Career Path - Data Engineer
Acquire key data engineering skills, build robust data pipelines, scalable solutions architecture for efficient data processing, integration, analysisPreview Career Path - Data Engineer course
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Courses included in Data Engineer Career Path Program
1) SQL Programming with MYSQL Database
2) SAS BI and Data Integration
3) SAP Data Services (BODS)
4) Talend
Who is Data Engineer
Data engineers are responsible for finding trends in data sets and developing algorithms to help make raw data more useful to the enterprise.A data engineer transforms data into a useful format for analysis. Imagine that you’re a data engineer working on a simple competitor to Uber called Rebu. Your users have an app on their device through which they access your service. They request a ride to a destination through your app, which gets routed to a driver, who then picks them up and drops them off.
Roles & responsibilities of a Data Engineer
The data engineers design, build and install the data systems. These systems fuel machine learning and AI analytics. They also develop information processes for a whole host of data tasks. These include data acquisition, data transformation, and data modelling, among others.
The Data Architect: Their work allows data systems to ingest, integrate, and manage all the required sources of data for business insights and reporting. The work of a data architect may need in-depth knowledge of SQL, NoSQL, and XML, among other systems and tools
The Database Administrator: Database administrators help design and maintain database systems. They ensure that database systems function seamlessly for all users in an organization.
The Data Engineer: Data engineers understand several programming languages used in data science. These include the likes of Java, Python, and R. They know the ins-and-outs of SQL and NoSQL database systems. They also understand how to use distributed systems such as Hadoop. Having such a wide expanse of knowledge allows them to work with data architects, database administrators, and data scientists.
Uplatz provides this extensive Data Engineer Career Path training to make you well versed with the most important tools & technologies used in data engineering space.
Course/Topic 1 - SQL Programming with MySQL Database - all lectures
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In this video get an in-depth introduction to the terminology, concepts, and skills you need to understand database objects, administration, security, and management tools. Plus, explore T-SQL scripts, database queries, and data types
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In this video learn basic of SQL Programming and overview the SQL basic commands and how we use these commands in SQL Programming. This SQL tutorial will teach you basics on how to use SQL in MySQL, SQL Server, MS Access, Oracle, Sybase, Informix, Postgres, and other database systems.
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In this video we talk about DDL (DATA DEFINATION LANGUAGE) and also cover all the basic techniques of DDL.In this video we will learn about the SQL commands – DDL, DML, DCL; SQL Constraints – Keys, Not Null, Check , Default, and also MYSQL Hands-on and basic Querying
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In this video session we learn SQL commands and how to use these commands like select command, insert command, delete command etc. In this video we will learn about hands-on experience on the terminal, creating database, Tables and manipulating data.
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In this video we learn about SQL Basic and Aggregate Functions and also cover different functions of SQL. This tutorial teaches us about clauses and the update command. We will also cover making records, updating and modifying rows.
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In this session we talk about SQL Regular Expression and we also cover all techniques of SQL Regular Expression.This tutorial teaches us about clauses and the update command. We will also cover making records, updating and modifying rows and EML commands.
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In this video we learn about SQL Comparison Clauses and how we use Comparison Clauses in SQL. This tutorial covers Comparison Operators by relating values by a mathematical symbol which is used to compare two values. Learn about comparison operators result - TRUE, FALSE, or UNKNOWN
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In this session we learn about SQL String and also cover all types of string in SQL and how we can use SQL Strings. In this video we will learn about the basic string functions such as concat_ws, file format, and insert function, L-case, u case, and lower case. We will also learn about basic functions such as upper functions.
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In this session we cover advance level string function and also cover all different commands we use in SQL String Function. This video is a sequel for string functions tutorial. In this tutorial we will learn few most useful string functions such as spaces and null issue as well, L-Pad command.
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In this SQL String function part 3 we learn select Repeat function and Select Replace function and also cover different between Select Repeat function and Select Replace function. This tutorial is another sequel to string functions, however, these functions are used less and not used that frequently. We will further learn here about the repeat function, absolute function, ceiling, and floor and down functions.
