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Databricks for Cloud Data Engineering, Analytics, and Machine Learning
Uplatz

Self-paced videos, Lifetime access, Study material, Certification prep, Technical support, Course Completion Certificate

Summary

Price
£100 inc VAT
Or £33.33/mo. for 3 months...
Study method
Online, On Demand 
Duration
53.1 hours · Self-paced
Qualification
No formal qualification
Certificates
  • Reed Courses Certificate of Completion - Free
  • Uplatz Certificate of Completion - Free

Overview

Uplatz offers this comprehensive course on Databricks for Cloud Data Engineering, Analytics, and Machine Learning. It is a self-paced course with video lectures. You will be awarded Course Completion Certificate at the end of the course.

Databricks is a cloud-based data platform that provides a unified environment for big data processing, analytics, and AI/ML workloads. Built on Apache Spark, it enables organizations to process large-scale data efficiently, integrate with various cloud services, and perform real-time analytics.

It follows the Lakehouse architecture, combining the best of data lakes and data warehouses to provide a scalable, secure, and collaborative data ecosystem.

Databricks is a powerful cloud-based data and AI platform that enables fast, scalable, and cost-efficient big data processing. With built-in ETL, machine learning, real-time analytics, and governance, it is an ideal choice for modern enterprises looking to leverage data for AI-driven decision-making.

Certificates

Curriculum

1
section
52
lectures
53h 4m
total

    • 1: Introduction to Databricks 1:15:23
    • 2: Databricks Platform Overview Preview 53:19
    • 3: Key Features of Databricks Workspace 1:08:34
    • 4: Databricks Architecture and Components 1:11:01
    • 5: Databricks vs. Traditional Data Platforms 1:12:49
    • 6: Setting up a Databricks Workspace 1:05:13
    • 7: Databricks Notebook Basics 1:04:23
    • 8: Importing and Organizing Datasets in Databricks 1:05:44
    • 9: Exploring Databricks Clusters 1:04:43
    • 10: Databricks Community Edition: Features and Limitations 1:05:39
    • 11: Introduction to ETL in Databricks 1:03:44
    • 12: Using Apache Spark with Databricks 1:05:21
    • 13: Working with Delta Lake in Databricks 1:03:14
    • 14: Incremental Data Loading using Delta Lake 1:03:37
    • 15: Data Schema Evolution in Databricks 1:03:19
    • 16: Running SQL Queries in Databricks 1:02:38
    • 17: Creating and Visualizing Dashboards 1:04:08
    • 18: Optimizing Queries in Databricks SQL 1:01:32
    • 19: Working with Databricks Connect for BI Tools 1:02:43
    • 20: Using the Databricks SQL REST API 1:00:41
    • 21: Introduction to Machine Learning with Databricks 1:00:49
    • 22: Feature Engineering in Databricks Preview 1:01:41
    • 23: Building ML Models with Databricks MLFlow 1:04:51
    • 24: Part 1 - Hyperparameter Tuning in Databricks 18:53
    • 25: Part 2 - Hyperparameter Tuning in Databricks 50:27
    • 26: Deploying ML Models with Databricks 1:05:34
    • 27: Integrating Databricks with Azure Data Factory 57:51
    • 28: Connecting Databricks with AWS S3 Buckets 57:54
    • 29: Databricks REST API Basics 1:03:33
    • 30: Connecting Power BI with Databricks 1:02:26
    • 31: Integrating Snowflake with Databricks 1:04:15
    • 32: Understanding Databricks Auto-Scaling Preview 1:00:29
    • 33: Cluster Performance Optimization Techniques 1:04:59
    • 34: Part 1 - Partitioning and Bucketing in Databricks 25:42
    • 35: Part 2 - Partitioning and Bucketing in Databricks 31:43
    • 36: Managing Metadata with Hive Tables in Databricks 1:01:02
    • 37: Cost Optimization in Databricks 1:01:29
    • 38: Securing Data in Databricks using Role-Based Access Control 39:45
    • 39: Setting up Secure Connections in Databricks 1:20:21
    • 40: Managing Encryption in Databricks 1:00:54
    • 41: Auditing and Monitoring in Databricks 1:09:23
    • 42: Real-Time Streaming Analytics with Databricks 1:02:20
    • 43: Data Warehousing Use Cases in Databricks 1:01:35
    • 44: Building Customer Segmentation Models with Databricks 1:01:28
    • 45: Predictive Maintenance using Databricks 1:01:40
    • 46: IoT Data Analysis in Databricks 1:00:44
    • 47: Using GraphFrames for Graph Processing in Databricks 1:01:09
    • 48: Time Series Analysis with Databricks 59:39
    • 49: Data Lineage Techniques in Databricks 1:02:02
    • 50: Building Custom Libraries for Databricks 1:02:55
    • 51: CI/CD Pipelines for Databricks Projects 1:05:05
    • 52: Best Practices for Managing Databricks Projects 1:22:39

Course media

Description

Databricks - Course Syllabus

1. Introduction to Databricks

  • Introduction to Databricks
  • What is Databricks? Platform Overview
  • Key Features of Databricks Workspace
  • Databricks Architecture and Components
  • Databricks vs Traditional Data Platforms

2. Getting Started with Databricks

  • Setting Up a Databricks Workspace
  • Databricks Notebook Basics
  • Importing and Organizing Datasets in Databricks
  • Exploring Databricks Clusters
  • Databricks Community Edition: Features and Limitations

3. Data Engineering in Databricks

  • Introduction to ETL in Databricks
  • Using Apache Spark with Databricks
  • Working with Delta Lake in Databricks
  • Incremental Data Loading Using Delta Lake
  • Data Schema Evolution in Databricks

