Big Data means tremendously huge data. Just for you to get an idea how huge it is, on an average day Facebook will have around 700+ terabytes of data, which is roughly 7,15,000+ Gigabytes of data. When calculated for a year this becomes roughly 250+ Petabytes of data (1 petabyte = 1024 Terabytes) i.e. roughly 2,55,500 Terabytes of data or 26,16,32,000 Gigabytes of data. Now imagine storing and processing all this data (more than 1000 Exabyte; 1 Exabyte = 1024 Petabytes) along with data from other such sources (which could all add up to zettabyte or yottabyte of data) in a single open source framework and that is Hadoop for you. This data could consist of more than Trillions of data of Billions of people from social media, banks, internet, mobile data etc. Hadoop Distributed Files System – HDFS (a software of Apache Software Foundation) provides software frameworks for storage and processing of Big Data. Learn more with SMEClabs, BIG DATA APACHE HADOOP taught in detail.
This Bigdata Apache Hadoop Spark Scala course from SMEClabs will make you ready to switch careers on big data Hadoop and spark. After watching this, you will understand about Hadoop, HDFS, YARN, Map reduce, python, pig, hive, oozie, sqoop, flume, HBase, No SQL, Spark, Spark sql, Spark Streaming.
Apache Spark is an open-source cluster computing framework for Hadoop community clusters. It qualifies to be one of the best data analytics and processing engines for large-scale data with its unmatchable speed, ease of use, and sophisticated analytics. Following are the advantages and features that make Apache Spark a crossover hit for operational as well as investigative analytics:
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