Tag: machine data analytics

CrateDB 2.0 Adds Clustering Upgrades and SQL Enhancements to Its Database Solution for IoT and Machine Data

On May 16, Crate.io announced the availability of CrateDB 2.0, an open source SQL database that specializes in IoT and machine data. The innovation of CrateDB consists in leveraging SQL to aggregate and perform real-time analytics on IoT and machine data instead of the NoSQL databases commonly used in the industry for related use cases. CrateDB’s ability to accommodate the ingestion of high velocity streams of data and to perform queries on rapidly changing datasets, with impressive levels of scalability and latency, allows developers to combine their familiarity with SQL alongside a solution specially designed for the unique needs of IoT and machine data applications. CrateDB 2.0 features clustering upgrades that deliver improved query performance by means of faster aggregations and new index structures. In addition, CrateDB 2.0 contains a bevy of SQL enhancements that give developers a greater range of options regarding joins, sub-selects and the renaming and re-indexing of tables. The Enterprise Edition of CrateDB 2.0 offers performance monitoring, enhanced security as well as the ability for end users to create user-defined functions. CrateDB 2.0’s clustering upgrades, SQL enhancements and enterprise-grade security and performance monitoring mark a new milestone in the platform’s evolution that testifies to its readiness to embrace enterprise-grade workloads that include sensor data, GPS data and the industrial internet more generally. Subsequent to news of its general availability in December 2016, Crate.io’s release of open source and enterprise-grade versions of CrateDB underscores the early traction the platform has received, with over 1.3 million downloads and 50 customers using Crate.io in production. With the IoT and machine data space gearing up for a rampant proliferation of devices and corresponding datasets in forthcoming years, expect Crate.io to continue building on its recent momentum, particularly as organizations look for scalable databases that allow organizations to leverage widely available skillsets in SQL.

The graphic below illustrates the platform’s Enterprise Edition user interface for monitoring the performance of clusters gives users real-time visibility into cluster performance with respect to the ingestion and transformation of IoT and machine data:

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CrateDB Combines SQL-based Queries And Extreme Scalability For Machine Data Analytics

Crate.io today announces the general availability of CrateDB, an open source SQL-database platform that specializes in storing and analyzing machine data and related applications. CrateDB features a distributed SQL query engine that empowers users to run complex queries in real-time without the diminution of performance specific to “first generation SQL databases”, as noted in a press release. The platform also boasts columnar field caches and enhanced versatility with respect to SQL-based queries on machine data. For example, CrateDB delivers the capability to create outer joins as well as run queries on structured and unstructured data, perform time series analysis and leverage advanced database search functionality. In addition, CrateDB features extreme scalability marked by automated sharding and data redistribution that optimizes data performance and availability in correspondence with the volume of data stored within the platform. Importantly, CrateDB allows organizations to take advantage of SQL-oriented skills and tools to expedite its integration and adoption. As such, the platform represents a SQL-based alternative to NoSQL machine data solutions such as Splunk and Cassandra that empowers organizations to collect and analyze massive volumes of machine data in real-time in conjunction with the platform’s enhanced querying versatility and scalability. Available under an Apache 2.0 license, CrateDB marks the emergence of another key player in the machine data analytics space that promises to disrupt the landscape of machine data analytics platforms, particularly given the nexus of its advanced SQL-based querying functionality and extreme scalability. Organizations with resources versed primarily in SQL will lean toward CrateDB given the richness of its distributed SQL querying engine and ability to query data in real-time without resorting to an ancillary data warehousing option to append to their machine data analytics infrastructure.

Logentries Adds Automated Alerts And Notifications To Its Log Management Platform

Logentries recently announced the availability of “anomaly detection and inactivity alerting” as part of its cloud-based log management platform. The newly available anomaly detection functionality allows customers to proactively identify aberrant system or user behavior toward the end of resolving issues as expeditiously as possible. In addition to using Logentries to store log data and run customized analytics to understand machine data-related trends, Logentries now delivers alerts and notifications that notify IT administrators about anomalous behavior as exemplified by potential security breaches, malfunctioning infrastructure components or underperforming applications. The platform’s push-based notifications leverage machine-learning technology that iteratively increases its understanding of the typical behavior of a specific IT infrastructure inclusive of seasonal or even daily and hourly fluctuations in user activity and its corresponding machine data. To set up alerts, customers specify tags that are applied to incoming log data that can be used to create real-time alerts that are received via “text, PagerDuty, HipChat, Campfire, and other webhook APIs.” Alerts and notifications can also be created by means of the Logentries Open API that allows users to transmit log data from select devices with the Logentries platform. Overall, Thursday’s announcement marks a significant addition to the Logentries platform’s suite of features and functionality by providing out of the box alerts and notifications in contrast to the customized identification of exceptions by means of business intelligence platforms such as Tableau. Expect Logentries to continue building out its analytics and data visualization functionality for machine data as it attempts to deliver a nimbler and simpler alternative to machine data analytics vendors such as Splunk.

