Data Engineering Course Training
Course Overview
Data engineering is the branch of computer science that deals with the conversion of data between different formats. Data engineers are responsible for collecting, processing, cleaning and storing large amounts of raw data into a form that is more easily analyzed.
Data engineers have several advantages when it comes to their jobs:
- They have experience working with databases and can be considered experts in the field.
- They are adept at finding important information in large datasets.
- They are expected to know the Django framework which makes them excellent candidates for Python or Java positions.
Data engineering refers to the practical application of computer science and engineering principles to store and process data.
Data engineers use languages like Java, SQL, or Pig for processing data. They are concerned with the different aspects of data pipeline such as collection, storage, analysis etc. Data engineers have a hand in designing databases from the ground up and maintain them to be sure they’re constantly running at peak performance.
Why Should You Learn Microsoft Data Engineering?
Data engineering is an essential part of the data-driven enterprise, and as an emerging field, it is a rapidly evolving discipline.
Data engineers have a unique set of skills that they need to work with in order to maximize their ability to solve business problems. Data engineering is not just about how data gets processed, but how it gets created, moved, stored and accessed.
Data engineering is an essential part of the data-driven enterprise, and as an emerging field, it is a rapidly evolving discipline.
Data engineers have a unique set of skills that they need to work with in order to maximize their ability to solve business problems. Data engineering is not just about how data gets processed, but how it gets created, moved, stored and accessed.
Data engineering is a discipline of software engineering, closely related to database management and data analysis.
The tasks of data engineers might include the design and implementation of databases, scaling up databases for increased performance, or the building of extract, transform and load (ETL) programs for archiving data from one database to another.
Data engineering is a process for collecting data from various sources, transforming it into the desired format, and extracting relevant knowledge. Data engineering is primarily concerned with storage, processing and extraction of data for various purposes like predictive analytics, data mining and business intelligence.
Data engineers work with a variety of tools including SQL, which constitutes the core of most programs that extract information from relational databases. Other languages include C++, Java to handle complexities in unstructured data such as video or sound files.
Data engineering is the process of getting data into a structured and searchable way.
Data engineering also helps in data integration, data quality, and finding necessary information by performing data analysis. With their help, companies are able to generate insights into customer behavior and share them with the marketing team to get better results for their campaigns. They can also identify trends and make predictions about various user anecdotes.
Desktop data engineer is a person who specializes in data transformation and loading. This person knows how to work with the Teradata, Apache Spark, and Hadoop ecosystem.
A data engineer must be proficient with tools such as Excel, WHMCS, Apache Kafka, Teradata, Python, or SQL. They must be able to be comfortable working in any environment and should have some knowledge of hardware architecture such as RAID and SAN.
Engineers are responsible for the development and maintenance of databases. They ensure that the databases meet the company’s data and performance requirements by designing, developing, deploying and maintaining them
Data engineers also help create reports to be used by other stakeholders and engineers. These types of reports can range from showing data changes over time to describing the database environment.
Data engineering is a growing field that is in need of a lot of skilled professionals.
A data engineer is someone who builds and maintains the infrastructure for collecting, storing and processing huge amounts of data. These individuals are responsible for designing data pipelines, maintaining databases, and ensuring that the data is consistent and accurate.
Data engineering also involves coming up with ideas to solve complex problems with big datasets. Data engineers are also responsible for developing new ways to store large volumes of information in order to make it more efficient to process.
Data engineering is the process of managing and storing data. Data engineers work with a variety of tools to make sure that their given organization has enough data to improve products and services.
