Keeping Up with the ELK Stack: Elasticsearch, Kibana, Beats, and Logstash

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Version 7 of the Elastic Stack adds powerful new features to the popular open source platform for search, logging, and analytics. Come hear directly from Elastic engineers and architecture team members on powerful new additions like GIS functionality and frozen-tier search. Plus, hear about the full range of orchestration options for getting the most out of your deployments, however and wherever you choose to run them. This session is sponsored by Elastic.

© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C T O R
S U M M I T
Keeping up with the Elastic
Stack
Dave Erickson
Director of Solution Architecture
Elastic

2
2015 was a long time ago
demo.elastic.co

3
Users! What does your Kibana look like?

4
Users! What does your Kibana look like?

10
What about Fed?
ATOs on every level government network
Production system at every cabinet level agency

11
Store, Search, &
Analyze
Visualize &
Manage
Ingest
Elastic Stack
SOLUTIONS
Elastic Stack
Kibana
Elasticsearch
Beats Logstash
SaaS SELF-MANAGED
Elastic cloud Elastic cloud
Enterprise Standalone

1212
Technology differentiation
SCALE
Distributed by design
SPEED
Find matches in milliseconds
RELEVANCE
Get highly relevant results

13
Logging
Metrics
Uptime
N/APM
Security
Data Lakes
Search
Reporting
Analytics
1
reducing complexity
Elastic Stack

14141
and maximizing ROI
1$ investment in a discrete solution might
solve for 2 or 3 business challenges
• App 1: Logging, Security, Metrics
• App 2: APM, RUM
• App 3: Network Metrics
• App 4: Site Search, App Search
• App 5: Business Analytics
1$ Investment in Elastic can solve for
all of these challenges, and more
APMLOGGING METRICS
APP SEARCH BUSINESS
ANALYTICS
ENTERPRISE
SEARCH
SITE SEARCH
SECURITY MAPS UPTIME

Solutions
Store, Search, &
Analyze
Visualize &
Manage
Ingest
Kibana
Elasticsearch
Beats Logstash
Elastic Stack
SaaS SELF-MANAGED
App Search Site Search Enterprise Search
FUTURE
Metrics APM
Business Analytics
Logging Security AnalyticsUptime
Maps

18
Logging
Your logs, your way. At any scale, with speed.

19
Infrastructure Metrics
Unify your infrastructure monitoring (logs, metrics, and traces) in one place

20
Application Performance Monitoring (APM)
Amplify your observability with application transaction and tracing

21
Security Analytics
A solution designed for fast, ad hoc exploration of security data at petabyte scale.

22
Security Analytics
A solution designed for fast, ad hoc exploration of security data at petabyte scale.
Step 1
Step 2 …

23
Site Search
Add search to your website in minutes. Powered by Elasticsearch.

24
App Search
Powerful search, built for developers

252
Deployment Options
Elastic Cloud
Enterprise
Orchestration tools &
integration with major
cloud platforms provide
the best of both worlds
Elastic
Cloud
Elastic Software as a
Service, managed
completely by the people
who know it best.
On
Premise
Leverage your data
centers and engineering
teams to get the most
from the Elastic solutions

262
Deployment Options
Elastic Cloud
Enterprise
Orchestration tools &
integration with major
cloud platforms provide
the best of both worlds
Elastic
Cloud
Elastic Software as a
Service, managed
completely by the people
who know it best.
On
Premise
Leverage your data
centers and engineering
teams to get the most
from the Elastic solutions

27
Live Demo Elasticsearch as a Service
Hosted Elasticsearch, from the creators. No one hosts it better.

Thank you!
© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C T O R
S U M M I T
Dave Erickson
dave@elastic.co

© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C T O R
S U M M I T

The document provides an introduction to the ELK stack, which is a collection of three open source products: Elasticsearch, Logstash, and Kibana. It describes each component, including that Elasticsearch is a search and analytics engine, Logstash is used to collect, parse, and store logs, and Kibana is used to visualize data with charts and graphs. It also provides examples of how each component works together in processing and analyzing log data.

