A big data engineer is an information technology (IT) professional who is responsible for designing, building
testing and maintaining complex data processing systems that work with large data sets. This type of data
specialist aggregates, cleanses, transforms and enriches different forms of data so that downstream data
consumers — such as business analysts and data scientists — can systematically extract information.
Big data is a label that describes massive volumes of customer, product and operational data, typically in the
terabyte and petabyte ranges. Big data analytics can be used to optimize key business and operational use cases,
mitigate compliance and regulatory risks and create net-new revenue streams.

Fundamentals of Big Data

Hadoop Fundamentals

SQL-On-Hadoop

MapReduce In Depth

SQL-On-Hadoop

Ingesting Data into Hadoop

Intro to Python Programming

Intro to Apache Spark

Project 1: Data Ingestion from RDBMS into HIVE (ORC File)

Project 2: Data Ingestion Streaming Data Using Kafka into Hadoop/Hive

Project 3: Data Analysis & Stream Processing using Spark 1

Project 4: Data Analysis & Stream Processing using Spark & Hive 2

Project 5: Data Visualization Using Apache

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You'll rely on your programming and problem-solving skills to create scalable solutions. As long as there is data to process, data engineers will be in demand.

هذه الدورة مقدمة من :

Design, construct and maintain large-scale data processing systems. This collects data from various data sources -- structured or not

Store data in a data warehouse or data lake repository

Handle raw data using data processing transformations and algorithms to create predefined data structures Deposit the results into a data warehouse or data lake for downstream processing

Transform and integrate various data into a scalable data repository (such as a data warehouse, data lake cloud)

Understand different data transformation tools, techniques and algorithms

Implement technical processes and business logic to transform collected data into meaningful and valuable information. This data should meet the necessary quality, governance and compliance considerations for operational and business usage to be considered trustable

Understand operational and management options, as well as the differences between data repository structures, massively parallel processing (MPP) databases and hybrid cloud

Evaluate, compare and improve data pipelines. This includes design pattern innovation, data lifecycle design data ontology alignment, annotated data sets and elastic search approaches

Prepare automated data pipelines to transform and feed the data into dev, QA and production environments

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