SMEC - Big Data Analyst Course
Big Data Course – 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.
- Data Analytics: Fundamentals
- Data Analytics: The Impact of Statistics
- SQL
- Tableau: Data Visualization
- Python For Data Analysis
- Python: Data Visualization
- Numpy: Machine Learning & Scientific Computing
- Pandas: Real-World Data Analysis
- Data Analytics with R
- Apache Spark: Next-Generation Big Data Framework
- Online Practice Labs
- Certification
- No Cost EMI Option
- Dedicated Student Mentor
- 24/7 Support
- Industry-grade Projects
- Self-Paced Videos
- 60+ Industry Projects
- What is Big Data?
- What is Spark?
- Why Spark?
- Spark Ecosystem
- Why Scala?
- Hello Spark – Hands on
- Big Data hadoop Developer
- Developer - Big Data/ Hadoop /devops/ Cloud Platform
- Hadoop Developer - Java/big Data
- Big Data/ Hadoop Developer/ Architect
- You will learn about Hadoop, eco system, tools and spark
- Big Data Hadoop Development
- Read data from persistent storage and load it into Apache Spark
- Manipulate data with Spark and Scala
- Express algorithms for data analysis in a functional style
- Recognize how to avoid shuffles and re computation in Spark
- People looking to advance their career in Data Engineering, Big Data, Hadoop, Spark
- Hadoop python spark scala hive pig oozie sqoop flume kafka
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.
Why Spark?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:
- The programs developed over Spark run 100 times faster than those developed in Hadoop MapReduce.
- Spark compiles 80 high-level operators.
- Spark Streaming enables real-time data processing.
- GraphX is a library for graphical computations.
- MLib is the machine learning library for Spark.
- Primarily written in Scala, Spark can be embedded in any JVM-based operational system, at the same time can also be used in REPL (Read, Evaluate, Process and Load) way.
- It has powerful caching and disk persistence capabilities.
- Spark SQL allows it to proficiently handle SQL queries
- Apache Spark can be deployed through Apache Mesos, Yarn in HDFS, HBase, Cassandra, or Spark Cluster Manager (Spark’s own cluster manager).
- Spark simulates Scala’s functional style and collections API, which is a great advantage to Scala and Java developers.