Data engineers have three key responsibilities:
- Ensuring the data's integrity
- Managing and scaling for different workloads
- Utilizing the information for decision making
Course Curriculum
- Data, Databases and RDBMS Software
- Database Types: OLTP, DWH, OLAP
- Microsoft SQL Server Advantages, Use
- Versions and Editions of SQL Server
- SQL: Purpose, Real-time Usage Options
- SQL versus Microsoft T-SQL [MSSQL]
- Microsoft SQL Server - Career Options
- SQL Server Components and Usage
- Database Engine Component and OLTP
- BI Components, Data Science Components
- ETL, MSBI and Data Engineering Components
- Course Plan, Concepts, Resume, Project
- 24 x 7 Online Labs for Remote DB Access
- Software Installation Pre-Requisites
- Data Engineering Job Roles in Real-time
- Data Engineering Data Analyst Job Roles
- Business Analyst - Job Roles
- Data Engineering Developer - Job Roles
- Data Engineering for Data Scientists
- Comparing MSBI and Data Engineering
- Comparing Tableau and Data Engineering
- MCSA 70-778, MCSA 70-779 Exam
- Types of Reports in Real-World
- Interactive & Paginated Reports
- Analytical & Mobile Reports
- Data Sources Types in Data Engineering
- Data Engineering Licensing Plans - Types
- Data Engineering Training : Lab Plan
- Data Engineering Dev & Prod Environments
- Understanding the Data Engineering Tools
- Installing Data Engineering & Connecting to Data
- The "Locale" used in the curriculum
- Working with the query Editor
- Working with the data model and creating a visualization
- Data Engineering Desktop Installation
- Data Sources & Visual Types
- Canvas, Visualizations and Fields
- Get Data and Memory Tables
- In-Memory xvelocity Database
- Table and Tree Map Visuals
- Format Button and Data Labels
- Legend, Category and Grid
- PBIX and PBIT File Formats
- Visual Interaction, Data Points
- Disabling Visual Interactions
- Edit Interactions - Format Options
- SPOTLIGHT & FOCUSMODE
- CSV and PDF Exports. Tooltips
- Data Engineering EcoSystem, Architecture
- Slicer Visual : Real-time Usage
- Orientation, Selection Properties
- Single & Multi Select, CTRL Options
- Slicer : Number, Text and Date Data
- Slicer List and Slicer Dropdowns
- Visual Sync Limitations with Slicer
- Disabling Slicers,Clear Selections
- Grouping : Real-time Use, Examples
- List Grouping and Binning Options
- Grouping Static / Fixed Data Values
- Grouping Dynamic / Changing Data
- Bin Size and Bin Limits (Max, Min)
- Bin Count and Grouping Options
- Grouping Binned Data, Classification
- Creating Hierarchies in Data Engineering
- Independent Drill-Down Options
- Dependant Drill-Down Options
- Conditional Drilldowns, Data Points
- Drill Up Buttons and Operations
- Expand & Show Next Level Options
- Dynamic Data Drills Limitations
- Show Data and See Records
- Filters : Types and Usage in Real-time
- Visual Filter, Page Filter, Report Filter
- Basic, Advanced and TOP N Filters
- Category and Summary Level Filters
- DrillThru Filters, Drill Thru Reports
- Keep All Filters" Options in DrillThru
- CrossReport Filters, Include, Exclude
- Drill-thru Filters, Page Navigations
- Bookmarks: Real-time Usage
- Bookmarks for Visual Filters
- Bookmarks for Page Navigations
- Selection Pane with Bookmarks
- Buttons, Images with Actions
- Buttons, Actions and Text URLs
- Bookmarks View & Selection Pane
- OLTP Databases, Big Data Sources
- Azure Database Access, Reports
- Import & Direct Query with Data Engineering
- SQL Queries and Enter Data
- Data Modeling : Currency, Relations
- Summary, Format, Synonyms
- Web View & Mobile View in PBI
- Stacked Charts and Clustered Charts
- Line Charts, Area Charts, Bar Charts
- 100% Stacked Bar & Column Charts
- Map Visuals: Tree, Filled, Bubble
- Cards, Funnel, Table, Matrix
- Scatter Chart : Play Axis, Labels
- Series Clusters & Selections
- Waterfall Chart and ArcGIS Maps
- Info graphics, Icons and Labels
- Color Saturation, Sentiment Colors
- Column Series, Column Axis in Lines
- Join Types : Round, Bevel, Miter
- Shapes, Markers, Axis, Plot Area
- Display Units, Data Colors, Shapes
- Series, Custom Series and Legends
- Power Query M Language Purpose
- Power Query Architecture and ETL
- Data Types, Literals and Values
- Power Query Transformation Types
- Table & Column Transformations
- Text & Number Transformations
- Date, Time and Structured Data
- List, Record and Table Structures
- let, source, in statements @ M Lang
- Power Query Functions, Parameters
- Invoke Functions, Execution Results
- Get Data, Table Creations and Edit
- Merge and Append Transformations
- Join Kinds, Advanced Editor, Apply
- ETL Operations with Power Query
- Query Duplicate, Query Reference