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In this session we learn about SQL Numeric Functions and how we use Numeric functions in SQL. In this video, we will be covering numerical functions. Learn about the basic date functions and also about truncate functions.
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In this video session we learn about SQL Numeric Function and also cover the basic functionality of SQL Numeric Function. SQL Data Functions. In this video we will learn about few more Date functions. We will further look into the day function option as well. This tutorial covers basic querying over a single table.
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: In this video we talk about SQL Joins Introduction and Demonstration and basic join’s function and how to make table using joins. In this tutorial learn about joints in SQL. This tutorial teaches us how to connect two different tables with joints. We will also cover the topic of querying two or more tables and about subquery .
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In this lecture last session we talk about MySQL Workbench and procedures and Views and MySQL Workbench functionality. In this tutorial learn about SQL in automating things. This tutorial covers stroll procedure, functions and views which are helpful for automation purposes in SQL.
Course/Topic 2 - SAS Business Intelligence - all lectures
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This tutorial teaches you the integrated platform for delivering enterprise intelligence. This platform, which we call the SAS Enterprise Intelligence Platform, optimally integrates individual technology components within your existing IT infrastructure into a single, unified system.
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This session teaches the change management feature enables a team of SAS Data Integration Studio users to work simultaneously with a set of related metadata and avoid overwriting each other's changes. With change management, most users are restricted from adding or updating the metadata in a change-managed folder in the Folders tree.
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This teaches you the Data marts which are small slices of data warehouse. This module is a collection of tips on how to run your data mart implementation project Planning a Data Warehouse, Exercises
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This Help you to Learn how to build a data mart during SAS BI training, starting from reviewing a case study. Review of the Case Study, Define the Source Data, what are the Target Tables in SAS BI, Load the Target Tables, Exercises
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In this session, you will learn the On-Line Analytical Processing (or OLAP) has long been part of the data storage and exploitation strategy for SAS professional. Take an overview on OLAP in this module of SAS BI Training. What Is OLAP, Building an OLAP Cube in SAS BI, Solutions to Exercises
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This tutorial is designed to give you a good idea about SCD, its dimensions, load transformation and Lookup transformation. Defining Slowly Changing Dimensions in SAS BI How to use SAS BI SCD Type 2 Loader Transformation Using the Fact Table Lookup Transformation
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This session teaches you how to schedule data integration studio jobs during SAS BI training. Scheduling SAS Data Integration Studio Jobs
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In this session you will understand about the online analytical processing concepts, building an OLAP cube with SAS OLAP Cube Studio, building an information map from a SAS OLAP cube
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This video teaches you about the introduction to SAS Visual BI and exploring the SAS integration with JMP
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This tutorial helps you to Reviewing the platform for SAS Business Analytics and reviewing the course environment
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This video teaches you about the SAS Stored Process concepts, creating a stored process from a SAS Enterprise Guide project creating a stored process from a SAS program, creating stored process parameters, creating a stored process to provide a dynamic data source
Course/Topic 3 - SAP Data Services (BODS) - all lectures
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SAP BO Data Services consists of a UI development interface, metadata repository, data connectivity to source and target system and management console for scheduling of jobs. This introductory tutorial gives a brief overview of the features of SAP BODS and how to use it in a systematic manner.
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In this beginner's SAP BODS tutorial, you will learn, History of SAP BODS, SAP Data Services Advantages and the disadvantages of SAP BODS.
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SAP BODS is an ETL tool for extracting data from disparate systems, transform data into meaningful information, and load data in a data warehouse. It is designed to deliver enterprise-class solutions for data integration, data quality, data processing and data profiling.
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Data Services Designer is a developer tool, which is used to create objects consisting of data mapping, transformation, and logic. It is GUI based and works as a designer for Data Services.
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SAP BO Data Services (BODS) is an ETL tool used for data integration, data quality, data profiling and data processing. It allows you to integrate, transform trusted data-to-data warehouse system for analytical reporting.
