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In this presentation, we will be learning about Big Data & Hadoop, challenges of Big Data, what is Spark, job roles in Big Data, companies hiring in 2020 and lastly how Simplilearn can help you in achieving your Big Data job role. With our advanced technology today, machines have become capable of acquiring and processing large sets of data. Big data is the term used to define large amounts of data that can be processed to reveal patterns, trends, and associations, especially relating to human behavior and interactions. We will be covering the below topics in this Big Data & Hadoop live session:<br>1. What is Big Data?<br>2. Challenges of Big Data<br>3. What is Hadoop?<br>4. What is Spark?<br>5. Job roles in Big Data<br>6. Companies hiring in 2020<br>7. How can Simplilearn help you?<br><br>What is this Big Data Hadoop training course about?<br>The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.<br><br>What are the course objectives?<br>This course will enable you to:<br>1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark<br>2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management<br>3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts<br>4. Get an overview of Sqoop and Flume and describe how to ingest data using them<br>5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning<br>6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution<br>7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations<br>8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS<br>9. Gain a working knowledge of Pig and its components<br>10. Do functional programming in Spark<br>11. Understand resilient distribution datasets (RDD) in detail<br>12. Implement and build Spark applications<br>13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques<br>14. Understand the common use-cases of Spark and the various interactive algorithms<br>15. Learn Spark SQL, creating, transforming, and querying Data frames<br><br>Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
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Today’s Agenda What is Big Data? Challenges of Big Data What is Hadoop? What is Spark? Job roles in Big Data Companies hiring in 2020 How can Simplilearn help
What is Big Data?
What is Big Data? Data has evolved in the last decade like never before. Lots of data is being generated each day in every business sector
What is Big Data? Data has grown vastly over the last decade and is expected to reach 175zettabytes in 2025 according to the International Data Corporation (IDC) 1 ZB = 1021 bytes
What is Big Data? Massive amount of data which cannot be stored, processed and analyzed using the traditional ways is known as Big data! Process Store Analyze Used to
Challenges of Big Data 1. Enormous amount of data is being generated every day Since data is growing at a rapid rate, storing it is a challenge. Also, unstructured data cannot be stored in traditional databases
Challenges of Big Data 2. Processing and analyzing big data is a major challenge Organizations don’t just store their big data, they use that data to achieve business goals. Processing and extracting insights from big data takes time
What is Hadoop? Hadoop is a framework that manages big data storage in a distributed way and processes it parallelly
What is Hadoop? Hadoop Distributed File System (HDFS) stores big data in a distributed manner and hence solves the issue of storing rapidly increasing data
What is Hadoop? Hadoop MapReduce is responsible for processing big data parallelly. This helps you process and analyze big data faster
What is Spark? Apache Spark is an open-source data processing engine to process, manipulate, and analyze data in real-time across various clusters of computers using simple programming constructs
Job roles in Big Data Big Data is a vast field, you can look into various job profiles in this field. Let’s have a look at the below profiles:
Job roles in Big Data Big Data is a vast field, you can look into various job profiles in this field. Let’s have a look at the below profiles: Big Data Engineer Hadoop Developer Big Data Architect Spark Developer
Job roles in Big Data Big Data is a vast field, you can look into various job profiles in this field. Let’s have a look at the below profiles: Big Data Engineer Hadoop Developer Big Data Architect Spark Developer
Who is a Big Data Engineer? Big Data Engineers are professionals who develop, maintain, test and evaluate a company’s big data infrastructure Develop Maintain Test Evaluate Integrate
Responsibilities of a Big Data Engineer Design, implement, verify and maintain software systems Build highly scalable robust systems for ingestion and processing of data Carry out ETLprocess by extracting data from one database, transforming it and loading it to another data store Research and propose new methods to acquire data, improve dataqualityand efficiency of the system
Responsibilities of a Big Data Engineer Building a data architecture in such a way that it meets all the business requirements Generating a structured solution by integrating several programming languages and tools together Mining data from various sources to build models that can reduce complexity and increase the efficiency of the whole system Work with other teams, including data architects, data analysts, and data scientists
Skills to become a Big Data Engineer Programming ETL and warehousing tools In-depth knowledge on DBMS and SQL Hadoop based analytics Knowledge on OS Real-time processing frameworks Data mining and modeling
Skills to become a Big Data Engineer Programming ETL and warehousing tools In-depth knowledge on DBMS and SQL Hadoop based analytics Knowledge on OS Real-time processing frameworks Data mining and modeling