4. Data Analysis with Databricks

  • Running SQL Queries in Databricks
  • Creating and Visualizing Dashboards
  • Optimizing Queries in Databricks SQL
  • Working with Databricks Connect for BI Tools
  • Using the Databricks SQL REST API

5. Machine Learning & Data Science

  • Introduction to Machine Learning with Databricks
  • Feature Engineering in Databricks
  • Building ML Models with Databricks MLFlow
  • Hyperparameter Tuning in Databricks
  • Deploying ML Models with Databricks

6. Integration and APIs

  • Integrating Databricks with Azure Data Factory
  • Connecting Databricks with AWS S3 Buckets
  • Databricks REST API Basics
  • Connecting Power BI with Databricks
  • Integrating Snowflake with Databricks

7. Performance Optimization

  • Understanding Databricks Auto-Scaling
  • Cluster Performance Optimization Techniques
  • Partitioning and Bucketing in Databricks
  • Managing Metadata with Hive Tables in Databricks
  • Cost Optimization in Databricks

8. Security and Compliance

  • Securing Data in Databricks Using Role-Based Access Control (RBAC)
  • Setting Up Secure Connections in Databricks
  • Managing Encryption in Databricks
  • Auditing and Monitoring in Databricks

9. Real-World Applications

  • Real-Time Streaming Analytics with Databricks
  • Data Warehousing Use Cases in Databricks
  • Building Customer Segmentation Models with Databricks
  • Predictive Maintenance Using Databricks
  • IoT Data Analysis in Databricks

10. Advanced Topics in Databricks

  • Using GraphFrames for Graph Processing in Databricks
  • Time Series Analysis with Databricks
  • Data Lineage Tracking in Databricks
  • Building Custom Libraries for Databricks
  • CI/CD Pipelines for Databricks Projects

11. Closing & Best Practices

  • Best Practices for Managing Databricks Projects

Who is this course for?

  1. Data Engineers:

    • Professionals responsible for building and maintaining data pipelines, ETL processes, and data infrastructure.

    • Those looking to leverage Databricks for scalable and efficient data processing in the cloud.

  2. Data Scientists:

    • Individuals focused on building machine learning models and performing advanced analytics.

    • Those interested in using Databricks for collaborative data science and ML workflows.

  3. Data Analysts:

    • Professionals who analyze data to derive insights and support decision-making.

    • Those who want to use Databricks for querying, visualizing, and analyzing large datasets.

  4. Cloud Architects:

    • Experts designing cloud-based data solutions and architectures.

    • Those interested in integrating Databricks with cloud platforms like AWS, Azure, or GCP.

  5. Machine Learning Engineers:

    • Professionals who deploy and operationalize machine learning models.

    • Those looking to use Databricks for end-to-end ML lifecycle management.

  6. IT Professionals and Developers:

    • Individuals involved in data platform management, DevOps, or software development.

    • Those seeking to learn how to use Databricks for data-driven applications.

  7. Business Intelligence (BI) Professionals:

    • Analysts and developers who create dashboards and reports.

    • Those interested in using Databricks for data preparation and integration with BI tools.

  8. Students and Aspiring Data Professionals:

    • Learners aiming to build a career in data engineering, analytics, or machine learning.

    • Those looking to gain hands-on experience with a leading cloud-based data platform.

Requirements

Passion and determination to achieve your goals!

Career path

  • Data Engineer
  • Big Data Engineer
  • Machine Learning Engineer
  • Data Scientist
  • AI Engineer
  • Data Analyst
  • Cloud Data Engineer
  • BI Developer
  • ETL Developer
  • Apache Spark Developer
  • Data Architect
  • Cloud Solutions Architect
  • Data Platform Engineer
  • Analytics Engineer
  • DevOps Engineer (DataOps)
  • Data Warehouse Engineer
  • Business Intelligence Analyst
  • Software Engineer (Big Data)
  • Cloud Engineer (Databricks & Spark)

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Legal information

This course is advertised on Reed.co.uk by the Course Provider, whose terms and conditions apply. Purchases are made directly from the Course Provider, and as such, content and materials are supplied by the Course Provider directly. Reed is acting as agent and not reseller in relation to this course. Reed's only responsibility is to facilitate your payment for the course. It is your responsibility to review and agree to the Course Provider's terms and conditions and satisfy yourself as to the suitability of the course you intend to purchase. Reed will not have any responsibility for the content of the course and/or associated materials.

FAQs

Study method describes the format in which the course will be delivered. At Reed Courses, courses are delivered in a number of ways, including online courses, where the course content can be accessed online remotely, and classroom courses, where courses are delivered in person at a classroom venue.

CPD stands for Continuing Professional Development. If you work in certain professions or for certain companies, your employer may require you to complete a number of CPD hours or points, per year. You can find a range of CPD courses on Reed Courses, many of which can be completed online.

A regulated qualification is delivered by a learning institution which is regulated by a government body. In England, the government body which regulates courses is Ofqual. Ofqual regulated qualifications sit on the Regulated Qualifications Framework (RQF), which can help students understand how different qualifications in different fields compare to each other. The framework also helps students to understand what qualifications they need to progress towards a higher learning goal, such as a university degree or equivalent higher education award.

An endorsed course is a skills based course which has been checked over and approved by an independent awarding body. Endorsed courses are not regulated so do not result in a qualification - however, the student can usually purchase a certificate showing the awarding body's logo if they wish. Certain awarding bodies - such as Quality Licence Scheme and TQUK - have developed endorsement schemes as a way to help students select the best skills based courses for them.
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