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This tutorial will help all those students who want to create their own local repository, configure a job server, start basic job development and execute the job to extract data from source systems and load the data to target systems after performing transformations, look-ups and validations.
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This tutorial will help all those readers who want to create their own local repository, configure a job server, start basic job development and execute the job to extract data from source systems and load the data to target systems after performing transformations, look-ups and validations.
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Learn SAP Business Objects Data Services from basic concepts to advanced concepts starting from introduction, architecture, data services, file formats, data loading, etc.
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SAP BODS (Business Object Data Services) is an SAP DWH (Data Warehouse) product, where DWH is an enterprise level centralized reporting system. Data services is an end-to-end data integration, Data management, Test analysis software.
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Before you start this SAP BODS tutorial, you should have a basic knowledge of SAP system, RDBMS, Data warehouse and Business Intelligence (BI).
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SAP Bods training tutorials as per syllabus wise so beginners can easily learn SAP Business Object Data Services (Bods) step by step with real time project scenarios.
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SAP BODS combines industry data quality into one platform. BODS provides a single environment for development, run-time, management, security, and data connectivity.
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SAP BODS is an ETL tool that delivers a single enterprise-class solution for data integration, data quality, and data profiling that permits you to integrate, transform, improve, and provide trusted data that supports important business processes and enables sound decisions.
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It provides a GUI that allows us to efficiently produce a job that mine data from various sources, convert that data to meet the business requirements of an organization, and load data into a single place.
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SAP BO Data Services (BODS) is an ETL tool used for data integration, data quality, data profiling and data processing. It allows you to integrate, transform trusted data-to-data warehouse system for analytical reporting.
Course/Topic 4 - Talend - all lectures
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Lecture 1 - Talend Introduction
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Lecture 2 - Architecture and Installation - part 1
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Lecture 3 - Architecture and Installation - part 2
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Lecture 4 - Architecture and Installation - part 3
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Lecture 5 - File - Java - Filter Components
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Lecture 6 - tAggregateRow - tReplicate - tRunJob Components - part 1
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Lecture 7 - tAggregateRow - tReplicate - tRunJob Components - part 2
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Lecture 8 - Join Components - part 1
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Lecture 9 - Join Components - part 2
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Lecture 10 - Sort Components
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Lecture 11 - Looping Components
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Lecture 12 - Context - part 1
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Lecture 13 - Context - part 2
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Lecture 14 - Slowly Changing Dimensions (SCD)
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Lecture 15 - tMap Components - part 1
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Lecture 16 - tMap Components - part 2
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Lecture 17 - tMap Components - part 3
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Lecture 18 - tMap Components - part 4
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Lecture 19 - Talend Error Handling
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Lecture 20 - Audit Control Jobs
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Lecture 21 - How to use tJAVA components with scenario
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Lecture 22 - Talend Big Data Hadoop Introduction and Installation
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Lecture 23 - Talend HIVE Components - part 1
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Lecture 24 - Talend HIVE Components - part 2
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Lecture 25 - Talend HDFS Components
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Lecture 26 - Talend TAC
Course/Topic 5 - API Design & Development - all lectures
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In this lecture session we learn about basic introduction to API Design and development with RAML and also talk about some key features of API design with RAML.
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In this lecture session we learn about data formats and authentication of API design and development with RAML and also talk about the importance of RAML in API design and development.
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In this lecture session we learn about how we start designing API and also talk about basic resources and method of API design and development in RAML.
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In this lecture session we learn about API design center and features of API and also talk about some function of API design center in brief.
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In this tutorial we learn about API best practices is to Provide language-specific libraries to interface with your service and also talk about features of API design and development with RAML.
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In this tutorial we learn about Schemes define which transfer protocols you want your API to use. If your API is enforced by an API Connect gateway, only the HTTPS protocol is supported and also talks about features of API security schemes.
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In this tutorial we learn about API Designer provides a visual or code-based guided experience for designing, documenting, and testing APIs in any language and also talk about the importance of API design principles in brief.
