Businesses today rely on real-time big data analytics to handle the vast and complex clusters of datasets. Here’s the state of big data today:
- The forecasted market value of big data will reach $650 billion by 2029.
- From 2010 to 2020, there has been a 5000% growth in the quantity of data created, captured, and consumed.
So, let’s understand what exactly real-time big data is and why it is becoming ultra-important for businesses of all sizes.
What is Real-Time Big Data Analytics?
Real-time big data analytics is a comprehensive application software that processes, analyzes, and extracts business-oriented information straight from hot data.
The hot data here refers to the data in continuous active use. This is on-demand data that requires immediate access, and is usually in transit mode.
For example, real-time customer data on an apparel e-commerce site is the hot data. It can be processed and analyzed right when the customer is shopping to personalize the customer experience, offer exclusive discounts, and optimize sales.
Real-time big data analytics has complex architecture that flawlessly works on a huge volume of data, including all unstructured, structured, and semi-structured formats. It generates insights with optimal precision, handling different data sizes, multiple sources, and complex patterns.
Layers of Real-Time Big Data Analytics Architecture
As big data solutions are complex in their nature, they require sophisticated orchestration and data management. Here, we are exploring the top 7 layers of real-time big data analytics architecture.
Layer #1 – Extracting Data From Multiple Data Sources
One of the key features of big data analytics solutions is their ability to pull a wide variety of data in different formats from various sources. Big data software extracts data from multiple sources, such as logs, databases, sensors, social media, and other digital platforms.
Layer #2 – Collecting and Ingesting Data
After being extracted from the data source, real-time data such as video, audio, application logs, website analytics, and IoT sensor data is ingested for machine learning (ML) and real-time analytics. Popular tools for real-time data streaming include Apache Kafka, Amazon Kinesis, and many more. They are scalable managed service platforms used to gather high-volume and high-velocity data streams.
Layer #3 – Cleaning and Processing Data
The real-time data is streamed in its initial format which means that it requires cleaning and processing before it becomes a valuable source for analysis. This is one of the important steps in big data processing and analytics in general. Open-source software, like Apache Spark or Apache Flink, is often used to prepare the real-time ingested data for on-demand analysis.
Layer #4 – Storing the Processed Data
Cleaned and processed data as well as raw data is stored in a data lake. Data lakes are repositories designed to securely store and process large amounts of data. Depending on the IT infrastructure, architects can choose data storage systems like Amazon S3, Apache Cassandra, Hadoop Distributed File System (HDFS), or others.
Layer #5 – Analyzing Data
Real-time big data solutions require data to be analyzed within several seconds or minutes, depending on the purpose of the software. This is possible due to Artificial Intelligence and Machine Learning algorithms. Data scientists might use analytics platforms like Apache Hadoop, Apache Spark, Apache Storm, and other advanced analytical techniques to analyze real-time data and extract valuable insights.
Layer #6 – Visualizing Data
Streaming big data analytics is often used to provide managers with real-time insights into operations processes, order volume, and supply chain capacity. This way, people in charge can make quick decisions based on the changing visuals. Tools like Tableau, QlikView, and D3.js are often used to create easy-to-understand visualizations, dashboards, and reports to present the analyzed data.
Layer #7 – Managing and Governing Data
Nowadays, businesses have to comply with data management policies and standards, such as HIPAA, GDPR and others. Companies can implement their own data management flow or use tools like Apache Atlas or Cloudera Navigator to manage data lineage and access control.
Benefits of Real-Time Big Data Analytics
Let’s explore why businesses choose to invest in big data solutions. Here are the top 5 benefits of real-time big data analytics.
Benefit #1 – Immediate Insights with Accuracy
The immediate real-time insights of decent accuracy help automate decision-making without involving a human. This allows businesses to offer customers products based on their real-time behavior, spot negative tendencies fast, and much more.
Benefit #2 – Operational Efficacy With Cost Savings
Real-time big data analytics emphasize current operational gaps and potential areas for improvement. Its flexible tools and compelling features save from high investment in processing, storing, and analyzing large volumes of data.
Benefit #3 – Omnichannel Customer Experience
Big data solutions seamlessly connect a variety of channels, allowing customers to pick up where they left off on one channel and continue the experience on another. This way, real-time customer data analysis facilitates an exclusive opportunity to provide personalized, cohesive, integrated, and prompt services.
Benefit #4 – Better Risk Monitoring and Management
Concurrent and smart data processing help businesses identify, respond, and mitigate potential cyber threats, financial fraudulence and process efficiency decline in real-time.
Benefit #5 – Foster Innovation
The comprehensive data-driven reports derive emerging trends and prospects, enabling businesses to stay ahead. Machine learning algorithms keep improving, so early implementation of data management solutions will always keep your company on the verge of innovation.
The Bottom Line
Big data applications are scalable multi-layered solutions with complex architecture. Overall, real-time big data analytics make businesses more agile, competitive, and responsive to changing market conditions and customer needs.