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Data Governance with Databricks

Whizlabs Instructor

Databricks is a cloud-based data engineering tool used to process and transform large amounts of data and explore the data through machine learning models. It combines data warehouses & data lakes into a lakehouse architecture.

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Databricks is a cloud-based data engineering tool used to process and transform large amounts of data and explore the data through machine learning models. It combines data warehouses & data lakes into a lakehouse architecture.

Data governance is a broad approach that comprises the principles, practices, and tools to manage an organization’s data assets throughout its lifecycle. A data governance strategy allows organizations to make data easily available protecting their data from unauthorized access, and ensuring compliance with regulatory requirements.

This course provides 4 hours of training videos which are segmented into modules. The course concepts are easy to understand through lab demonstrations. In order to test the understanding of learners, every module includes Assessments in the form of Quizzes and In-Video Questions. A mandatory Graded Questions Quiz is also provided at the end of every module.

Candidate should have hands-on knowledge of the Databricks platform with the basic knowledge of AWS services. This course is tailored for professionals seeking to establish a strong foundation in data governance, fraud detection, and prevention strategies. By the end of this course, you will be able to:

-Understand the benefits and features of Databricks on AWS.

-Demonstrate Data Cleansing Pipelines in Databricks.

-Analyze Data Access Control Models and Data Privacy Regulations.

-Elaborate Data Lineage and Data Versions in Databricks Pipelines

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What's inside

Syllabus

Introduction to Data Governance with Databricks
Welcome to Week 1 of Data Governance with Databricks course. This week, you will learn about Introduction to Databricks on AWS. Additionally, you will learn about the benefits and features of Databricks and AWS Integration.
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Data Classification Techniques and Data Quality Management
This week, we learn Data Classification Techniques including Data Lineage and Impact Analysis and Metadata Management and Data Catalogs. We will also learn about the Data Profiling and Quality Assessment, Data Cleansing Techniques and implement Data Cleansing Pipelines in Databricks.
Data Privacy and Security
This week, we will learn about RBAC, Data Access Control Models and Data security policies in Databricks. We will also learn how to implement RBAC in Databricks, and Data Security Best practices.
Data Governance in Data Pipelines
This week, we will learn about Data Governance in Data Pipelines including Data Lineage in Data Pipelines, ETL/ELT processes, Data Versiong and Change Data Capture. We will also about Data Governance Best Practices and Tools including Continuous Improvement of Data Governance Processes and implementation and applying best practices in Data goverance.

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Suitable for professionals seeking a solid understanding in data governance, fraud detection, and prevention strategies
Assumes familiarity with Databricks platform and basic knowledge of AWS services
Emphasizes the benefits of using Databricks on AWS for data management and analytics
Provides hands-on lab demonstrations to reinforce learning
Incorporates assessments and graded quizzes to gauge understanding

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Data Governance with Databricks with these activities:
Study Data Warehouses
Refresh your understanding of data warehouses to strengthen your knowledge of the industry and how it relates to data governance.
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  • Review notes or tutorials on data warehouses
  • Take practice quizzes or tests on data warehouse concepts
AWS Fundamentals Review
Review AWS fundamentals to refresh your knowledge and ensure you have a solid foundation for the course.
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  • Go through your AWS notes or online resources.
  • Take a practice quiz or mock exam to assess your understanding.
Data Governance Concepts and Principles Practice
Practice answering multiple-choice questions to reinforce core data governance concepts and principles covered in the course.
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  • Review the provided data governance concepts material.
  • Take the practice quiz to test your understanding.
  • Refer to the course material to clarify any misunderstandings.
Nine other activities
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Practice Data Cleansing Pipelines in Databricks
Practice constructing data cleansing pipelines in Databricks to improve your data manipulation skills.
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  • Create a sample dataset with common data quality issues
  • Use Databricks to create a data cleansing pipeline to address the issues
  • Test the pipeline and evaluate its effectiveness
Databricks Query Optimization Techniques Practice
Practice applying query optimization techniques in Databricks to improve performance and efficiency.
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Show steps
  • Review the course material on query optimization.
  • Solve practice problems or coding exercises to apply the techniques.
  • Compare your solutions with optimal approaches.
Data Lineage and Data Versioning in Databricks Pipelines Tutorial
Follow an online tutorial to gain hands-on experience implementing data lineage and data versioning in Databricks pipelines.
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Show steps
  • Identify a suitable online tutorial on data lineage and data versioning in Databricks.
  • Set up the required environment and tools.
  • Follow the tutorial steps to implement data lineage and data versioning.
  • Test the implementation and troubleshoot any issues.
Data Governance Best Practices Workshop
Attend a workshop designed to share best practices, case studies, and insights on data governance implementation for improved data management.
Browse courses on Data Management
Show steps
  • Identify and register for a data governance best practices workshop.
  • Attend the workshop and actively participate in discussions.
  • Network with industry professionals and learn from their experiences.
  • Apply the knowledge and insights gained to enhance your organization's data governance strategy.
Implementing Data Security Best Practices in Databricks Tutorial
Follow a guided tutorial to learn and apply best practices for implementing data security in Databricks.
Browse courses on Data Security
Show steps
  • Identify a suitable online tutorial on data security best practices in Databricks.
  • Set up the required environment and tools.
  • Follow the tutorial steps to implement data security best practices.
  • Test the implementation and troubleshoot any issues.
Design a Data Governance Plan for a Fictional Organization
Create a comprehensive data governance plan to demonstrate your understanding of data management principles and their application in real-world scenarios.
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  • Define the scope and objectives of the data governance plan
  • Identify the key stakeholders and their roles and responsibilities
  • Develop data governance policies and procedures
  • Implement data governance tools and technologies
  • Monitor and evaluate the effectiveness of the data governance plan
Data Governance Policy Framework Creation
Create a data governance policy framework to solidify your understanding of data governance principles and their application.
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  • Research and gather information on data governance policy frameworks.
  • Draft a data governance policy framework tailored to your organization's needs.
  • Review and refine your framework with feedback from peers or mentors.
  • Present your framework to stakeholders for feedback and approval.
Contribute to the Apache Atlas Project
Contribute to open-source data governance initiatives to gain practical experience and connect with the broader data community.
Show steps
  • Review the Apache Atlas documentation
  • Identify areas where you can contribute
  • Submit a pull request with your contributions
  • Engage with the Apache Atlas community
Contribute to Open-Source Data Governance Projects
Engage with the open-source community by contributing to data governance projects to enhance your understanding and gain practical experience.
Show steps
  • Identify open-source data governance projects that align with your interests.
  • Join project communities and contribute in areas such as documentation, issue resolution, or feature development.
  • Collaborate with project maintainers and fellow contributors to learn from their expertise.
  • Share your insights and contribute to the growth of the data governance ecosystem.

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