name: DataEngineeringWithPython 
    node.name: OnlyNode
  4. Now, you can start Elasticsearch. To start Elasticsearch, run the following:
    bin/elasticsearch
  5. Once Elasticsearch has started, you can see the results at http://localhost:9200. You should see the following output...
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