Data Engineer (Hadoop) Course in Noida

Data Engineer Course

Ducat's Data Engineering Certification Training Course using Azure is designed by IT experts. In this training, you will be taught concepts like data warehousing, Hadoop, Databricks, Delta lake, Delta Live table, and many more with industry-relevant projects. This Data Engineer ( Hadoop) Course provides real-world experience in various sectors.

People often confuse Data Engineers and Big data Hadoop developers. Data Engineering technology is migrated with Microsoft Azure (cloud) and has some more additional technologies than Big data Hadoop developers, this is the basic difference between data engineers and Big data Hadoop developers.

Course Overview of Data Engineering

In this Ducat's Data Engineer (Hadoop) course, you will get a deep understanding of cloud services of Azure and AWS, data transformation, SQL, Spark, python and many more. You will be given real-world projects under the guidance of subject matter experts so that you can get the practical learning which will help you to become an expert in core concepts like pulling data from multiple sources, creating cloud data warehouses, creating production-ready ETL, Data modelling and many more.

WHY CHOOSE DUCAT FOR DATA ENGINEER COURSE?

Looking to unlock the exciting world of Big Data? Ducat's Data Engineer (Hadoop) course in Noida can be your launchpad! Here's why Ducat stands out for students like you:

Master in-demand skills: Gain expertise in Hadoop, a core framework for handling massive datasets. Learn data warehousing, data transformation, and tools...

What you will learn?

Hadoop Fundamentals: Understand the core concepts of Hadoop ecosystem, including HDFS (Hadoop Distributed File System) and MapReduce, essential for managing and processing large-scale data efficiently.
Data Ingestion and Storage: Learn techniques for ingesting various types of data into Hadoop clusters, and explore best practices for organizing and storing data effectively to optimize processing performance.
Data Processing with MapReduce: Gain proficiency in developing MapReduce programs to process and analyze massive datasets, enabling you to extract valuable insights and perform complex computations.
Hadoop Ecosystem Components: Explore additional components of the Hadoop ecosystem such as Hive, Pig, HBase, and Spark, and understand how to leverage these tools for different data processing and analysis tasks.Scalability and Performance Optimization: Master techniques for optimizing Hadoop cluster performance and scalability, including resource management, tuning configurations, and implementing parallel processing strategies to handle increasing data volumes efficiently.

Skill you will gain

Hadoop Administration
Data Warehousing
Big Data Analytics
Data Integration

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Data warehousing Concepts

Need for Data Warehouse DWH Characteristics Data Warehouse Architecture Data marts in DWH Data Mining Difference between Data Warehouse and Data Mart OLTP vs OLAP Dimension & Types of dimensions Junk dimensions

Hadoop

Hadoop Overview Hadoop Architecture Hadoop Installation Hadoop Components HDFS HDFS command

Databricks

Introduction to Databricks Databricks Architecture Databricks Concepts Creating and configuring clusters in Databricks Free Account and Free Subscription For Databricks How to create Databricks Cluster, Notebook & quick tour on notebook options

Beginner & Intermediate

Just Enough Python for Spark Spark Overview Spark Architecture & Internal Working Mechanism Spark components PySpark installation PySpark Data structure (RDD, Dataframe, Dataset) Pyspark utility command (DBUtils) Pyspark Read TSV Files and PIPE Seperated CSV Files Pyspark Read Parquet file Pyspark Read text file Pyspark Read Avro file Pyspark Read CSV file with multiple delimiters at different positions PySpark Write TSV file Pyspark: Handling Null - Part1 Pyspark Functions| Part 2 : Array_Intersect Pyspark Functions| Part 3 : Array_Except Pyspark Functions| Part 4 : Array_Sort

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At DUCAT, we are committed to providing inclusive educational opportunities to a diverse range of learners. While the specific prerequisites may vary depending on the course, the following eligibility criteria generally apply to all our training courses:

Age Requirement: There is no minimum age requirements, however, some courses may have specific age requirements, which will be clearly mentioned in their respective descriptions.

Educational Background: Most courses are open to all; advanced courses may have prerequisites, as specified in the course details.

Language Proficiency: Proficiency in English is recommended for an optimal learning experience. Course-specific language requirements may apply; consult course details.

Technical Requirements: A reliable internet-connected computer or mobile device is essential. Specific software and hardware requirements are detailed in course descriptions.

Commitment to Learning: Active participation, completion of assignments, and discussion involvement are vital for course success.

Payment and Fees: Some courses may have associated fees or tuition costs. Please check the individual course details for information on fees, scholarships, and payment options.

Please note that individual courses may have additional or specific eligibility criteria based on their content and objectives.

At DUCAT, we believe that education should be accessible to everyone, and we strive to accommodate a wide range of learners.

If you have any questions or need further clarification regarding eligibility criteria for a particular course, our support team is available to assist you.

Online Training : Online training is the most convenient option, as you can enrol from anywhere in the world.

Offline Training : Offline training is the traditional option, where you visit the training centre in person. Corporate enrollment is available for companies that want to enrol their employees in the course.

Hybrid Training : Hybrid training is best as it is as per student comfort where students can take training in both online or offline mode.

Q: What is Hadoop, and why is it important for data engineers?

A: Hadoop is an open-source framework for distributed storage and processing of large datasets. It is crucial for data engineers because it allows them to store and analyze massive amounts of data efficiently. Hadoop's ecosystem includes tools like HDFS for storage and MapReduce for processing, making it essential in big data processing and analytics.

Q: What skills do I need to become a data engineer with expertise in Hadoop?

A: To become a data engineer specializing in Hadoop, you should have a strong foundation in programming languages like Java or Python. Additionally, you need to be proficient in Hadoop-related technologies, including HDFS, MapReduce, HBase, Hive, Pig, and Spark. Familiarity with data modeling, ETL (Extract, Transform, Load) processes, and distributed systems is also essential.

Q: How can I learn Hadoop and related technologies?

A: You can learn Hadoop and related technologies through various means, including online courses, tutorials, and certifications. Some popular online platforms like Coursera, edX, Udemy, and Pluralsight offer courses on Hadoop and big data technologies. Additionally, you can explore Hadoop's official documentation and community forums to gain practical knowledge.

Q: Are certifications important for a career in Hadoop and data engineering?

A: Certifications can be beneficial in demonstrating your expertise in Hadoop and data engineering to potential employers. Some popular Hadoop-related certifications include the Cloudera Certified Data Engineer (CCDE), Hortonworks Data Platform (HDP) certifications, and Databricks certifications. Earning certifications can boost your resume and increase your chances of landing a job or advancing in your career.

Q: What are the job prospects for data engineers with Hadoop skills?

A: Data engineers with Hadoop expertise are in high demand as organizations continue to deal with massive amounts of data. You can find job opportunities in various industries, including finance, healthcare, e-commerce, and technology. Typical job roles include Hadoop Developer, Big Data Engineer, Data Engineer, and ETL Developer. Salaries for data engineers with Hadoop skills are often competitive, making it a promising career path.

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