Search the Community

Solr is an open-source, highly scalable search platform built on top of Apache Lucene. It provides powerful capabilities for searching, indexing, and faceting large amounts of data. Here are 10 real use cases of Solr: Apache Solr is an open-source search platform built on Apache Lucene, which is a high-performance, full-text search engine library. Solr is widely used for enterprise search and analytics purposes because it provides robust full-text search, hit highlighting, faceted search, dynamic clustering, database integration, and rich document (like Word and PDF) handling capabilities. It is designed to handle large volumes of text-centric data and provides distributed search and index replication functionalities. Solr is also known for its scalability and fault tolerance, making it a popular choice for large-scale search applications. Here are ten real use cases of Solr: E-commerce Product Search: Solr is commonly used in e-commerce platforms to provide advanced search capabilities over a vast inventory of products. It helps in delivering relevant search results, supporting facets and filters (like brand, price range, and product features) to enhance user experience. Content Management Systems (CMS): Integrating Solr with CMSs allows websites to manage and search through large repositories of content such as articles, blogs, and other media types efficiently. Enterprise Document Search: Companies use Solr to index and search through extensive collections of documents, including emails, PDFs, Word documents, and more, making it easier for employees to find the information they need quickly. Social Media Analytics: Solr can process and index large streams of social media data for sentiment analysis, trend tracking, and monitoring public opinion, enabling businesses to gain insights into customer perceptions. Geospatial Search: Solr supports location-based searches, which can be used in applications like real estate listings and location-specific services to find entities within a given distance from a geographic point. Data Collection and Discovery: Research institutions use Solr to manage, search, and analyze large datasets, facilitating data discovery and academic research. Job and Resume Searching: Job portals utilize Solr to match candidates with jobs effectively. It indexes job listings and resumes, providing powerful search and filtering capabilities. News and Media Sites: Media outlets use Solr to manage and retrieve news content and articles based on various attributes like publication date, relevance, keywords, etc. Healthcare Information Systems: Solr is used in healthcare for indexing medical records, research papers, treatment histories, and other data, improving access to information and supporting better healthcare outcomes. Recommendation Systems: Solr’s ability to handle complex queries and analyze large amounts of data helps in building recommendation engines that suggest products, services, or content based on user preferences and behavior. The post What is Solr? appeared first on DevOpsSchool.com. View the full article

The post How To Install Elasticsearch, Logstash, and Kibana (ELK Stack) on RHEL first appeared on Tecmint: Linux Howtos, Tutorials & Guides .If you are a person who is, or has been in the past, in charge of inspecting and analyzing system logs in Linux, you know The post How To Install Elasticsearch, Logstash, and Kibana (ELK Stack) on RHEL first appeared on Tecmint: Linux Howtos, Tutorials & Guides.View the full article

30 Best DevOps Tools to Learn and Master In 2023: Git, Docker ... https://www.simplilearn.com/tutorials/devops-tutorial/devops-tools

This is the first post of a 2 part series where we will set-up production grade Kubernetes logging for applications deployed in the cluster and the cluster itself. View the full article

Amazon QuickSight dashboards can now visualize data from Amazon Elasticsearch Service. Amazon Elasticsearch Service is a fully managed service that makes it easy for you to deploy, secure, and run Elasticsearch cost effectively at scale. Authors in QuickSight can select Amazon Elasticsearch Service as a data source, select the specific data domain to analyze and start visualizing in QuickSight. See here to learn more. View the full article

You can now use Amazon Elasticsearch Service as a target data store with AWS Glue Elastic Views. Now in limited preview, AWS Glue Elastic Views is a new capability of AWS Glue that makes it easy to combine and replicate data across multiple data stores without you having to write custom code. With AWS Glue Elastic Views, you can use familiar Structured Query Language (SQL) to quickly create a virtual table—called a view—from multiple different source data stores. Based on this view, AWS Glue Elastic Views copies data from each source data store and creates a replica—called a materialized view—in a target data store. AWS Glue Elastic Views monitors for changes to data in your source data stores continuously, and provides updates to your target data stores automatically, ensuring data accessed through the materialized view is always up-to-date. View the full article

