We are speaking today with Rahul Gaikwad and Krishna Palati of FireEye’s devops team. Their presentation at Scylla Summit 2019 is entitled FireEye & Scylla: Intel Threat Analysis using a graph database.
Anyone who’s tried to build such a solution knows that one of the chief difficulties is encompassing the sheer number and complexity of existing data sources. In order to deliver a true solution, we need to be able to bring this disparate data together. A graph data system, built with JanusGraph and backed by the power of Scylla, is a great fit for solving this problem.
CHECK OUT PART ONE OF THIS BLOG We covered the basics of Elasticsearch and how Scylla is a perfect complement for it in part one of this blog. Today we want to give you specific how-tos on connecting Scylla and Elasticsearch, including use cases and sample code. Use Case #1 If combining a persistent, highly available datastore with full text search engine is a market requirement, then implementing a single, integrated solution is an ultimate goal that requires time and resources. To answer this challenge we describe below a way for users to use best-of-breed solutions that support full text […]
Full text search is required in many human-facing applications, such as where users need to interact with a datastore to retrieve and insert data based on partial, wildcard information, spell correction and autocompletion. Additional benefits of full text search is the ability to retrieve multiple results sorted by their relevance. Lucene, the common parent to Solr and Elasticsearch The most popular textual search engine in the market is Lucene. It is used by Solr, Elasticsearch, Lucidworks and other text search tools. Lucene is a great search engine. It is extremely fast, stable, and you probably can’t get much better than […]
When an organization changes their database backend, it is not a simple task and there is usually an interesting story behind it. This was the case with Zenly, a mobile application that lets you know where your friends are in real time. They were using Elasticsearch as their main database to take advantage of its full-text search capabilities. However, Elasticsearch did not perform well for Zenly’s workload, which consists mostly of update operations, and they found it difficult to monitor and locate their data, so they began to look for a database replacement.
The combination of a database and full-text search analytics becomes unavoidable these days. In this blog post, I will demonstrate a simple way to analyze data from a database with analytics software by using Scylla and Elasticsearch together to perform a simple data mining exercise that gathers data from Twitter. This demonstration will use a series of Docker containers that will run a Scylla and Elasticsearch cluster and a Node.js app that will feed data from Twitter into both platforms. This demo can be run on a laptop or production Docker server. To get started, let’s go over the prerequisites […]