DP-080- Azure Data Factory and Data Lake Training

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Course Highlights

  • Instructor-led Online Training
  • Project Based Learning
  • Certified & Experienced Trainers
  • Course Completion Certificate
  • Lifetime e-Learning Access
  • 24x7 After Training Support

DP-080- Azure Data Factory and Data Lake Training Course Overview

Master Azure's potent data handling services in DP-080: Azure Data Factory and Data Lake with Multisoft Systems. Gain comprehensive knowledge on extracting, transforming, and loading (ETL) data with Azure Data Factory, and explore how to manage large scale data using Azure Data Lake. Equip yourself with the right skills to architect efficient data solutions, paving the way for data-driven decision making.

The DP-080 course titled "Designing and Implementing Data Flows with Azure Data Factory and Azure Data Lake" is a course meant for professionals looking to understand and implement data solutions using Microsoft Azure services such as Azure Data Factory (ADF) and Azure Data Lake (ADL). This course would be best suited for data engineers, data architects, and individuals who design and implement data solutions on Azure.

The course begins with an overview of the role of data in modern enterprises and introduces the concept of cloud-based data solutions. It then delves into the Azure Data Platform and its components, giving you a well-rounded understanding of its capabilities and use-cases. Azure Data Factory, one of the core components of the course, is covered in detail. Students will learn how to build, design, and manage data pipelines in ADF, and understand its transformative capabilities. This involves dealing with various data sources, transforming the data to meet business requirements, and orchestrating data movement and transformation. The course also extensively covers Azure Data Lake, a highly scalable and secure data lake that allows for data storage and analytics. You'll learn how to create a Data Lake store, ingest data, and implement security measures. The course further explores how to use Data Lake Analytics for big data processing tasks.

DP-080- Azure Data Factory and Data Lake Course Objective
  • Understanding the basics of Azure Data Factory (ADF) and Azure Data Lake (ADL) Setting up ADF and ADL Working with Data Pipelines in ADF Data Ingestion and Transformation Data Storage in Azure Data Lake Data Security and Compliance Integration with other Azure Services Real-world Scenarios and Case Studies
DP-080- Azure Data Factory and Data Lake Online Training
  • Recorded Videos After Training
  • Digital Learning Material
  • Course Completion Certificate
  • 24x7 After Training Support
Target Audience
  • Data Engineers
  • Data Scientists
  • IT Professionals
  • Database Administrators
  • Data Analysts Solution Architects
DP-080- Azure Data Factory and Data Lake Course Prerequisites
  • Understanding of Basic Cloud Concepts: Familiarity with the basics of cloud computing, particularly Microsoft Azure.
  • Basic Programming Knowledge: Some knowledge of programming, especially in a language such as Python or SQL, can be useful for working with data transformations and querying.
  • Database Knowledge: Understanding of basic database concepts, including data schemas, SQL, and data structures.
  • Basic Data Processing Knowledge: Understanding of ETL (Extract, Transform, Load) processes, data pipelines, and data warehousing concepts.
  • Familiarity with Azure Services: Some familiarity with Azure services can be beneficial, although it's not strictly necessary as the course should cover this.
DP-080- Azure Data Factory and Data Lake Course Certification
  • Multisoft Systems provides a globally recognized training certificate after successful completion of DP-080- Azure Data Factory and Data Lake Certification Training Course.

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DP-080- Azure Data Factory and Data Lake Training Course Content

Module 1: Introduction to Data Structures

  • Overview of Data Structures
  • Types of Data Structures
  • Arrays and Linked Lists
  • Stacks and Queues
  • Trees and Graphs
  • Hash Tables
  • Heaps
  • Priority Queues
  • Searching Algorithms
  • Sorting Algorithms
  • Big O Notation
  • Memory Management
  • Recursion
  • Dynamic Programming

Module 2: Algorithmic Design Techniques

  • Greedy Algorithms
  • Divide and Conquer Algorithms
  • Dynamic Programming
  • Backtracking Algorithms
  • Branch and Bound Algorithms
  • Network Flow Algorithms
  • Linear Programming
  • Approximation Algorithms
  • Randomized Algorithms
  • Heuristic Algorithms

Module 3: Dynamic Programming

  • Introduction to Dynamic Programming
  • Applications of Dynamic Programming
  • Greedy Algorithms vs Dynamic Programming
  • Memoization and Tabulation
  • Longest Common Subsequence
  • Optimal Substructure and Overlapping Subproblems
  • 0-1 Knapsack Problem
  • Matrix Chain Multiplication
  • Bellman-Ford Algorithm
  • Floyd-Warshall Algorithm

Module 4: Greedy Algorithms

  • Introduction to Greedy Algorithms
  • Greedy Algorithms for Optimization Problems
  • Greedy Algorithms for Graph Problems
  • Greedy Algorithms for Scheduling Problems
  • Greedy Algorithms for Knapsack Problems
  • Greedy Algorithms for Sorting Problems
  • Greedy Algorithms for String Matching Problems
  • Greedy Algorithms for Network Flow Problems
  • Greedy Algorithms for Approximation Algorithms
  • Greedy Algorithms for NP-Hard Problems