Amazon Elasticsearch Service now supports Piped Processing Language (PPL), a new feature that enables users to explore, discover and find data stored in Amazon ES, using a set of commands delimited by pipes (|). PPL extends Elasticsearch to support a standard set of commands that is easy for system developers, DevOps engineers, support engineers, site reliability engineers (SREs), and IT managers who are proficient with Linux or Unix to learn. PPL enables these users to begin extracting insights from their log, monitoring and observability data on day one. View the full article

Amazon Elasticsearch Service now offers anomaly detection for high cardinality datasets. This new feature enables you to sift through thousands of metrics from millions of events to accurately pinpoint individual entities with abnormal patterns. By leveraging machine learning, Amazon Elasticsearch Service now provides reliable and actionable insights to drastically reduce the time to isolate and remediate issues. High cardinality anomaly detection can be invaluable for a number of operational, security and business use cases like identifying hosts with high CPU and memory consumption, finding services with most error rates, isolating suspicious users or IP addresses accessing sensitive information, or detecting outliers in sales by region. View the full article

Amazon Elasticsearch Service now offers support for Remote Reindex, enabling you to migrate data from a remote cluster into Amazon Elasticsearch Service. With this feature, you can simply copy data from one cluster to another, making it easier to migrate from legacy versions of Elasticsearch. Remote Reindex also supports migrating indexes from self-managed Elasticsearch onto Amazon Elasticsearch Service, providing a simple mechanism to onboard onto the service. View the full article

Amazon Elasticsearch Service now supports Gantt charts, a new visualization in Kibana. Users can now embed Gantt charts into dashboards to enable visualization of events, steps and tasks as horizontal bars. The length of the bars shows the amount of time associated with an event, step or a task. Gantt charts are used to represent a series of events that contain a parent-child relationship. This can be particularly useful in trace analytics, telemetry, and monitoring use cases, in which the users need to understand the overall interaction between traces or events. Gantt charts help users manage their resources by getting an overview of the events or tasks and understanding the relationships between them. View the full article

Amazon Elasticsearch Service has introduced several security enhancements to the fine-grained access control feature that include a revamped and improved security workflow in Kibana, and integration with Open Distro for Elasticsearch Alerting and Anomaly Detection features. View the full article

Amazon Elasticsearch Service has introduced several security enhancements to the fine-grained access control feature that include a revamped and improved security workflow in Kibana, and integration with Open Distro for Elasticsearch Alerting and Anomaly Detection features. View the full article

Amazon Elasticsearch Service now supports open source Elasticsearch 7.9 and its corresponding version of Kibana. This minor release includes bug fixes and enhancements. View the full article

Amazon Elasticsearch Service now supports the ability to reload dictionary files without reindexing your data. Elasticsearch uses analyzers to convert string data into terms or tokens that power its search capabilities. These analyzers can do things like remove white space and stop words, perform stemming, handle compound words, and add synonyms. Previously, on Amazon Elasticsearch Service these analyzers could only process data as it was indexed. If you wanted to add some additional synonyms at a later time, you had to reindex your data with the new dictionary file. View the full article

Amazon Elasticsearch Service now natively supports using Security Assertion Markup Language (SAML) to offer single sign-on (SSO) for Kibana. SAML authentication for Kibana enables users to integrate directly with third-party identity providers (IDP) such as Okta, Ping Identity, OneLogin, Auth0, Active Directory Federation Services (ADFS) and Azure Active Directory. With this feature, your users can leverage their existing usernames and passwords to log in to Kibana, and roles from your IDP can be used for controlling privileges in Elasticsearch and Kibana, including what operations they can perform and what data they can search and visualize. View the full article

Amazon Elasticsearch Service now supports open source Elasticsearch 7.8 and its corresponding version of Kibana. This minor release includes bug fixes and enhancements. View the full article