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In this lecture session we learn about RESTful API Modeling Language (RAML) makes it easy to manage the API lifecycle from design to deployment to sharing. It's concise and reusable; you only have to write what you need to define and you can use it again and again.
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In this lecture session we learn about RESTful API Modeling Language (RAML) is a YAML-based language for describing RESTful APIs. It provides all the information necessary to describe RESTful or practically RESTful APIs and also talk about the importance of API design and development with RAML.
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In this lecture session we learn about RAML stands for RESTful API Modeling Language. It's a way of describing practically-RESTful APIs in a way that's highly readable by both humans and computers. We say "practically RESTful" because, today in the real world, very few APIs today actually obey all constraints of REST.
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In this lecture session we learn about RAML (RESTful API Modeling Language) provides a structured, unambiguous format for describing a RESTful API. It allows you to describe your API; the endpoints, the HTTP methods to be used for each one, any parameters and their format, what you can expect by way of a response and more.
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In this lecture session we learn about The RAML specification (this document) defines an application of the YAML 1.2 specification that provides mechanisms for the definition of practically-RESTful APIs, while providing provisions with which source code generators for client and server source code and comprehensive user documentation can be created.
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In this tutorial we learn about RESTful API Modeling Language (RAML) is a YAML-based language for describing RESTful APIs. It provides all the information necessary to describe RESTful or practically RESTful APIs.
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In this lecture session we learn about API is the acronym for Application Programming Interface, which is a software intermediary that allows two applications to talk to each other.
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In this lecture session we learn about RAML can be used in a multitude of ways: to implement interactive PAI consoles, generate documentation, describing an API you are planning to build, and more. Despite the name, RAML can describe APIs that do not follow all of the REST rules (hence why it's referred to as "practically RESTful").
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In this lecture session we learn about API architecture refers to the process of developing a software interface that exposes backend data and application functionality for use in new applications.
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In this lecture session we learn about RAML (RESTful API Modeling Language) provides a structured, unambiguous format for describing a RESTful API. It allows you to describe your API; the endpoints, the HTTP methods to be used for each one, any parameters and their format, what you can expect by way of a response and more.
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In this session we learn about RESTful API Modeling Language (RAML) is a YAML-based language for describing RESTful APIs. It provides all the information necessary to describe RESTful or practically RESTful APIs.
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In this lecture session we learn about RAML libraries that may be used to modularize any number and combination of data types, security schemes, resource types, traits, and annotations.
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In this lecture session we learn about API fragments that are reusable components of RAML to make the design and build of a reusable API even quicker and easier. Another advantage of building an API spec out of reusable API fragments is that consistency of definitions reduces the effort of implementing APIs.
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In this tutorial we learn about The RAML type system borrows from object oriented programming languages such as Java, as well as from XML Schema (XSD) and JSON Schema. RAML Types in a nutshell: Types are similar to Java classes. Types borrow additional features from JSON Schema, XSD, and more expressive object oriented languages
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In this lecture session we learn about Properties is nothing but in terms of JAVA ,Its Object Oriented Name. But Facet is nothing but More information about Property like MinLength,MaxLength,Minimum and Maximum and many more what you have said as well.
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In this lecture session we learn about how API fragments are reusable components of RAML to make the design and build of a reusable API even quicker and easier. Another advantage of building an API spec out of reusable API fragments is that consistency of definitions reduces the effort of implementing APIs.
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In this lecture session we learn that RAML belongs to the "API Tools" category of the tech stack, while YAML can be primarily classified under "Languages". According to the StackShare community, RAML has a broader approval, being mentioned in 9 company stacks & 6 developers stacks; compared to YAML, which is listed in 5 company stacks and 4 developer stacks.
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In this lecture session we learn about The WSDL document represents a contract between API providers and API consumers. RAML is a modern WSDL counterpart specifically for REST APIs. The RAML Spec is an open standard that was developed by the RAML workgroup and with support from MuleSoft.
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In this lecture session we learn about RAML to HTML is a documentation tool that outputs a single HTML page console based on a RAML definition. It's written in NodeJS and it can be executed as a command line.
