This article explains how to use Logstash to import CSV data into Elasticsearch. We make use of the file input, CSV filter, and Elasticsearch output components of Logstash. Importing CSV into Elasticsearch using Logstash is a pretty simple and straightforward task, but several aspects of this process can make importing a CSV into Elasticsearch complicated quickly. I'm going to teach you some concepts that are important in this context. Some of these concepts will be useful for working with Logstash and Elasticsearch in general.

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We have seen numerous pipeline aggregations in previous posts. Here we discuss another pipeline aggregation called the moving average aggregation and its significance, as well as its application in real-life scenarios.

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Not yet enjoying the benefits of a hosted ELK-stack enterprise search on Qbox? Discover how easy it is to manage and scale your Elasticsearch environment.

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Logstash has an interesting feature called the Translate Filter. The translate filter is used to filter specific fields in incoming data against a dictionary or lookup file. It then performs an action if the incoming field matches an entry in the dictionary lookup file such as adding a field to your data or sending an email. 

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This tutorial introduces Moloch and how to use it in conjunction with Elasticsearch. Moloch is an open source piece of software that can be used to index very large PCAP files into Elasticsearch. Moloch is a project which began at AOL. You can find the source code here: https://github.com/aol/moloch

Moloch consists of four different parts: A web interface or viewer, a capture application which was written in C, a datastore which is Elasticsearch, and a REST API. The web interface is used to view the PCAP files or network traffic indexed into Elasticsearch. Moloch was designed, with performance in mind, to be able to handle very large sets of data. Moloch is fast and can scale upwards, which is helpful if you have many server resources to allocate to a Moloch cluster.

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Natural Langue Processing, or NLP, is one of the most active areas of research in Data Analytics due to the large volume of data available across the web and the need to analyze and gain insights from this data that constitute to development and growth from a business perspective. There are a number of areas like Entity Extraction, Event Classification Sentiment Analysis, and more that NLP can be thought of like a super set to. We considered how elasticsearch can be used as a source to visualize the end product of all these tasks. This series introduces basic level prototypes of the functional areas of NLP to help you get started.

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Let's look at the basics of indexing data into Elasticsearch. A wealth of knowledge on Elasticsearch will help you understand why you sometimes encounter issues when working with both Logstash and Kibana. Many issues encountered by new users has to do with them not understanding how Logstash and Kibana interact with Elasticsearch.

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