The Best Approach To Elasticsearch Security

Introduction

Elasticsearch has rapidly—and deservedly—established itself as a popular choice for enterprise application developers. The one negative associated with the highly capable distributed open-source search and log analytics engine is that it has garnered headlines for security hiccups. This reputation for security is arguably undeserved. I think it says more about the fact that organizations too often fail to treat Elasticsearch security with the respect required for any data storage solution. With the proper attention and an accurate understanding of Elasticsearch’s specific needs, the technology can be made sufficiently secure for enterprise needs.

The basic default Elasticsearch configuration lacks enterprise-grade security features. This combination—a simple deployment that’s just as easy to then ignore when it comes to subsequent security hardening—can easily lead to lax access restrictions and data protection. And, it has, as those aforementioned headlines have shown over the past couple of years. But, by implementing enterprise-grade security and adhering to best practices, enterprises can eliminate the errors that put Elasticsearch data in peril.

Elastic Search @ 6.4.3

Elastic - 6.4.3

Working with Elastic is quite fun. You can simply use a query string URL or compose a Java program using the Java high-level/low-level client to consume and return the results.

In this blog, I am going to write a simple Java service that will expose a specific computation on top of an elastic search query. You can read more at the elastic.io

Analytics on Kafka Event Streams Using Druid, Elasticsearch, and Rockset

Everything you need to get started analyzing Kafka Event Streams

Events are messages that are sent by a system to notify operators or other systems about a change in its domain. With event-driven architectures powered by systems like Apache Kafka becoming more prominent, there are now many applications in the modern software stack that make use of events and messages to operate effectively. In this blog, we will examine the use of three different data backends for event data - Apache Druid, Elasticsearch, and Rockset.

Using Event Data

Events are commonly used by systems in the following ways:

Hunting the ELK (Stack): Data Monitoring to Visualization

Experts in the field

Made up of Elastisearch, "a search and analytics engine," Logstash, "a server-side data processing pipeline that "ingests data from multiple sources simultaneously, transforms it, and then sends it to a 'stash'," (according to Elastic's official site) and Kibana, a robust visualization tool, the ELK stack has quickly become one of the premier tools available to developers for data processing, management, and visualization. 

Whether you're just starting out with any of the three technologies, or you're a seasoned veteran, we've compiled the best that our community has to offer for basic questions about getting started to complex tutorials for real-time data management.