Immutability and RDD Interface in Spark are key concepts and it must be understood in detail. Spark defines an RDD interface with the properties that each type of RDD must implement. These properties include the RDD’s dependencies and information about data locality that are needed for the execution engine to compute that RDD. Since RDDs are statically typed and immutable, … read the rest
Spark In-Memory Persistence and Memory Management must be understood by engineering teams. Spark’s performance advantage over MapReduce is greatest in use cases involving repeated computations. Much of this performance increase is due to Spark’s use of in-memory persistence. Rather than writing to disk between each pass through the data, Spark has the option of keeping the data on the executors … read the rest
Spark Model of Parallel Computing and sometimes also called RDD is an important API. Spark Model of Parallel Computing internally uses RDD and part of Spark Core library.
Spark allows users to write a program for the driver (or master node) on a cluster computing system that can perform operations on data in parallel. Spark represents large datasets as RDDs—immutable, … read the rest
Why you should be worried about how Apache Spark works? To get the most out of Spark, it is important to understand some of the principles used to design Spark and, at a cursory level, how Spark programs are executed this article introduces the overall design of Spark as well as its place in the big data ecosystem. Spark is … read the rest
In this article, Apache Sqoop Introduction, we will primarily discuss why this tool exists. Apache sqoop is part of Hadoop Core project or part of Hadoop Ecosystem project.
Bigdata tools which we use for transferring data between Hadoop and relational database servers is what we call Sqoop. Sqoop primarily stands for Sql for Hadoop.
In addition, there are … read the rest
The Beginners Impala Tutorial covers key concepts of in-memory computation technology called Impala. It is developed by Cloudera. MapReduce based frameworks like Hive is slow due to excessive I/O operations. Cloudera offers a separate tool and that tool is what we call Apache Impala. This Beginners Impala Tutorial will cover the whole concept of Cloudera Impala and how this Massive … read the rest
Hadoop 3.0 or Bigdata jobs are in demand and in Hadoop 3.0 Interview Question article covers almost all the important topic including the reference link to other tutorials.
Hadoop 3.0 New Features Questions
What are the new features in Hadoop 3.0?
- Java 8 (jdk 1.8) as runtime for Hadoop 3.0
- Erasure Encoding for to reduce storage cost
- YARN Timeline Service
This short article talks and compare UNIX kernel shells, which many technical folks are confused of. The Unix operating system used a shell program called the Bourne Shell. Then, slowly, many other shell kernel were developed for different flavors of UNIX operating system. The following is some brief information about different shells:
- sh—Bourne shell
- csh—C shell
Hadoop 3.0 GPU : Hadoop is still behind high performance capacity due to CPUs’ limited parallelism, though. GPU (Graphical Processing Unit) accelerated computing involves the use of a GPU together with a CPU to accelerate applications to data processing on GPU cluster toward higher efficiency. However, GPU cluster has low level data storage capacity
Leveraged Hadoop 3.0 GPU Computing
MapReduce … read the rest
This deep dive article on “Hadoop 3.0 Erasure coding explained” will highlight how the erasure coding will help reducing 50% of storage overhead cost. The storage component (HDFS) of Hadoop 3.0 by default replicates each block 3 times (and could be higher based on configuration). Replication facilitates a simple and robust form of redundancy to protect against failure … read the rest