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In this lecture session we learn about A resource node is one that begins with the slash and is either at the root of the API definition or a child of a resource node.
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In this lecture session we learn about RAML stands for RESTful API Modeling Language. It's a way of describing practically-RESTful APIs in a way that's highly readable by both humans and computers. We say "practically RESTful" because, today in the real world, very few APIs today actually obey all constraints of REST.
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In this lecture session we learn about RAML is a Rest API Modeling Language and it is based on YAML for describing your API's. It is basically used to describe your API, which can be easily readable by humans and computers.
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In this lecture session we learn about The baseURI im raml definition is a optional field that serves initially to identify the endpoint of the resources you will describe in the raml definition of a api. The baseURI may also be used to specify the URL at which the api is served.
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In this lecture session we learn about RAML stands for RESTful API Modeling Language. It's a way of describing practically RESTful APIs in a way that's highly readable by both humans and computers. It is a vendor-neutral, open-specification language built on YAML 1.2 and JSON for describing RESTful APIs.
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In this lecture session we learn about RESTful API Modeling Language (RAML) makes it easy to manage the API lifecycle from design to deployment to sharing. It's concise and reusable; you only have to write what you need to define and you can use it again and again. Uniquely among API specs, it was developed to model an API, not just document it.
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In this lecture session we learn about The WSDL document represents a contract between API providers and API consumers. RAML is a modern WSDL counterpart specifically for REST APIs. The RAML Spec is an open standard that was developed by the RAML workgroup and with support from MuleSoft.
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In this tutorial we learn about The RAML specification (this document) defines an application of the YAML 1.2 specification that provides mechanisms for the definition of practically-RESTful APIs, while providing provisions with which source code generators for client and server source code and comprehensive user documentation can be created.
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In this lecture session we learn about A string is a data type used in programming, such as an integer and floating point unit, but is used to represent text rather than numbers. It consists of a set of characters that can also contain spaces and numbers.
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In this RAML is used to design and manage the whole REST API lifecycle. MULE API Kit: Helps to build the APIs from Anypoint Studio using a RAML file. I will be explaining the generating flows from the RAML file and executing it.
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In this lecture session we learn about APIs (application programming interfaces) are simply communication tools for software applications. APIs are leading to key advances within the banking industry as financial institutions continue to collaborate with third parties.
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In this lecture session we learn about Music (alternatively called the Music app; formerly iPod) is a media player application developed for the iOS, iPadOS, tvOS, watchOS, and macOS operating systems by Apple Inc.
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In this lecture session we learn about An API application program interface is used in mobile apps just like it is in web apps. It allows developers to access another application or platform. APIs are the foundational element of a mobile app strategy.
The objectives are:
a).To implement ways to improve data reliability, efficiency and quality
b).To understand the company or client’s objectives
c).To identify the business goals
d).To give clean data in usable format to data analysts, data scientists or to the concerned team
Course Overview: This course is designed to equip participants with the skills and knowledge necessary to become proficient Data Engineers. The syllabus covers data architecture, ETL processes, big data technologies, data warehousing, and data pipeline development, preparing participants for roles focused on managing and optimizing data flows within organizations.
