Certified Big Data Engineering Course

BY
Analytixlabs

Know about the aspects of big data analytics and engineering and enhance your skills associated with big data with the certified big data engineering course.

Mode

Online

Duration

120 Hours

Fees

₹ 25000

Quick Facts

particular details
Collaborators IBM
Medium of instructions English
Mode of learning Self study, Virtual Classroom +1 more
Mode of Delivery Video and Text Based
Frequency of Classes Weekdays, Weekends
Learning efforts 8-10 Hours Per Week

Course overview

The certified big data engineering course is offered and developed by the leading data science institute and online education platform in collaboration with the International Business Machines Corporation. The course is organized for 210 hours which includes thirty classes, e-learning sessions, and eight to ten hours of every week for independent study.

In this program, the learners gain the highly demanded knowledge and skills of big data analytics that drives data processing and management in industries. This course enables the students to become big data professionals who are required in the fields like Information Technology and Information technology-enabled services. The experiential learning techniques with the projects, assignments and other practical exercises provide the students with a wholesome learning experience.

 In this certification program, the students will get an opportunity to receive dual certification partnered with IBM. The certified big data engineering course is a skill development program that also provides career guidance for the participants.

The highlights

  • Online mode
  • Virtual live sessions
  • Self-paced learning
  • 210 hours course
  • Thirty classes
  • Demo session
  • Placement guidance
  • Dual certification
  • Technical support

Program offerings

  • Course videos
  • Live sessions
  • Independent learning
  • Assignments
  • Capstone projects
  • Exercises
  • Case study
  • Doubt resolution
  • Technical support
  • Student loan
  • Job assistance
  • Mentorship
  • Course completion certificate
  • Dual certification
  • Free demo session

Course and certificate fees

Fees information
₹ 25,000

The certified big data engineering course training is available to the candidates in three versions with respective costs. The students who can’t afford the upfront payment can pay the course fee in installments or can apply for a student loan.

Certified big data engineering course fee structure

HeadAmount
Fully Interactive Live Online
₹ 30000 + taxes
Blended eLearning
₹ 25000 + taxes
certificate availability

Yes

certificate providing authority

Analytixlabs

Who it is for

The certified big data engineering course training is aimed at the professionals who belong to the sectors of Information Technology(IT) Information Technology Enabled Services, business intelligence manager, officials who work with databases like the data engineer, database administrator, machine learning engineer, and data architect. The course helps computer science graduates who are interested in learning about Hadoop for the job of a data engineer. This certified big data engineering course benefits the students by providing them certification in big data engineering which includes the applied Hadoop-Spark and cloud computing skills

Eligibility criteria

Certificate qualifying details

The students will receive the course completion certificate after submitting the assignments and projects for assessment without plagiarism in one year. The certified big data engineering course training also provides dual certification by partnering with IBM, for which the students have to pass the MCQ tests in 2 attempts.

What you will learn

Knowledge of big data Business sense Knowledge of cloud computing Data science knowledge Knowledge of artificial intelligence Machine learning Sql knowledge

The certified big data engineering course helps the students to understand and comprehend the concepts of business analytics. This course helps the students to get trained in big data and acquire technical skills with the help of SQL, NoSQL(MongoDB), and the Hadoop ecosystem. The learners will gain hands-on experience with the tools such as HDFS, Sqoop, Hive, Impala, Spark, and cloud computing technologies. The certified big data engineering course syllabus will also focus on aspects of advanced analytics, artificial intelligence, and machine learning models for predictive analytics associated with big data.

The syllabus

Building blocks for Big Data Engineering

Introduction to Linux
  • Role of Linux commands in Data Engineering
  • Introduction to Linux Environment
  • Overview of frequently used commands
Introduction RDBMS Concepts
  • Introduction to Relational Database management system. Why SQL?
  • A glance at the tool and its advantages and disadvantages
  • Understanding Schema, ERDs and Metadata
Introduction to MS SQL Server
  • What is SQL – A Quick Introduction
  • Installing MS SQL Server for windows
  • Overview of SQL Server Management Studio
  • Understanding basic database concepts
Data Based Objects Creation( DDL Commands )
  • Creating databases and tables. Understanding data types
  • Inserting values into the table
  • Altering table properties
  • Introduction to Keys and constraints
  • Creating, Modifying & Deleting Tables
  • Create Table & Create Index statements
  • Drop vs. Truncate statements
  • DDL Statements with constraints
  • Import and Export wizard to get the data in SQL server from excel files or delimited files
Data Manipulation (DML Commands)
  • Data Manipulation statements
  • Insert, Update & Delete statements
  • Select statement – Sub setting, Filters, Sorting. Removing Duplicates, grouping and aggregations etc
  • Operators, predicates and built in functions(Top, distinct, Limit)
  • Where, Group By, Order by & Having clauses
  • SQL Functions – Number, Text, Date, etc
  • SQL Keywords – Top, Distinct, Null, etc
  • SQL Operators -  Relational (single valued and multi valued), Logical (and, or, not), Use of wildcard operators and wildcard characters, etc
Accessing Data from Multiple Tables Using SELECT
  • Append and Joins
  • Union and Union All – Use & constraints
  • Intersect and Except statements
  • Joins - inner join, left join, right join, full join
  • Cross joins/cartisian products, self joins etc.
  • Inline views and sub-queries & it's types
  • Optimizing your work
  • Update operations with and without joins

