Real-time big data analytics - definition & overview

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.

Real-time

In a computing context, real-time data processing essentially means that we are operating 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, analyzing event logs after the fact is no longer effective in determining whether an attack occurred. Real-time big data analytics helps organizations mitigate attacks as they happen by analyzing event logs milliseconds after they are created.

Big data

The widespread use of software applications generated massive amounts of data.

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

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 analyzed manually.

Today’s best analytics tools combine advanced technologies like machine learning and pattern recognition with other software features to achieve a specified goal. IT organizations use analytics tools 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 typically deployed by IT organizations in industries that produce or capture large amounts of data over a short period, like banking or IT. 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 necessary to ensure the functioning of the IT infrastructure. ITOps is directly responsible for monitoring the IT infrastructure through defined 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 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 network intrusion. Security analysts use analytics tools to gather threat intelligence, automate threat detection and response and conduct forensic investigations after a cyber attack.

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 also be used to gather application usage data and assess the performance of deployed services in the cloud. Organizations can analyze 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.

What are some popular tools and technologies used for real-time big data analytics?

• Apache Kafka

• Amazon Kinesis

• Apache Flink

• Spark SQL

• Apache Hadoop

• Google Cloud

• Tableau

• Power BI

What are the key differences between traditional data analytics, real-time data analytics and real-time big data analytics?

Traditional data analytics involves analyzing historical data to identify trends and gain actionable insight for decision-making. On the other hand, real-time analytics focuses on analyzing data as it is generated to provide immediate insights and enable swift decision-making. Real-time big data analytics combines the speed of real-time analytics with the vast amount of data in big data sets to provide instant insights on large, complex data sets

What role does machine learning play in enhancing real-time big data analytics capabilities?

Machine learning enables automated model training and prediction in real time. By leveraging machine learning algorithms, organizations can analyze massive volumes of data in real time, extract valuable insights, detect patterns, and make predictive decisions instantaneously. Machine learning algorithms can adapt and learn from new data on the fly, continuously improving the accuracy and efficiency of real-time analytics. This dynamic nature of machine learning enhances the speed and accuracy of real-time data processing, enabling organizations to derive actionable insights quickly and make informed decisions in the face of rapidly changing data streams.