Data Architect training
Shanu - 89259 58904
25000
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Data Architect Training from ACTE.
Module 1: Database Framework
• • Introduction to ERDs
• • Develop a database schema
• • Understand normalization and its use cases
• • Learn to normalize data to the 3rd Normal Form
Module 2: Relational Data Design
• • Build a conceptual ERD
• • Build a logical ERD
• • Learn about cardinality and Crow’s Foot notation
• • Build a physical ERD
Module 3: Creating a Physical Database
• • Learn about factors that affect database performance
• • Learn about file and data storage solutions
• • Use DDL SQL to create database objects in PostGreSQL
• • Learn about data ingestions methods, including: ETL, Pipelines, APIs and direct feeds
• • Use DML SQL to populate a database with data in PostGreSQL
• • Use CRUD SQL commands to demonstrate proper operation of a database
Module 4: Enterprise Data Architecture
• • Understand importance of Data Architecture in any organization
• • Learn the benefits of executing a Data Architecture
• • Learn the business and technical artifacts required
• • Understand business and functional requirements
• •
• • Learn how OLTP, ODS and OLAP models are being designed
Module 5: Staging Data
• • Build staging area for data ingestion
• • Learn to organize data assets based on schemas
• • Design schedules for data processing based on the requirements
• • Learn to manage staging area through metadata
Module 6: Operational Data Store
• • Build an integrated ER model connecting distributed data assets
• • Learn to design Data Dictionary and Master Data
• • Apply normalization rules to eliminate redundancies
• • Learn when to use ETL vs ELT techniques
• • Learn to cleanse data anomalies
• •
Module 7: Data Warehouse
• • Learn two OLAP modeling designs — Star and Snowflake schemas
• • Learn various dimensional and fact table types
• • Build ELT data processing from ODS to Data warehouse
• • Write SQL queries for the purpose of reporting
Module 8: Characteristics of Big Data
• • Explain what is big data
• • Articulate the business value of big data
• • Describe the characteristics of big data
• • Distinguish between horizontal scaling vs vertical scaling
• • Describe the components of a big data ecosystem
Module 9: Ingestion, Storage and Processing Frameworks
• • Explain how distributed storage works in HDFS
• • Explain how distributed processing works
• • Explain how resources are managed in a Hadoop cluster
• • Distinguish between different distributed processing frameworks
• • Apply frameworks to appropriate use cases
Module 10: NoSQL Databases
• • Explain difference between SQL and NoSQL Databases
• • Differentiate between ACID and CAP properties of SQL and NoSQL databases
• • Implement, create, read, write, update NoSQL DB operations with DynamoDB
• • Create simple NoSQL data model
Module 11: Scalable Data Lake Architecture
• • Explain what is a data lake and it’s business value
• • Distinguish between different data formats and their application
• • Articulate Data Lake design patterns and challenges
• • Explain how to enable transactional capabilities in Data Lake
Module 12: Introduction to Data Governance
• • Understand what is Data Governance and its importance
• • Learn about the different disciplines of Data Governance
• • Understand the different stakeholders involved in Data Governance projects
Module 13: Metadata Management
• • Understand the different types of metadata
• • Understand the components and capabilities of Metadata Management System
• • Create conceptual and logical Enterprise Data Models
• • Create an Enterprise Data Catalog
Module 14: Data Quality Management
• • EPerform data profiling using various techniques using data quality dimensions
• • Identify remediation options for data quality issues
• • Measure data quality using data quality scores and thresholds
• • Monitor data quality using dashboards, exception and trend reports
Module 15: Master Data Management
• • Understand the concepts of master data and golden record
• • Understand different types of Master Data Management Architectures
• • Create a golden record using various match and merge techniques
• • Understand data governance processes for authoring, monitoring and approval of master data
Hands-on Experience on Live Data Architect Training Projects
Project 1
Data for Inventory Control Management
An inventory Data is a centralised storage location for all inventory data in a company.
Project 2
Data for the Student Record Keeping System
A student record management system stores and organises the school's primary Data.
Project 3
College Data Project Data
I'll tell you about some of the most interesting and unique Data project ideas that will really help you with your final year project.
Project 4
Project for Payroll Management System Data
It is a document that provides employees with information about their earnings and deductions, which vary from one employee to the next.
Hands-on Experience on Live Data Architect Training Projects
Project 1
Data for Inventory Control Management
An inventory Data is a centralised storage location for all inventory data in a company.
Project 2
Data for the Student Record Keeping System
A student record management system stores and organises the school's primary Data.
Project 3
College Data Project Data
I'll tell you about some of the most interesting and unique Data project ideas that will really help you with your final year project.
Project 4
Project for Payroll Management System Data
It is a document that provides employees with information about their earnings and deductions, which vary from one employee to the next.
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