Learn Python, NumPy, Pandas, Scikit-learn, HDFS, ZooKeeper, Hive, HBase, NoSQL, Oozie, Flume, Sqoop, Spark, Spark RDD, Spark Streaming, Kafka, SparkR, SparkSQL, MLlib, Regression, Clustering, Classification, SVM, Random Forests, Decision Trees, Dimensionality Reduction, TensorFlow 2, Keras, Convolutional & Recurrent Neural Networks, Autoencoders, and Reinforcement Learning
Learn HDFS, ZooKeeper, Hive, HBase, NoSQL, Oozie, Flume, Sqoop, Spark, Spark RDD, Spark Streaming, Kafka, SparkR, SparkSQL, MLlib, and GraphX.
In this chapter, we learn the basics of Big Data which include various concepts, use-cases and understanding of the eco-system.
This chapter doesn't require any knowledge of programming or technology. We believe it is very useful for every to learn the basics of Big Data. So, jump in!
Happy Learning!
In this chapter, we learn the basics of Big Data which include various concepts, use-cases and understanding of the eco-system.
This chapter doesn't require any knowledge of programming or technology. We believe it is very useful for every to learn the basics of Big Data. So, jump in!
Happy Learning!
As everyone knows, Big Data is a term of fascination in the present-day era of computing. It is in high demand in today’s IT industry and is believed to revolutionize technical solutions like never before.
Upon learning the big data concepts, we will get a vivid picture of the need for clusters of machines (distributed systems), and appreciate the use of this architecture in solving critical problems associated with storing and processing humungous data. In addition, we will get an idea of system design concepts, which aid us in designing scalable and resilient systems - the most desirable kind of …
Spark Streaming is an extension of the core Spark API that enables scalable, high-throughput, fault-tolerant processing of live data streams. Learn Spark Streaming from the industry experts.
MapReduce is Framework as well as a paradigm of computing. By the way of map-reduce, we are able to break-down complex computation into distributed computing.
As part of this chapter, we are going to learning how to build MapReduce programmes using Java.
Please make sure you work along with the course instead of just sitting back and watching.
Happy Learning!