Part 1: Introduction to Data Engineering
Week 1: Fundamentals of Data Engineering
a.Definition and role of a Data Engineer
b.Overview of the data engineering lifecycle
c.Key concepts: Data pipelines, data lakes, and data warehouses
d.Group Discussion: The importance of data engineering in data-driven organizations
Week 2: Data Modeling and Database Design
a.Introduction to data modeling concepts
b.Types of data models: Conceptual, logical, and physical
c.Designing relational database schemas and normalization
d.Hands-on Exercise: Creating ER diagrams for a sample project
Part 2: Data Processing and ETL
Week 3: Data Processing Fundamentals
a)Overview of data processing techniques: Batch vs. stream processing
b)Tools and frameworks for data processing (Apache Spark, Apache Flink)
c)Understanding data ingestion methods
d)Hands-on Exercise Setting up a Spark environment
Week 4:ETL Processes and Tools
a)Introduction to ETL (Extract, Transform, Load) concepts
b)Best practices for designing ETL pipelines
c)Tools for ETL: Talend, Apache NiFi, Airflow
d)Hands-on Exercise: Building a simple ETL pipeline
Part 3: Data Warehousing and Data Lakes
Week 5: Data Warehousing Concepts
a)Introduction to data warehousing: Architecture and design
b)Understanding star and snowflake schemas
c)Data warehousing solutions (Amazon Redshift, Google BigQuery)
d)Hands-on Exercise: Designing a data warehouse schema
Week 6: Data Lakes and Big Data Technologies
a).Understanding data lakes and their benefits
b).Overview of big data frameworks (Hadoop, Spark)
c).Data governance and management in data lakes
d).Group Activity: Exploring a big data use case
Part 4: Data Pipeline Development
Week 7: Building Data Pipelines
a).Principles of data pipeline architecture
b).Tools for building data pipelines (Apache Kafka, AWS Glue)
c).Monitoring and maintaining data pipelines
d).Hands-on Exercise: Developing a data pipeline using Kafka
Week 8: Data Quality and Testing
a).Importance of data quality in engineering
b).Techniques for data validation and cleansing
c).Implementing testing strategies for data pipelines
d).Hands-on Exercise: Conducting data quality checks
Part 5: Cloud Data Engineering
Week 9: Cloud Platforms and Services
a).Overview of cloud data engineering services (AWS, Azure, Google Cloud)
b).Implementing data solutions in the cloud
c).Cost management and optimization strategies
d).Hands-on Exercise: Deploying a data pipeline in a cloud environment
Week 10: Real-time Data Processing
a).Introduction to real-time data processing concepts
b).Tools and frameworks for real-time analytics (Apache Storm, Kinesis)
c).Use cases for real-time data processing
d).Hands-on Exercise: Implementing a real-time data processing application
Part 6: Capstone Project
Week 11: Capstone Project Preparation
a).Overview of project objectives and expectations
b).Defining project scope: Building a comprehensive data pipeline for a use case
c).Initial project planning and outlining tasks
d).Group Discussion: Feedback on project proposals
Week 12: Capstone Project Execution
a).Implementing the project using skills learned throughout the course
b).Presenting the data pipeline and architecture to the class
c).Peer reviews and discussions on project experiences
Recommended Resources:
Textbooks:
1)"Designing Data-Intensive Applications" by Martin Kleppmann
2)"Data Engineering with Apache Spark" by James Lee
Online Resources:
1)Courses on Coursera, edX, and DataCamp
2)Data engineering blogs and forums for ongoing learning
Tools:-SQL, Apache Spark, Kafka, ETL tools, cloud platforms (AWS, Azure, GCP)
Assessment:
1)Weekly quizzes and assignments
2)Mid-term project focused on data modeling and ETL processes
3)Final capstone project showcasing comprehensive data engineering skills
The Data Engineer Certification ensures you know planning, production and measurement techniques needed to stand out from the competition.
A data engineer is an IT worker whose primary job is to prepare data for analytical or operational uses. These software engineers are typically responsible for building data pipelines to bring together information from different source systems.
Overall, becoming a data engineer is a great career choice for people who love detail, following engineering guidelines, and building pipelines that allow raw data to be turned into actionable insights. As mentioned earlier, a career in data engineering also offers excellent earning potential and strong job security.
To get well prepared for the exam, I encourage you to complete the Official Data Engineer course videos and read about the best practices of GCP products, followed by the ML Crash Course provided by Google. You should be ready to pass the exam by combining your studies with your knowledge.
Uplatz online training guarantees the participants to successfully go through the Data Engineercertification provided by Uplatz. Uplatz provides appropriate teaching and expertise training to equip the participants for implementing the learnt concepts in an organization.
Course Completion Certificate will be awarded by Uplatz upon successful completion of the Data Engineeronline course.
