Real Time Analytics

    Empower your Data with “Time Critical”

              Situational Awareness.

Real Time Analytics

Empower your Data with “Time Critical”

 Situational Awareness.

What’s happening to your business, product, or customer behavior in real time?

Do you have a way to view and analyze what is going on within your business at any given time? Companies like Facebook and Twitter have built systems to measure petabytes of real-time data, and we can apply those same theories to produce actionable metrics to help you make better business decisions. In the modern era of data science, we know that billions of devices are already connected to the internet, with more connecting each day. With the evolution of the Internet of Things (IOT), organizations need to prepare for an eruption of data from new sources such as smart meters, sensors and wearable medical devices. Real time analytics will leverage these devices to deliver insights and automated actions within milliseconds of a trigger.
There are many technologies that support real-time analytics, such as processing in memory (PIM), in-database analytics, data warehouse appliances, in-memory analytics and massively parallel programming (MPP). This variety of technologies and choices creates an upfront challenge for organizations to decide which technology

should be leveraged to complete each project. At DataFactZ, we have built an extensive practice around sophisticated analytics solutions. Our team of big data engineers starts by designing a scalable real-time architecture that is able to extract insights based on information from past to present, within a timeframe as short as a few milliseconds. Additionally, the system design will scale up seamlessly as volume and variety of data increases. We design a real-time analytics system with the following properties for your business:

  • Robustness
  • Fault tolerance
  • Low-latency reads and updates
  • Incremental analytics and learning
  • Scalability

Hadoop-like batch processing systems have matured enough to serve as an excellent choice for high-throughput system processing and large volumes of data. However, in domains where

we need to make faster decisions, Hadoop is not suitable. Real-time systems provide low-latency updates, but perform analytics only on small batches of data. For better prediction capabilities, we need to develop models based on all data which can only be done in batch mode. Our solution merges both batch-oriented and real-time architectures in a creative way to meet all of your needs. For true real-time analytics, data must be processed at the speed equal to or faster than the speed at which it arrives. To meet the challenges of scalable real-time analytics, stream-oriented data processing architectures have evolved.
Figure 1 below shows the building blocks of a stream-oriented real-time analytics architecture. Streaming data may be collected from various sources by using data source specific connectors which move and receive data from the source to the queuing system. Data is then buffered to be consumed by the stream processing engine. The queuing system is a high-throughput, low latency system which provides high availability and fail-over capabilities.

image (3)

Building Blocks of a Real-Time Stream Data Processing Architecture

Designing a scalable real-time analytics model is a step-by-step process involving multiple tools and technologies. There is no single tool that provides a complete solution.
One approach to solution development is Lambda architecture. This approach takes advantage of both batch and stream-processing architecture to provide a balance among latency, throughput and fault-tolerance. It uses batch processing to provide comprehensive and accurate analytics on entire data, while simultaneously using real-time stream processing to provide incremental analytics on the continually arriving data. The two analytics results are then merged to generate a comprehensive overview of insights.
Lambda architecture describes a system consisting of three layers:

Batch layer – for batch processing of all data
Speed layer – for real-time processing of streaming data
Serving layer – for responding to queries

The below diagram illustrates an example of designing a scalable real-time analytics architecture.

real-time-analytics-2

Lambda Architecture

See Whitepaper – Lambda and Apache Spark

Designing scalable, real-time analytics systems requires the integration of several components. We have described the major building blocks of such a system by combining the features of both batch processing and stream processing using lambda architecture concepts. A scalable, real-time analytics architecture can be realized through the integration of the following open source components:

1. Apache Flume for data aggregation and transformation
2. Apache Kafka as the distributed messaging layer
3. Apache HDFS for batch layer incoming data store
4. Apache Spark for batch processing logic
5. Elephant DB or Druid for batch layer output store
6. Spark Streaming for speed layer
7. Apache Cassandra or Druid for speed layer store
8. Druid for serving layer store.