- Group By and Advanced Options
- Aggregations with Power Query
- Transpose, Header Row Promotion
- Reverse Rows and Row Count
- Data Type Changes & Detection
- Replace Columns: Text, NonText
- Replace Nulls: Fill Up, Fill Down
- PIVOT, UNPIVOT Transformations
- Move Column and Split Column
- Extract, Format and Numbers
- Date & Time Transformations
- Deriving Year, Quarter, Month, Day
- Add Column : Query Expressions
- Query Step Inserts and Step Edits
- Creating Parameters in Power Query
- Parameter Data Types, Default Lists
- Static/Dynamic Lists For Parameters
- Removing Columns and Duplicates
- Convert Tables to List Queries
- Linking Parameters to Queries
- Testing Parameters and PBI Canvas
- Multi-Valued Parameter Lists
- Creating Lists in Power Query
- Converting Lists to Table Data
- Advanced Edits and Parameters
- Data Type Conversions, Expressions
- Columns From Examples, Indexes
- Conditional Columns, Expressions
- DAX : Importance in Real-time
- Real-world usage of Excel, DAX
- DAX Architecture, Entity Sets
- DAX Data Types, Syntax Rules
- DAX Measures and Calculations
- ROW Context and Filter Context
- DAX Operators, Special Characters
- DAX Functions, Types in Real-time
- Vertipaq Engine, DAX Cheat Sheet
- Creating, Using Measures with DAX
- Creating, Using Columns with DAX
- Quick Measures and Summaries
- Validation Errors, Runtime Errors
- SUM, AVERAGEX, KEEPFILTERS
- Dynamic Expressions, IF in DAX
- Data Modeling Options in DAX
- Detecting Relations for DAX
- Using Calculated Columns in DAX
- Using Aggregated Measures in DAX
- Working with Facts & Measures
- Modeling : Missing Relations
- Modeling : Relation Management
- CALCULATE Function Conditions
- CALCULATE & ALL Member Scope
- RELATED & COUNTROWS in DAX
- Entity Sets and Slicing in DAX
- Dynamic Expressions, RETURN
- Date, Time and Text Functions
- Logical, Mathematical Functions
- Running Total & EARLIER Function
- Connection with CSV, MS Access
- AVERAGEX and AVERAGE in DAX
- KEEPFILTERS and CALCUALTE
- COUNTROWS, RELATED, DIVIDE
- PARALLELPERIOD, DATEDADD
- CALCULATE & PREVIOUSMONTH
- USERELATIONSHIP, DAX Variables
- TOTALYTD , TOTALQTD
- DIVIDE, CALCULATE, Conditions
- IF..ELSE..THEN Statement
- SELECTEDVALUE, FORMAT
- SUM, DATEDIFF Examples in DAX
- TODAY, DATE, DAY with DAX
- Time Intelligence Functions - DAX
- Data Engineering Service Architecture
- Data Engineering Cloud Components, Use
- App Workspaces, Report Publish
- Reports & Related Datasets Cloud
- Creating New Reports in Cloud
- Report Publish and Report Uploads
- Dashboards Creation and Usage
- Adding Tiles to Dashboards
- Pining Visuals and Report Pages
- Visual Pin Actions in Dashboards
- LIVE Page Interaction in Dashboard
- Adding Media: Images, Custom Links
- Adding Chs and Embed Links
- API Data Sources, Streaming Data
- Streaming Dataset Tiles (REST API)
- Dashboards Actions, Report Actions
- DataSet Actions: Create Report
- Share, Metrics and Exports
- Mobile View & Dashboard Themes
- Q & A [Cortana] and Pin Visuals
- Export, Subscribe, Subscribe
- Favorite, Insights, Embed Code
- Featured Dashboards and Refresh
- Gateways Configuration, PBI Service
- Gateway Types, Cloud Connections
- Gateway Clusters, Add Data Sources
- Data Refresh : Manual, Automatic
- PBIEngw Service, ODG Logs, Audits
- DataFlows, Power Query Expressions
- Adding Entities and JSON Files
- Import and Upload Options in Excel
- Excel Workbooks and Dashboards
- Datasets in Excel and Dashboards
- Using Excel Analyzer in Data Engineering
- Using Excel Publisher in PBI Cloud
- Excel Workbooks, PINS in Data Engineering
- Excel ODC Connections, Power Pivot
- Row Level Security (RLS) with DAX
- Need for RLS in Data Engineering Cloud
- Data Modeling in Data Engineering Desktop
- DAX Roles Creation and Testing
- Adding Data Engineering Users to Roles
- Custom Visualizations in Cloud
- Histogram, Gantt Chart, Infographics
- Need for Report Server in PROD
- Install, Configure Report Server
- Report Server DB, Temp Database
- Web service URL, Webportal URL
- Creating Hybrid Cloud with Data Engineering
- Using Data Engineering DesktopRS
- Uploading Interactive Reports
- Report Builder For Report Server
- Report Builder For Data Engineering Cloud
- Designing Paginated Reports (RDL)
- Deploy to Data Engineering Report Server
- Data Source Connections, Report
- Data Engineering Report Server to Cloud
- Tenant IDs Generation and Use
- Mobile Report Publisher, Usage
- Overview
- Basic Power App Concept
- Canvas Apps | Navigation | Customization
- Contents (Galleries, Data Cards, Forms, Triggers, Functions & Formulas, Edit Forms, Text Boxes)
- Why Data Engineering Service?
