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.
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.
