Elasticsearch 7 and Elastic Stack teaches you to search, analyze, and visualize big data on a cluster with Elasticsearch, Logstash, Beats, Kibana, and more. Fuzzy searches, partial matches, search-as-you-type, pagination, sorting—this course covers them all. You’ll even learn how to take Elasticsearch beyond web search, using Elasticsearch as an alternative to Hadoop and Spark to aggregate and graph Petabytes of data in a matter of milliseconds. Fully updated and upgraded to Elasticsearch 7, this essential liveCourse adds highly marketable skills to your toolbox!
Distributed by Manning Publications
This course was created independently by big data expert Frank Kane and is distributed by Manning through our exclusive liveVideo platform.
About the subject
Elasticsearch 7 is a powerful tool for powering search, capable of searching and indexing huge amounts of unstructured data—but there are more uses for Elasticsearch than websites. Increasingly, Elasticsearch is being used as a real-time alternative to Hadoop and Spark for analysing big data sets. Elasticsearch’s ability to aggregate and graph Petabytes of data in a matter of milliseconds has made it a valuable skill to have in today's job market.
About the video
Frank Kane brings his decade of experience at Amazon.com and IMBD.com to teach you Elasticsearch 7, from installation to operations. You’ll learn what's new in Elasticsearch 7, including tools for managing security with the Elastic stack, and cover the often-overlooked problem of importing data into an Elasticsearch index. You’ll discover that Elasticsearch isn't just for search anymore, learning to use its powerful aggregation capabilities for structured data, including bucketing and analyzing data, and visualizing it using the Kibana web UI. Fuzzy searches, partial matches, search-as-you-type, pagination, sorting—this course covers them all. And it's not just theory! Every lesson has hands-on examples where you'll practice each skill using a virtual machine running Elasticsearch on your own PC.
Term Frequency / Inverse Document Frequency (TF/IDF)
What’s new in Elasticsearch 7
How Elasticsearch scales
Quiz: Elasticsearch concepts and architecture
Mapping and indexing data
Connecting to your cluster
Introducing the MovieLens data set
Importing a single movie via JSON / REST
Insert many movies at once with Bulk API
Updating data in Elasticsearch
Deleting data in Elasticsearch
[Exercise} Insert, update and delete a movie.
Dealing with concurrency
Using analyzers and tokenizers
Data modeling and parent/child relationships Part 1
Data modeling and parent/child relationships Part 2
Searching with Elasticsearch
"Query Lite" interface
JSON search in-depth
[Exercise] Querying in different ways
More with filters
[Exercise] Using filters
Query-time search as you type
N-Grams Part 1
N-Grams Part 2
Importing data into your index - big or small
Importing data with a script
Importing with client libraries
[Exercise] Importing with a script
Logstash and MySQL, Part 1
Logstash and MySQL Part 2
Logstash and S#
Elasticsearch and Kafka Part 1
Elasticsearch and Kafka Part 2
Elasticsearch and Apache Spark Part 1
Elasticsearch and Apache Spark Part 2
[Exercise] Importing data with Spark
Aggregations, buckets and metrics
[Exercise] Generating histogram data
Nested aggregations Part 1
Nested aggregations Part 2
Playing with Kibana
[Exercise] Exploring data with Kibana
Analyzing log data with Elastic Stack
FileBeat and the Elastic Stack architecture
Analyzing logs with Kibana dashboards
[Exercise] Log analysis with Kibana
Elasticsearch operations and SQL support
Choosing the right number of shards
Adding indices as a scaling strategy
Index aliast rotation
Index lifecycle management
Choosing you cluster’s hardware
Failover in action Part 1
Failover in action Part 2
Elasticsearch in the cloud
Amazon Elasticsearch service Part 1
Amazon Elasticsearch service Part 2
The Elastic cloud
You made it!
For tech-minded individuals familiar with web services and REST. Some exposure to Linux and JSON-formatted data would also be beneficial.
What you will learn
Install and configure Elasticsearch 7 on a cluster
Search full-text and structured data in different ways
Integrate Elasticsearch with Spark, Kafka, relational databases, S3, and other systems
Manage operations on production Elasticsearch clusters
Frank Kane holds 17 issued patents in the fields of distributed computing, data mining, and machine learning. He spent 9 years at Amazon and IMDb, developing and managing the technology that automatically delivers product and movie recommendations to millions of customers every day. Sundog Software, his own company specializing in virtual reality environment technology and teaching others about big data analysis, is his pride and joy.
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