Module 5: Graph Algorithms

  • Introduction to Graph Algorithms
  • Breadth-First Search Algorithm
  • Depth-First Search Algorithm
  • Shortest Path Algorithms
  • Minimum Spanning Tree Algorithms
  • Network Flow Algorithms
  • Maximum Flow Algorithms
  • Connectivity Algorithms
  • Matching Algorithms
  • Eulerian Path Algorithms
  • Hamiltonian Path Algorithms
  • Graph Coloring Algorithms
  • Topological Sorting Algorithms
  • Graph Isomorphism Algorithms
  • Graph Partitioning Algorithms
  • Graph Drawing Algorithms
  • Graph Clustering Algorithms
  • Graph Optimization Algorithms
  • Graph Search Algorithms
  • Graph Traversal Algorithms

Module 6: Network Flow Algorithms

  • Introduction to Network Flow Algorithms
  • Maximum Flow Problem
  • Minimum Cost Flow Problem
  • Ford-Fulkerson Algorithm
  • Edmonds-Karp Algorithm
  • Dinic's Algorithm
  • Push-Relabel Algorithm
  • Goldberg-Tarjan Algorithm
  • Network Simplex Algorithm
  • Applications of Network Flow Algorithms

Module 7: Linear Programming

  • Introduction to Linear Programming
  • Formulating Linear Programming Problems
  • Graphical Solution of Linear Programming Problems
  • Simplex Method for Solving Linear Programming Problems
  • Duality in Linear Programming
  • Sensitivity Analysis in Linear Programming
  • Integer Programming
  • Transportation and Assignment Problems
  • Network Flow Problems
  • Project Scheduling Problems

Module 8: Integer Programming

  • Introduction to Integer Programming
  • Formulating Integer Programming Problems
  • Linear Programming Relaxations
  • Branch and Bound Algorithms
  • Cutting Plane Algorithms
  • Column Generation Algorithms
  • Integer Programming Applications
  • Advanced Integer Programming Techniques
  • Integer Programming Software
  • Integer Programming Modeling Examples

Module 9: Approximation Algorithms

  • Introduction to Approximation Algorithms
  • Greedy Algorithms
  • Randomized Algorithms
  • Local Search Algorithms
  • Linear Programming Relaxation
  • Primal-Dual Algorithms
  • Semidefinite Programming Relaxation
  • Approximation Algorithms for NP-Hard Problems
  • Approximation Algorithms for Graph Problems
  • Approximation Algorithms for Combinatorial Optimization Problems
  • Approximation Algorithms for Machine Learning Problems
  • Approximation Algorithms for Data Mining Problems
  • Approximation Algorithms for Image Processing Problems
  • Approximation Algorithms for Network Design Problems
  • Approximation Algorithms for Scheduling Problems
  • Approximation Algorithms for Online Problems
  • Approximation Algorithms for Distributed Systems
  • Approximation Algorithms for Big Data Problems
  • Approximation Algorithms for Cloud Computing
  • Approximation Algorithms for Internet of Things (IoT)

Module 10: NP-Completeness

  • Introduction to NP-Completeness
  • Polynomial Time Reductions
  • Cook-Levin Theorem
  • Verifying NP-Completeness
  • NP-Complete Problems
  • Approximation Algorithms
  • NP-Hard Problems
  • Coping with NP-Completeness
  • NP-Completeness in Practice
  • Advanced Topics in NP-Completeness

Module 11: Parallel Algorithms

  • Introduction to Parallel Algorithms
  • Designing Parallel Algorithms
  • Parallel Algorithms for Sorting and Searching
  • Parallel Algorithms for Graphs
  • Parallel Algorithms for Matrix Operations
  • Parallel Algorithms for Numerical Computations
  • Parallel Algorithms for Image Processing
  • Parallel Algorithms for Data Mining
  • Parallel Algorithms for Machine Learning
  • Parallel Algorithms for Natural Language Processing
  • Parallel Algorithms for Big Data Analysis
  • Parallel Algorithms for Cloud Computing
  • Parallel Algorithms for High Performance Computing
  • Parallel Algorithms for Distributed Systems
  • Parallel Algorithms for Embedded Systems

Module 12: Advanced Topics in Dynamic Programming

  • Reinforcement Learning and Dynamic Programming
  • Multi-Objective Dynamic Programming
  • Stochastic Dynamic Programming
  • Approximate Dynamic Programming
  • Optimal Control Theory
  • Markov Decision Processes
  • Dynamic Programming for Optimal Control
  • Dynamic Programming for Markov Decision Processes
  • Dynamic Programming for Multi-Agent Systems
  • Dynamic Programming for Robotics
  • Dynamic Programming for Resource Allocation
  • Dynamic Programming for Network Optimization
  • Dynamic Programming for Game Theory
  • Dynamic Programming for Machine Learning
  • Dynamic Programming for Optimization Problems


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