Skills to become a Big Data Engineer Programming Programming ETL and warehousing tools In-depth knowledge on DBMS and SQL Programming skills is one of the most important aspects required to become a Big Data Engineer. Hands-on experience in any programming language is always a benefit Hadoop based analytics Knowledge on OS Java Python C++ Real-time processing frameworks Data mining and modeling
Skills to become a Big Data Engineer Programming ETL and warehousing tools In-depth knowledge on DBMS and SQL Hadoop based analytics Knowledge on OS Real-time processing frameworks Data mining and modeling
Skills to become a Big Data Engineer Programming In-depth knowledge on DBMS and SQL ETL and warehousing tools In-depth knowledge on DBMS and SQL Data Engineers need to have a good understanding of how data is managed and maintained in a database. So, they need to know how to write SQL queries for any RDBMS Hadoop based analytics Knowledge on OS Real-time processing frameworks Data mining and modeling
Skills to become a Big Data Engineer Programming ETL and warehousing tools In-depth knowledge on DBMS and SQL Hadoop based analytics Knowledge on OS Real-time processing frameworks Data mining and modeling
Skills to become a Big Data Engineer Programming ETL and warehousing tools ETL and warehousing tools In-depth knowledge on DBMS and SQL As a Big Data Engineer, you need to know how to construct and use a data warehouse and carry out ETL operations. It helps you aggregate unstructured data from one or more sources and analyze it for better business Hadoop based analytics Knowledge on OS Real-time processing frameworks Data mining and modeling
Skills to become a Big Data Engineer Programming ETL and warehousing tools In-depth knowledge on DBMS and SQL Hadoop based analytics Knowledge on OS Real-time processing frameworks Data mining and modeling
Skills to become a Big Data Engineer Programming Knowledge on OS ETL and warehousing tools In-depth knowledge on DBMS and SQL Good knowledge of Unix, Linux, and Windows is necessary as most tools are based on these systems due to their unique demands for root access to hardware and operating system functionality Hadoop based analytics Knowledge on OS Real-time processing frameworks Data mining and modeling
Skills to become a Big Data Engineer Programming ETL and warehousing tools In-depth knowledge on DBMS and SQL Hadoop based analytics Knowledge on OS Real-time processing frameworks Data mining and modeling
Skills to become a Big Data Engineer Programming Hadoop based analytics ETL and warehousing tools In-depth knowledge on DBMS and SQL Strong understanding of Apache Hadoop-based technologies are frequent requirements in this space, with knowledge of HDFS, MapReduce, HBase, Pig, and Hive are often considered a necessity Hadoop based analytics Knowledge on OS Real-time processing frameworks Data mining and modeling
Skills to become a Big Data Engineer Programming ETL and warehousing tools In-depth knowledge on DBMS and SQL Hadoop based analytics Knowledge on OS Real-time processing frameworks Data mining and modeling
Skills to become a Big Data Engineer Programming Real-time processing frameworks ETL and warehousing tools In-depth knowledge on DBMS and SQL Big Data Engineers often deal with vast volumes of data, so they need an analytics engine like Spark for large-scale real-time data processing Hadoop based analytics Knowledge on OS Real-time processing frameworks Data mining and modeling
Skills to become a Big Data Engineer Programming ETL and warehousing tools In-depth knowledge on DBMS and SQL Hadoop based analytics Knowledge on OS Real-time processing frameworks Data mining and modeling
Skills to become a Big Data Engineer Programming Data mining and modeling ETL and warehousing tools In-depth knowledge on DBMS and SQL Data Engineers examine massive pre-existing data to discover patterns and new information to build predictive models for business Hadoop based analytics Knowledge on OS Real-time processing frameworks Data mining and modeling
Avg Salary of a Big Data Engineer $102,864 p.a. Rs 7,26,000 p.a. Source: Glassdoor
Big Data is a vast field, you can look into various job profiles in this field. Let’s have a look at the below profiles: Job roles in Big Data Big Data Engineer Hadoop Developer Big Data Architect Spark Developer
Hadoop Developers takes care of the coding and programming of Hadoop applications. The position is similar to that of a Software Developer Who is a Hadoop Developer?
Skills to become a Hadoop Developer Knowledge of Hadoop ecosystem and its components – HBase, Pig, Hive, Sqoop, Flume, Oozie, etc. Data modelling experience with OLTP and OLAP. Should have basic knowledge of SQL, and database structures. Basic knowledge of popular ETL tools like Pentaho, Informatica, Talend, etc. Experience in writing Pig Latin and MapReduce jobs
Avg Salary of a Hadoop Developer $76,526 p.a. Rs 4,57,000 p.a. Source: Glassdoor
Big Data is a vast field, you can look into various job profiles in this field. Let’s have a look at the below profiles: Job roles in Big Data Big Data Engineer Hadoop Developer Big Data Architect Spark Developer
Spark Developers are professionals responsible for creating spark jobs using Scala/Python for data transformation and aggregation. They design data processing pipelines and write analytics code Who is a Spark Developer?
Skill to become a Spark Developer Knowledge of Spark and its components such as Spark Core, Spark Streaming, Spark MLlib, etc. is important. Knowledge of Scala and scripting languages like Python or Perl. Basic knowledge of SQL queries and database structures. Good understanding of Linux and its commands.
Avg Salary of a Spark Developer $81,149 p.a. Rs 5,87,500 p.a. Source: Glassdoor