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( ELK Stack Training - https://www.edureka.co/elk-stack-trai... ) This Edureka tutorial on What Is ELK Stack will help you in understanding the fundamentals of Elasticsearch, Logstash, and Kibana together and help you in building a strong foundation in ELK Stack. Below are the topics covered in this ELK tutorial for beginners: 1. Need for Log Analysis 2. Problems with Log Analysis 3. What is ELK Stack? 4. Features of ELK Stack 5. Companies Using ELK Stack

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ELK (Elasticsearch, Logstash, Kibana) is an open source toolset for centralized logging, where Logstash collects, parses, and filters logs, Elasticsearch stores and indexes logs for search, and Kibana visualizes logs. Logstash processes logs through an input, filter, output pipeline using plugins. It can interpret various log formats and event types. Elasticsearch allows real-time search and scaling through replication/sharding. Kibana provides browser-based dashboards and visualization of Elasticsearch query results.

Elastic Stack is a suite of open source tools for log analytics and data processing including Beats, Logstash, Elasticsearch, Kibana, Curator, and hosted cloud solutions. Beats are lightweight data shippers that collect data from endpoints and send to Logstash or Elasticsearch. Logstash is used for data collection, transformation, and transport to Elasticsearch for storage and search. Kibana provides data visualization and dashboards. Curator manages Elasticsearch indices. The Elastic Stack can be self-hosted or used via cloud offerings.

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Elk - An introduction

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So, what is the ELK Stack? "ELK" is the acronym for three open source projects: Elasticsearch, Logstash, and Kibana. Elasticsearch is a search and analytics engine. Logstash is a server‑side data processing pipeline that ingests data from multiple sources simultaneously, transforms it, and then sends it to a "stash" like Elasticsearch. Kibana lets users visualize data with charts and graphs in Elasticsearch.

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The ELK stack is an open source toolset for data analysis that includes Logstash, Elasticsearch, and Kibana. Logstash collects and parses data from various sources, Elasticsearch stores and indexes the data for fast searching and analytics, and Kibana visualizes the data. The ELK stack can handle large volumes of time-series data in real-time and provides actionable insights. Commercial plugins are also available for additional functionality like monitoring, security, and support.

Kibana + timelion: time series with the elastic stack

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The document discusses Kibana and Timelion, which are tools for visualizing and analyzing time series data in the Elastic Stack. It provides an overview of Kibana's evolution and capabilities for creating dashboards. Timelion is introduced as a scripting language that allows users to transform, aggregate, and calculate on time series data from multiple sources to create visualizations. The document demonstrates Timelion's expression language, which includes functions, combinations, filtering, and attributes to process and render time series graphs.

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Kafka Streams vs. KSQL for Stream Processing on top of Apache Kafka

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Spoilt for Choice – Kafka Streams vs. KSQL for Stream Processing on top of Apache Kafka: Apache Kafka is a de facto standard streaming data processing platform. It is widely deployed as event streaming platform. Part of Kafka is its stream processing API “Kafka Streams”. In addition, the Kafka ecosystem now offers KSQL, a declarative, SQL-like stream processing language that lets you define powerful stream-processing applications easily. What once took some moderately sophisticated Java code can now be done at the command line with a familiar and eminently approachable syntax. This session discusses and demos the pros and cons of Kafka Streams and KSQL to understand when to use which stream processing alternative for continuous stream processing natively on Apache Kafka infrastructures. The end of the session compares the trade-offs of Kafka Streams and KSQL to separate stream processing frameworks such as Apache Flink or Spark Streaming.

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Introducing ELK

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This document introduces the (B)ELK stack, which consists of Beats, Elasticsearch, Logstash, and Kibana. It describes each component and how they work together. Beats are lightweight data shippers that collect data from logs and systems. Logstash processes and transforms data from inputs like Beats. Elasticsearch stores and indexes the data. Kibana provides visualization and analytics capabilities. The document provides examples of using each tool and tips for working with the ELK stack.

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Examining OpenData with a Search Index using Elasticsearch

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Elastic is a search company that provides three main solutions - Elastic Enterprise Search, Elastic Security, and Elastic Observability. It has employees in over 40 countries and is a public company listed on the NYSE. Elastic focuses on fast, scalable search capabilities that are relevant to users. It provides unified visibility for logs, metrics and application performance monitoring. Elastic Security integrates endpoint protection and security information and event management. All solutions are powered by the Elastic Stack which allows ingestion, storage, search and analysis of data from any source.

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