1).CCA Data Analyst.
2).IBM Data Science Professional Certificate.
3).Amazon AWS Certified Big Data.
4).SAS Certified Advanced Analytics Professional.
5).Microsoft Certified Solutions Expert (MCSE): Data Management and Analytics.
6).Microsoft Azure Data Scientist Associate Certification.
The Data Engineer draws an average salary of $120.000 per year depending on their knowledge and hands-on experience. The Data Engineer Admin job roles are in high demand and make a rewarding career.
Data engineers work in a variety of settings to build systems that collect, manage, and convert raw data into usable information for data scientists and business analysts to interpret. Their ultimate goal is to make data accessible so that organizations can use it to evaluate and optimize their performance.
In 2020, the Dice Tech Job Report stated data engineering to be the fastest-growing job in technology with a predicted 50% year-over-year growth in the number of open positions
The database engineer is becoming extinct, with data warehousing needs moving to the cloud, and data engineers are increasingly responsible for managing data performance and reliability.
Note that salaries are generally higher at large companies rather than small ones. Your salary will also differ based on the market you work in.
a).Data Science Manager
b).Data Science Analyst — Smart Devices
c).Product Data Scientist
d).Data Analyst
Q1.Define Data Engineering.
Ans-Data engineering is a terminology used in big data. It mainly focuses on the application of data collection and research. The data retrieved from various sources are just raw data. Data engineering is used to convert this raw data into useful information.
Q2.Define Data Modelling?
Ans-Data modelling refers the method of documenting complex software design as a pictorial representation so that end user can easily understand.
Q3.What are the types of design schemas in Data Modelling?
Ans-The two types of schemas in data modeling are Star schema and Snowflake schema.
Q4.Mention the components of a Hadoop application?
Ans-The components of Hadoop application are:
a)Hadoop Common
b)HDFS
c)Hadoop MapReduce
d)Hadoop YARN
Q5.Define NameNode?
Ans-It refers to the centerpiece of HDFS. It is used to store data of HDFS and tracks various files across the clusters. The data is saved as DataNodes.
Q6.Define Hadoop streaming?
Ans-It is a utility which is used for the creation of the map and Reduces jobs and submits them to a specific cluster.
Q7.What is the abbreviation of HDFS?
Ans-HDFS stands for Hadoop Distributed File System.
Q8.Define Block and Block Scanner in HDFS
Ans-Blocks are the smallest unit of a data file. Hadoop automatically separates ahuge file into small pieces.Block Scanner validates the list of blocks that are presented in a DataNode.
Q9.What are the steps that happens when Block Scanner detects a corrupted data block?
Ans-When Block Scanner find a corrupted data block, Data Node reports to Name NodeThe Name Node initiates the process of creating a new replica using a replica of the corrupted block.
The Replication count of the correct replicas tries to match with the replication factor. If the match is found corrupted data block will not be deleted.
Q10.Mention the two messages that NameNode gets from DataNode?
Ans-There are two messages which Name Nodereceives from DataNodeare Block report and Heartbeat.
Q11.What are the various XML configuration files in Hadoop?
Ans-The five XML configuration files in Hadoop:
a)Mapred-site
b)Core-site
c)HDFS-site
d)Yarn-site
Q12.Mention the four V's of big data?
Ans-Four V's of big data are:
a)Velocity
b)Variety
c)Volume
d)Veracity
Q13.What are the features of Hadoop
Ans-The key features are:
a)It is an open-source framework that is available for free.
b)Hadoop is compatible with the different hardware types.
c)Hadoop supports faster-distributed data processing.
d)It saves the data in the cluster.
14.What are the main methods of Reducer?
Ans-
a)setup (): It is used for configuring parameters such as size of input data and distributed cache.
b)cleanup(): It is used to clean temporary files.
c)reduce(): It is a core of the reducer which is called once per key with the associated reduced task
Q15.What is the full form of COSHH?
Ans-The COSHH is also known as Classification and Optimization based Schedule for Heterogeneous Hadoop systems.