Logentries Enhances Log Management And Analytics Platform With DevOps-Focused Collaboration Functionality

Log management and analytics vendor Logentries today announced an enhancement to its platform marked by the availability of a suite of collaboration features that improve the ability of teams to analyze and share insights regarding log data. Users of the Logentries platform can now annotate log data, share dashboards and send automated notifications to individuals and groups. The newly released collaboration functionality enhances the ability of the platform to serve the needs of DevOps teams that demand real-time agility with respect to log data analytics as well as the ability to communicate their observations regarding log data. The real-time collaboration functionality enabled by today’s release of the Logentries platform empowers DevOps professionals to more efficaciously identify root causes for issues such as system downtime, diminished application performance or networking-related bottlenecks as illustrated by the screenshot below.

The graphic above illustrates the annotation capability specific to today’s release. The annotation on the 404 Failure identifies an issue on a development server that may pertain to production servers as well. Logentries further instantiates the theme of accessibility and collaboration by enabling users to search log data using natural language and a click-through user interface that frees analysts from the need to write complex queries to understand the significance of log data. The platform also leverages a pre-processing engine that powers its analytics and data visualization capabilities in ways that deliver actionable business intelligence regarding real-time data. As told to Cloud Computing Today by Logentries CEO Andrew Burton, the Logentries platform can be used to understand data within on premise, public cloud, private cloud and hybrid cloud environments. The platform differentiates itself from the likes of Splunk, Loggly and Sumo Logic by means of enhanced data visualization and collaboration functionality that renders the platform amenable to business stakeholders that have little or no experience with scripting languages. Logentries plays in the hot machine analytics space with a platform whose rich analytics, collaboration and UI render it distinctive. Expect to hear more about the progress of Logentries as it builds on its 25,000 user base in subsequent months.

Glassbeam SCALAR Delivers Machine Data Analytics And BI For Internet Of Things

Today, machine data analytics vendor Glassbeam announces the release of Glassbeam SCALAR, a cloud-based platform that specializes in machine data analytics for the internet of things. In contrast to machine data analytics vendors such as Splunk and Loggly that focus on analytics related to machine data specific to data center and cloud-based IT environments, Glassbeam concentrates on complex, multi-structured log data from medical devices, sensors and automobiles in addition to data center devices.

The SCALAR platform leverages open source Big Data technologies such as Cassandra and Solr to deliver actionable business intelligence derived from structured and unstructured data. Meanwhile, SCALAR’s parallel asynchronous engine allows the platform to scale horizontally to process massive amounts of machine data that are interpreted by way of its dynamic schema functionality and semantic technologies that enrich the platform’s ability to make connections between source data elements.

A key component of the Glassbeam SCALAR platform is Glassbeam Studio, a tool that allows developers to transform unstructured machine data into structured formats for the purpose of performing a deeper dive into incoming data. Glassbeam Studio positions developers to transform data into Glassbeam’s proprietary semiotic parsing language (SPL) for understanding machine data before it is digested by the Glassbeam SCALAR platform and transformed into visual analytics as illustrated below:

Glassbeam’s output consists of visually rich, business intelligence dashboards that enable customers to make proactive, strategic decisions by way of a dynamic, real-time dashboard that illustrates trends and key attributes of source data. Key features of the Glassbeam platform in relation to the larger landscape of machine data sources and modalities of deployment are as follows:

Given that the internet of things remains to be fully realized, the release of Glassbeam SCALAR represents a bold attempt to carve out an early niche in the space by focusing on business intelligence in relation to machine data. Nevertheless, the underlying technology platform impresses by way of the breadth of its functionality as exemplified by Glassbeam Explorer, the platform’s new search and discovery tool for understanding, analyzing and visualizing machine data. Expect Glassbeam to continue to innovate and differentiate itself from the rest of the machine data analytics space, even as it captures market share related to data center devices such as servers, routers and firewall technologies. In the meantime, the race is on with respect to who will capture the early foothold in the internet of things Big Data analytics space as the industrial internet matures and gradually renders itself more and more ubiquitous in the lives of enterprises and consumers alike. Pivotal One has already placed its bets in the Internet of Things analytics space thanks to a $105 million investment from GE, but the landscape still remains wide open.