TIBCO Spotfire® Data Science is an enterprise big data analytics platform that can help your organization become a digital leader. The collaborative user-interface allows data scientists, data engineers, and business users to work together on data science projects. These cross-functional teams can build machine learning workflows in an intuitive web interface with a minimum of code, while still leveraging the power of big data platforms.
Spotfire Data Science provides a complete array of tools (from visual workflows to Python notebooks) for the data scientist to work with data of any magnitude, and it connects natively to most sources of data, including Apache™ Hadoop®, Spark®, Hive®, and relational databases. While providing security and governance, the advanced analytic platform allows the analytics team to share and deploy predictive analytics and machine learning insights with the rest of the organization, white providing security and governance, driving action for the business.
Watch this on-demand webcast to learn how you can accelerate your security transformation from traditional SIEM to a unified platform for incident detection, investigation and advanced security analysis. Understand why organizations are moving to a true big data security platform where compliance is a byproduct of security, not the other way around.
Published By: LogRhythm
Published Date: Jan 24, 2013
A SANS functional product review of LogRhythm version 6.1, conducted by senior SANS Analyst Dave Shackleford. It shows LogRhythm's SIEM toolset capable of analyzing and reporting on security data in many differed ways with easy-to-use features.
The status quo approach of collecting more logs from more sources won't help in detecting and responding to advanced threats. Logs are inherently limited in the level security visibility that they provide. Consider a new way of looking at SIEM.
To develop the visibility, agility and speed to deal with advanced threats, security information and event management (SIEM) systems need to evolve into a central nervous system for large-scale security analytics.
Published By: Microsoft
Published Date: Jul 20, 2018
At its Build conference in May, Microsoft took the wraps off Cosmos DB, the new incarnation of its
existing cloud-based Azure DocumentDB NoSQL database. With a nod to the dramatic, Microsoft
terms Cosmos DB as its biggest database bet since SQL Server; it is positioning it as its flagship
cloud database, suited for use cases ranging from security and fraud detection, to IoT (consumer and
industrial), personalization, e-commerce, gaming, social networks, chats, messaging, bots, oil and gas
recovery and refining, and smart utility grids. Cosmos DB is a good example of how cloud platform
providers are rethinking databases for scalable, elastic environments and commodity infrastructure.
The platform that is most comparable is Google Cloud Spanner, but each of these databases is
engineered for different purposes: Cosmos DB as a globally distributed operational database and
Spanner as a globally distributed SQL-supporting OLTP database.
The highlights of Cosmos DB include its flexibility in
Published By: SnowFlake
Published Date: Jul 08, 2016
In the era of big data, enterprise data warehouse (EDW) technology continues to evolve as vendors focus on innovation and advanced features around in-memory, compression, security, and tighter integration with Hadoop, NoSQL, and cloud. Forrester identified the 10 most significant EDW software and services providers — Actian, Amazon Web Services (AWS), Hewlett Packard Enterprise (HPE), IBM, Microsoft, Oracle, Pivotal Software, SAP, Snowflake Computing, and Teradata — in the category and researched, analyzed, and scored them. This report details our findings about how well each vendor fulfills our criteria and where they stand in relation to each other to help enterprise architect professionals select the right solution to support their data warehouse platform.
This white paper examines how some of the ways organizations
use big data make their infrastructures vulnerable to attack. It
presents recommended best practices organizations can adopt
to help make their infrastructures and operations more secure.
And it discusses how adding advanced security software solutions
from IBM to their big-data environment can fill gaps that
big-data platforms by themselves do not address. It describes
how IBM® Security Guardium®, an end-to- end solution for
regulatory compliance and comprehensive data security, supports
entitlement reporting; user-access and activity monitoring;
advanced risk analytics and real-time threat detection analytics;
alerting, blocking, encryption and other data protection capabilities,
as well as automated compliance workflows and reporting
capabilities, to stop threats.