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Real-Time Log Analytics using Amazon Kinesis and Amazon Elasticsearch Service - June 2017 AWS Online Tech Talks

Amazon Web Services
Amazon Web Services
Amazon Web ServicesAmazon Web Services

Learning Objectives: - Understand how to easily build an end to end, real time log analytics solution - Get an overview of collecting and processing data in real-time using Amazon Kinesis - Learn how to Interactively query and visualize your log data using Amazon Elasticsearch Service Log analytics is a common big data use case that allows you to analyze log data from websites, mobile devices, servers, sensors, and more for a wide variety of applications such as digital marketing, application monitoring, fraud detection, ad tech, gaming, and IoT. Moving your log analytics to real time can speed up your time to information allowing you to get insights in seconds or minutes instead of hours or days. In this session, you will learn how to ingest and deliver logs with no infrastructure using Amazon Kinesis Firehose. We will show how Amazon Kinesis Analytics can be used to process log data in real time to build responsive analytics. Finally, we will show how to use Amazon Elasticsearch Service to interactively query and visualize your log data.

Real-Time Log Analytics using Amazon Kinesis and Amazon Elasticsearch Service - June 2017 AWS Online Tech Talks

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© 2015, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Ray Zhu, Sr. Product Manager, Amazon Kinesis
6/13/2017
Real-Time Log Analytics using
Amazon Kinesis and Amazon
Elasticsearch Service
Agenda
• Amazon Kinesis
• Amazon Elasticsearch Service
• Real-time Log Analytics
o Step 1 Create Amazon Elasticsearch Cluster
o Step 2 Set Up Firehose Delivery Stream
o Step 3 Send Data to Firehose Delivery Stream
o Step 4 Analyze the Data from Amazon Elasticsearch Service
o Step 5 Monitor Log Ingestion Pipeline
Amazon Kinesis
Amazon Kinesis makes it easy to collect, process,
and analyze real-time, streaming data so you can
get timely insights and react quickly to new
information.
Kinesis Streams
Stores data as a
continuous replayable
stream for custom
applications
Kinesis Firehose
Load streaming data into
Amazon S3, Amazon
Redshift, and Amazon
Elasticsearch Service
Kinesis Analytics
Analyze data streams
using standard SQL
queries
Amazon Kinesis
Kinesis Firehose Benefits
Amazon Elasticsearch Service
A cost-effective managed service that makes it easy to
deploy, manage, and scale open source Elasticsearch for
log analytics, full-text search and more.
Ad

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Real-Time Log Analytics using Amazon Kinesis and Amazon Elasticsearch Service - June 2017 AWS Online Tech Talks

  • 1. © 2015, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Ray Zhu, Sr. Product Manager, Amazon Kinesis 6/13/2017 Real-Time Log Analytics using Amazon Kinesis and Amazon Elasticsearch Service
  • 2. Agenda • Amazon Kinesis • Amazon Elasticsearch Service • Real-time Log Analytics o Step 1 Create Amazon Elasticsearch Cluster o Step 2 Set Up Firehose Delivery Stream o Step 3 Send Data to Firehose Delivery Stream o Step 4 Analyze the Data from Amazon Elasticsearch Service o Step 5 Monitor Log Ingestion Pipeline
  • 3. Amazon Kinesis Amazon Kinesis makes it easy to collect, process, and analyze real-time, streaming data so you can get timely insights and react quickly to new information.
  • 4. Kinesis Streams Stores data as a continuous replayable stream for custom applications Kinesis Firehose Load streaming data into Amazon S3, Amazon Redshift, and Amazon Elasticsearch Service Kinesis Analytics Analyze data streams using standard SQL queries Amazon Kinesis
  • 6. Amazon Elasticsearch Service A cost-effective managed service that makes it easy to deploy, manage, and scale open source Elasticsearch for log analytics, full-text search and more.
  • 8. Time Value of Money Data
  • 9. Real-Time Log Pipeline Elasticsearch cluster backup S3 bucket source records data source source records Firehose delivery stream transformed records delivery failure transformation failure
  • 10. Step 1 Create Amazon Elasticsearch Cluster
  • 14. Step 2 Set Up Firehose Delivery Stream
  • 18. Step 3 Send Data to Firehose Delivery Stream
  • 19. Sample Log Data 219.134.32.117 - - [16/Feb/2017:09:38:20 -0800] "GET /wp-content HTTP/1.1" 200 4521 "-" "Mozilla/5.0 (compatible; MSIE 10.0; Windows NT 6.1; Trident/5.1; .NET CLR 3.8.23015.5)“ 95.169.41.62 - - [16/Feb/2017:09:38:20 -0800] "PUT /app/main/posts HTTP/1.1" 200 3883 "-" "Mozilla/5.0 (Windows NT 6.2; Trident/7.0; rv:11.0) like Gecko“ 221.147.191.247 - - [16/Feb/2017:09:38:20 -0800] "GET /explore HTTP/1.1" 200 6579 "-" "Mozilla/5.0 (Windows; U; Windows NT 5.1) AppleWebKit/538.0.1 (KHTML, like Gecko) Chrome/38.0.895.0 Safari/538.0.1“ 179.96.123.130 - - [16/Feb/2017:09:38:20 -0800] "GET /list HTTP/1.1" 200 560 "-" "Mozilla/5.0 (Windows NT 6.3; Win64; x64; rv:5.4) Gecko/20100101 Firefox/5.4.6“ 132.119.12.76 - - [16/Feb/2017:09:38:20 -0800] "PUT /explore HTTP/1.1" 200 3131 "-" "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_6_0 rv:5.0; AZ) AppleWebKit/535.1.0 (KHTML, like Gecko) Version/4.0.3 Safari/535.1.0"
  • 20. After Data Transformation {"host":"210.197.211.230","ident":"-","authuser":"-","request":"GET /explore HTTP/1.1","response":301,"bytes":2457,"verb":"GET","@timestamp":"2017-06- 07T14:35:32.000Z","timezone":"-0700","@timestamp_utc":"2017-06-07T21:35:32.000Z"} {"host":"25.217.150.148","ident":"-","authuser":"-","request":"PUT /app/main/posts HTTP/1.1","response":200,"bytes":662,"verb":"PUT","@timestamp":"2017-06- 07T14:35:32.000Z","timezone":"-0700","@timestamp_utc":"2017-06-07T21:35:32.000Z"} {"host":"187.232.212.221","ident":"-","authuser":"-","request":"GET /app/main/posts HTTP/1.1","response":200,"bytes":1549,"verb":"GET","@timestamp":"2017-06- 07T14:35:32.000Z","timezone":"-0700","@timestamp_utc":"2017-06-07T21:35:32.000Z"} {"host":"227.167.15.81","ident":"-","authuser":"-","request":"PUT /explore HTTP/1.1","response":200,"bytes":7591,"verb":"PUT","@timestamp":"2017-06- 07T14:35:32.000Z","timezone":"-0700","@timestamp_utc":"2017-06-07T21:35:32.000Z"}
  • 22. Step 4 Analyze the Data from Amazon Elasticsearch Service
  • 23. Query Data from Kibana
  • 25. Step 5 Monitor Log Ingestion Pipeline
  • 28. Q & A