- Comparison Data Engineering Free & Premium
- Logging into Data Engineering Service
- Interface overview
- Importing data from Desktop to Service
- Dataset menu
- Working on reports
- Dashboard overview
- Workspace & Gateways
- Installing Gateways - Personal & On-premise
- Working alone or collaborating with colleagues
- Collaborating in App Workspace
- Sharing the results
- Publishing the app
- Content packs from online services
- Data Engineering Mobile Overview
- Excluding dataset from sharing
- Options for Publishing from Excel
- Pin Excel Elements to Data Engineering
- Analyze in Excel (Data Engineering Pro or Premium)
- Excel Publish: Upload and Export to Data Engineering
- Sharing Published Excel Dashboards (Data Engineering Pro or Premium)
- Project Requirement Analysis
- Implementing SDLC Phases
- Requirement Gathering, FSA
Upcoming Batches
| Type | Course Name | Start Date | Time | Day |
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Frequently Asked Questions (FAQ)
The demand for a data scientist is rapidly growing globally. Data Engineering, Machine Learning, and other fields are incredibly promising, interesting & have limitless applications. Even though there are many practitioners, there is a lack of skilled specialists in these domains. Data Engineering skill is in great demand since it results in tangible & measurable perks. The significant increase in open Data Engineering professions indicates that Data Engineering careers are well-positioned for the future.
Statistics from numerous employment portals reveal that the number of Data Engineering jobs posted has steadily increased over the years.
There are several paths to becoming a data scientist. The following are the primary steps to pursuing a career in Data Engineering.
- - Choose a profession in Data Engineering.
- - Continue to learn by developing your Data Engineering projects.
- - Learn about Data Engineering using our personalized Data Engineering training, such as providing resources, unique programs & more.
- - Build your Data Engineering portfolio while improving & refining your abilities through our expertise.
For many years, one of the top occupations in India was that of a data scientist. In terms of compensation, job demand, work satisfaction, organizations utilize the phrase "data scientist" to refer to other comparable professions, such as "data analyst."
Demand for Data Engineering professionals is rapidly increasing in the market, as the organization maintains itself through data-driven insights. Various firms notice the worth & potential of Big Data knowledge as they thrive to use it to create higher business choices. The supply of skilled applicants is growing at a higher pace. So, it is a great choice to become a data scientist to become a stalwart in the IT industry.
We are happy & proud to say that we have impaneled with numerous small, mid-sized MNCs. Many of these organizations/businesses have Data Engineering opportunities widely open for the skillful. Furthermore, we have highly active placement support that assists our students with 100% placement. Our support team will also help by training participants through mock interviews and other skill enhancement training.
The demand for a data scientist is rapidly growing globally. Because the requirement in this domain is so high & the supply of people who can do this job well is limited, Data Engineering offers huge pay and perks in a position even at entry-level. Several organizations/companies also refer to data analysts as data scientists. Particular individuals are usually involved in operating with the organization's database.
So coming to this, Data Engineering is the field that comprises everything related to data cleansing, data mining, data preparation & data analysis. Big data refers to the amount of data that is difficult to store & process in real-time. These data utilize insights that can lead to better decision-making. Data Engineering algorithms will create a difference with the data insights across industries (like internet searches, search recommendations & advertisements).
Data analysts do not require significant coding abilities, but they should be familiar with analytics tools, data visualization software & data management applications.
Our personalized curriculum is programmed to give a hands-on approach to the students in Data Engineering. The Data Engineering course program teaches the basics of every module, followed by high-intensity practical sessions reflecting the current challenges & needs of the industry that will demand aspirants time & commitment.
Baskar
- 40 Videos
- 45 Lectures
- Exp. 10Year
Courses
Course Features
- Demo Training
- Virtual Training & Offline Training
- Flexible Batches
- Professional Curriculum
- Internship On Real-Time Projects
- Industry Expert Faculties
- 100% Job Oriented Training
- Certification Guidance
- Industry Success Case Studies
- Life Time Video Access
- Resume Building & Placement Assistance
- Gain Hands-on Experience
- Mock Interviews
Course Features
- Student Enrolled:1740
- lectures:10
- Quizzes:4
- Duration:60 hours
- Skill Level:Beginner
- Language:English
- Assessment:Yes
