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Apache Spark is a great tool for dealing large volume of data quickly. Apache Spark is one of the widespread unified analytics engines with the ability to combine SQL, Streaming, Machine Learning, and complex analytics. The story gets better when you get into the zone of real-time applications. If your business depends on making decisions quickly, you should certainly consider Apache Spark stack.

Learn about the business use cases of Spark with our Big Data Expert. Join us for a live and interactive webinar as we explain increasing number of Apache Spark business use cases so you can brag this new approach to this platform and explore how your business can use Spark to improve, build and scale big data applications.

Discussion Topics:

Implementation Approach for End-to-End Big Data Analytics Processing
Comparison of Apache Spark with other Popular Spark Alternatives
Live Demo and Runtime Comparison
Use Cases for the Spark ECO-System
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Apache Spark

SM Apache Spark
On-Demand Webinar

6 Core Planning Tactics and
Real-Time Use Cases with Apache Spark

Apache Spark &

Its Business Use Cases

Apache Spark is a great tool for dealing large volume of data quickly. Apache Spark is one of the widespread unified analytics engines with the ability to combine SQL, Streaming, Machine Learning, and complex analytics. The story gets better when you get into the zone of real-time applications. If your business depends on making decisions quickly, you should certainly consider Apache Spark stack.

Learn about the business use cases of Spark with our Big Data Expert. Join us for a live and interactive webinar as we explain increasing number of Apache Spark business use cases so you can brag this new approach to this platform and explore how your business can use Spark to improve, build and scale big data applications.

Join us
Reserve your Seat

AGENDA & SPEAKERS

What Can You Learn?

  • Implementation Approach for End-to-End Big Data Analytics Processing
  • Comparison of Apache Spark with other Popular Spark Alternatives
  • Live Demo and Runtime Comparison
  • Use Cases for the Spark ECO-System

Make the most of with the “next-big-thing” and set your organization’s IT action plan.

Muhammad Aneeq Yusuf

Muhammad Aneeq Yusuf

Big Data Lead
Join us
Reserve your Seat

Apache Spark

Apache Spark

Apache Spark for Faster and Better

Unified Engine for Big Data Processing

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With the advent of distributed data processing frameworks, big data analysis has become a reality. More and more organizations want to get more value and insights into the data they have. There is a gradual shift, where decision making in organizations is driven by data analytics. This data analysis need not be limited to existing data warehousing systems. Data can be retrieved from a variety of sources, including the existing warehouse and combined to derive new insights.

How Royal Cyber can help you make the transition?

  • Royal Cyber pioneer in enterprise solutions, in core doing consulting giving fair advantage to clients.
  • Experts in setting up clusters and enabling it to run with an existing Hadoop environment.
  • Assistance in setting up a data processing environment, in writing data processing routines to extract data from different sources, run analytics using spark libraries.
  • Specialists in creating a light weight application for visualizing the results.
What is CPQ?
About Spark

About Apache Spark

Apache® Spark™ is an open-source cluster computing framework with in-memory processing to speed analytic applications up to 100 times faster compared to technologies on the market today.

  • Apache spark runs much faster than apache Hadoop map reduce
  • Runs on any file system and the performance is good even on small datasets
  • Very good performance on iterative data analysis
  • Spark map reduce will replace Hadoop map reduce for data processing
  • Apache spark has libraries to stream data processing, machine learning, SQL query processing, and graph data processing
  • Companies want to make a transition to apache spark for its out of the box functionality

Framework for big data processing

  • The most distinguished framework for big data processing is apache Hadoop.
  • Vendors like cloudera, mapR, Hortonworks, IBM have this framework with some additions.
  • Apache Hadoop is extremely smart in processing distributed data using map reduce
    on the hdfs file system.
  • It has limitations when it needs to perform iterative computation over the data.
Big Data Processing