It's more than a technological buzzword: real time big data analytics is a new technology that is redefining how IT organizations gather actionable business intelligence, detect cyber security threats and measure the performance of critical applications and web or cloud-deployed services.
Real time big data analytics is a software feature or tool capable of analyzing large volumes of incoming data at the moment that it is stored or created with the IT infrastructure. Enterprise IT security software such as Security Event Management (SEM) or Security Information and Event Management (SIEM) technologies frequently feature capabilities for the analysis of large data sets in real time.
Enterprise organizations today are deploying more applications to the cloud than ever before. Each application or server creates computer-generated records of all its activities known as event logs. With millions of new event logs created every day, organizations depend on real time big data analytics to efficiently comb the data for relevant patterns and insights that drive responsive IT and business decision-making.
To better understand the meaning of real time big data analytics, let's break the phrase down into its component parts - "real-time", "big data" and "analytics" - and delve deeper into the nuances of each one.
In a computing context, real-time data processing essentially means that we are performing an operation on the data just milliseconds after it becomes available. When it comes to monitoring your security posture, detecting threats and initiating rapid quarantine responses, a real time response is necessary to mitigate cyber attacks before hackers can damage systems or steal data.
In today's cyber security environment, it is no longer effective to analyze event logs after-the-fact to determine whether an attack occurred. Real-time big data analytics helps organizations mitigates attacks as they happen by analyzing event logs milliseconds after they are created.
The phrase Big Data is being used everywhere, but what's the difference between data and big data? Throughout the digital age, the widespread use of software applications has resulted in the generation of massive amounts of data. The storage of this data has been enabled by the simultaneous evolution of increasing cost and space-efficient hardware storage devices.
As the world's leading data collectors generated data sets that included many cases and high degrees of complexity, it became clear that traditional data processing applications could no longer meet the requirements of these organizations. Thankfully, increases in computer processing power led to the development of predictive analytics software and other tools that could help these organizations begin to extract information and insights from their enormous data sets.
IT organizations can leverage their big data through log management or SIEM tools that aggregate network, application and event log files into a centralized, normalized database.
Analytics is a software capability that takes data input from various sources, searches it for patterns, interprets those patterns and ultimately communicates the results in a human readable format. Analytics software uses mathematics, statistics, probabilities, and predictive models to find hidden relationships in data sets that are too complex and varied to be efficiently analyzed manually.
The best analytics tools today combine advanced technologies like machine learning and pattern recognition with other software features to achieve a specified goal. In IT organizations, analytics tools are used to review event logs and correlate events from across applications to identify Indicators of Compromise (IoCs) and respond to security incidents.
Now that we've fleshed out the details, it should be clear: real time big data analytics is helping businesses of all sizes gather valuable intelligence by leveraging insights from massive volumes of data more quickly than ever before. This technology is most often deployed by IT organizations in industries that produce or capture large amounts of data over a short time period - logistics, banking, or IT, for example. Here are three ways that IT organizations can benefit from real time big data analytics.
Empower IT Operations with Rapid Monitoring and Troubleshooting
IT operations teams are charged with carrying out the routine operational and maintenance tasks that are necessary to ensure the functioning of the IT infrastructure. IT Ops is directly responsible for monitoring the IT infrastructure through a defined set of control tools (SEM, SIM or SIEM tools, etc.), backing up databases to prevent data loss and restoring the system in case of outages. Real time big data analytics can be used to review event logs from across the network, enabling rapid identification and remediation of issues that are impacting customers.
Enhance IT Security with Rapid Incident Response Capabilities
IT security analysts work in the security operations center (SOC) and are accountable for maintaining the IT organization's security posture and guarding against cyber attacks.
In today's IT security environment, analysts rely on real time data and analytics to sift through millions of aggregated log files from across the network and detect signs of a network intrusion. Analytics tools are used by security analysts to gather threat intelligence, automate threat detection and response and to conduct forensic investigations after a cyber attack occurs.
Collect and Manage Performance Data to Drive Business Decision-Making
The impact of real time big data analytics goes beyond monitoring and securing the IT infrastructure. This technology can be also used to gather application usage data and assess the performance of deployed services in the cloud. Organizations can analyze that application performance data to drive product development decisions that increase customer engagement by prioritizing the right features and improvements at the right time.
Sumo Logic's cloud analytics platform makes it easy for organizations who deploy many applications in a hybrid cloud environment to leverage real time big data and analytics. Sumo Logic uses machine learning and pattern recognition capabilities to turn your existing data into actionable insights that drive excellence in business, IT security and IT operations.
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