Cloud & Cloud Based RBDMS for Data Engineering

Introduction to Data Engineering
  • What is data engineering for data science?
  • Key terminologies (Data Mart, Data warehouse, Data Lake, Data Ocean, ETL, Data Model, Schema, Data pipeline , downstream applications, etc…)
  • Introduction to software for this course
  • Cloud Based RDBMS Systems (Azure SQL Server, Amazon Redshift, GCP BigQuery, IBM DB2, Snowflake etc…)
  • Hadoop – Hive
  • Spark – Pyspark
  • Data Engineering pipeline – Key steps
Introduction to Cloud Computing for Data Engineering
  • Introduction to cloud computing for Data Engineering

Cloud & Cloud Based RBDMS for Data Engineering-RDBMS in Cloud Platforms-Advanced SQL

Overview of RDBMS Systems in Cloud
RDBMS vs. Cloud Based RDBMS Systems
  • Creating databases and tables in the cloud
  • Loading & Unloading data
  • Inserting values into the table
  • Altering table properties
Advanced SQL Concep
  • Conditions in SQL
  • Views
  • Transactions
  • Window functions
  • Creating Scalar SQL UDF, Stored Procedures in SQL
  • Crud operations using stored procedures

Big Data Engineering Using Cloud Based Hadoop - Hive-Impala

Introduction to Hadoop
  • Motivation for Parallel computing (Hadoop & Spark)
  • Hadoop Ecosystem & core components
  • The Hadoop Distributed File System - Concept of data storage
  • Explain different types of cluster setups(Fully distributed/Pseudo etc.)
  • HDFS Overview & Data storage in HDFS
  • Practice complete data loading and managing them using command line(Hadoop commands) & HUE
  • Map Reduce Overview(Traditional way Vs. MapReduce way)
Data Analysis Using Hive & Impala
  • Discuss the Hive data storage principle
  • Explain the File formats and supported by the Hive environment
  • Perform operations with data in Hive
  • Joining Tables, Dynamic Partitioning, Custom Map Reduce Scripts
  • Join datasets using a variety of techniques, including Map-side joins and Sort-Merge-Bucket joins
  • Use advanced Hive features like windowing, views and ORC files
  • Hive Persistence formats
  • Loading data in Hive - Methods
  • Serialization & Deserialization
  • Integrating external BI tools with Hadoop Hive
  • Use the Hive analytics functions (rank, dense_rank, cume_dist, row_number)
  • Use Hive to compute ngrams on Avro-formatted files
  • Impala & Architecture
  • How Impala executes Queries and its importance

Spark for Big Data Engineering (PySpark)

Introduction to Spark
  • Introduction to Apache Spark
  • Spark Architecture
  • Introduction to Spark Eco-system
  • Overview of Spark on a cluster
  • Spark Standalone Cluster
  • Spark on cloud
Spark : Spark in Practice
  • Invoking Spark Shell
  • Basic operations on Shell
  • Spark Context and Spark Properties
  • Persistence in Spark
  • Performing Some Basic Operations on Files in Spark Shell
RDD's & Spark Data Frames
  • Understanding & Loading data into RDD
  • Hadoop RDD, Filtered RDD, Joined RDD
  • Transformations, Actions and Shared Variables
  • Sequence File Processing
  • Overview of Spark Data frames
Introduction to Python for Spark (PySpark)
  • Introduction to Python for Spark (PySpark)
Spark SQL: Analysing Structured Data
  • Spark SQL Architecture
  • Analyze Spark SQL
  • Context in Spark SQL
  • Implement a sample example for Spark SQL
  • Analyze Hive and Spark SQL Architecture
  • Integrating hive and Spark SQL
  • Support for JSON and Parquet File Formats Implement Data Visualization in Spark
  • Loading of Data
  • Shared Variables: Broadcast Variables & Accumulators
Spark Streaming
  • Spark Streaming, its Architecture and abstraction
  • Different Transformations in Spark Streaming
Spark MLLib: Scalable Machine Learning on Spark
  • Introduction to MLLib
  • Data Types and working with vectors
  • Examples for usage of Spark MLLib
Spark: Best Practices
  • Best practice for coding spark jobs, Performance Fine tuning, handle external data sources
  • Overview on Lambda Architecture

Admission details

The admission procedure for the certified big data engineering course is done by registering online on the website of Analytixlab.

Step 1: Go to the certified big data engineering course page at the official website using the following link, https://www.analytixlabs.co.in/big-data-analytics-hadoop-spark-training-course-online

Step 2: Select the mode of learning and curriculum among the three options available.

Step 3: Click on the respective ‘Enroll Now’ link

Step 4: Fill in the relevant information on the registration form and complete the process.


Filling the form

On the registration page for the certified big data engineering course, the candidates will have to enter their name, phone number, email address, course name, and city name for enrollment.

How it helps

The certified big data engineering course helps the learners to gain industrially relevant skills and experience associated with big data engineering, Scalable machine learning on Spark software, the process of data blending, and data manipulation. The course equips the students with skills for large-scale analytics on cloud computing, the relational database management systems integration with Hadoop, and the methods for Spark streaming.

FAQs

Which institute offers the online certified big data engineering course?

Analytixlab has developed the course and provides it online for the students to upskill themselves in big data engineering.

What is the duration of the certified big data engineering course?

The course takes around 210 hours to complete in total.

How many classes and projects are included in the certified big data engineering course syllabus?

The course includes 30 classes and 11 projects for the students.

Does Analytixlab provide a loan facility for the certified big data engineering course fee?

Yes, the candidates who can’t afford the course fee can apply for a student loan.

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