How to Become a Data Engineer — I

… using Databricks Learning & Trainings Materials

Part I — The Fundamentals

• Leverage Databricks’ comprehensive learning materials developed by industry experts to gain practical hands-on experience in data engineering and analytics.
• Use Databricks’ certification programs to validate your skills and expertise as a data engineer and improve your credibility and marketability with potential employers.

Note: This is a slightly shortened version of my article on Medium

Introduction

Data Engineers are central to maximizing the potential of the Lakehouse platform to enable data-driven decisions and innovation. They manage large-scale data processing and ensure robust pipelines, data integrity and performance optimization by leveraging various tools and governance practices for seamless integration and utilization of data resources.

The first part covers essential aspects for aspiring data engineers, from mastering basic tools and techniques to understanding complex data processing and management principles. By diving into these fundamentals, participants can pave their way to becoming an indispensable part of modern data-driven organizations.

Overview

Why Databricks: Databricks provides industry-leading expertise, hands-on experience, and insights into cutting-edge technologies. Aspiring data engineers benefit from community support, certifications, and advanced resources, empowering them to excel in data engineering careers with updated insights and guidance.

Data Engineer: Data engineers are critical to building data processing infrastructure, managing pipelines, ELT processes and data cleansing. From junior to senior positions, they ensure the organization and accessibility of data, enabling data-driven decision making and strategic planning for business success.

Data Lakehouse: The Data Lakehouse seamlessly integrates data lakes and warehouses and provides scalable storage and processing. Using Apache Spark and Delta Lake, it addresses the challenges of siloed systems and supports real-time processing, ACID compliance, schema development and governance. This unified solution improves data management, quality and collaboration.

Data Engineering — The Basics: Data engineers need expertise in platform management, ETL processes, incremental data processing, production pipelines and data governance. These skills enable them to design robust data solutions, optimize performance, ensure integrity and maintain reliability. Mastery of these areas is critical to the success of data-driven initiatives in organizations and passing the Databricks Certified Data Engineer Associate exam.

Data Engineering — Advanced Techniques: Professional data engineers must have proficiency in tooling, data processing, modeling, security, governance, monitoring and testing. These skills are critical to developing robust data solutions, ensuring integrity and maintaining reliability. Passing the Databricks Certified Data Engineer Professional exam requires mastery of advanced data engineering tasks using the associated tools.

Preparing for Interviews: Preparing for data engineering job interviews requires mastering data architectures, programming languages, and problem-solving skills. Adaptability and collaboration are essential. Thorough preparation enhances prospects and contributes to personal and professional growth. A collection of resources, including interview questions and answers, aids in preparation.

Why Databricks?

The use of Databricks learning material is of great benefit to aspiring data engineers for several reasons:

• Industry-leading expertise: Databricks is renowned for its expertise in data engineering and analytics, offering comprehensive learning materials developed by industry experts.
• Practical, hands-on experience: Databricks learning materials often include practical exercises and projects that allow learners to apply theoretical knowledge in real-world scenarios, honing their skills through hands-on experience.
• Cutting-edge technologies: Databricks frequently updates its learning materials to incorporate the latest advancements and best practices in data engineering, ensuring learners stay abreast of industry trends and technologies.
• Community support: Databricks provides access to a vibrant community of data professionals where learners can seek guidance, share insights, and collaborate on projects, fostering a supportive learning environment.
• Certification opportunities: Databricks offers certification programs that validate the skills and expertise of data engineers, providing learners with a valuable credential to showcase their proficiency to employers.
• Advanced learning opportunities: Databricks offers advanced learning materials for data engineers, covering emerging fields like generative AI, providing specialized insights and practical guidance.

Databricks - Learn: Let us help you on your lakehouse journey with Databricks. With our online resources, training and certification…

By using Databricks learning materials, aspiring data engineers can gain the knowledge, skills and practical experience they need for their career.

Data Engineer

Data engineers are central to organizations and are tasked with building an infrastructure for collecting, storing, converting and managing data. They ensure the accessibility and organization of data and facilitate analysis by stakeholders, data scientists and analysts. Their tasks include the development and management of data pipelines, ETL processes and data cleansing to ensure the suitability of data for analysis. Data Engineers can specialize in different areas such as data storage, programming or analysis, depending on the company's requirements.

As they progress from junior to senior positions, they take on management and strategic planning tasks. Typical projects include determining data requirements, extracting and preparing data and creating access endpoints. Ultimately, data engineers empower organizations to leverage data assets for business success and decision making. Over the course of their career, they adapt to evolving technological and industry-specific requirements, continuously optimize data infrastructure and drive innovation within the organization.

Databricks - Data Engineer: Tens of millions of production workloads run daily on Databricks

Data Lakehouse

Data engineers should understand the concept of the data lakehouse, as it represents a significant evolution in data management, seamlessly integrating the benefits of data lakes and data warehouses to provide scalable storage and processing capabilities essential for modern organizations.

Purpose and Architecture: A data lakehouse serves as a comprehensive data management system for modern organizations that require scalable storage and processing capabilities. It addresses the challenges that arise from siloed systems used for different workloads such as machine learning (ML) and business intelligence (BI). The architectural design follows a medallion pattern, where data is extended incrementally through different implementation and transformation phases. The core technologies in Databricks Lakehouse are Apache Spark, Delta Lake for optimized storage with ACID transactions and schema enforcement, and Unity Catalog for unified, fine-grained governance for data and AI.

Data Ingestion: At the first layer, data arrives in raw formats through batch or streaming processes. This logical layer provides a landing pad for raw data prior to conversion to delta tables. Delta Lake’s schema enforcement capabilities ensure data compliance during this process. Unity Catalog is used for table registration, alignment with data governance models, and setting data isolation boundaries, which are essential for maintaining data privacy and security.

Data Processing, Curation, and Integration: Once the data is verified, the curation and refinement phase begins. Data scientists and machine learning experts work together, combining or creating new features and performing thorough data cleansing. The schema-on-write approach combined with delta schema evolution capabilities enables changes to this layer without disrupting downstream logic, providing the flexibility to adapt to changing business needs.

Data Serving: The final layer provides end users with clean, enriched data designed for different use cases. A unified governance model ensures that data provenance can be traced back to the single source of truth. Optimized data layouts for different tasks enable end users to access data for machine learning, data engineering, business intelligence and reporting applications.

Capabilities of a Databricks Lakehouse: A Databricks Lakehouse replaces the dependencies of traditional data lakes and warehouses and offers a range of features. These include real-time data processing, data integration for a single source of truth, schema evolution to adapt to changing business needs, data transformations for speed and reliability, and data analytics and reporting with an engine optimized for warehousing workloads. It also supports machine learning and AI, data versioning, data lineage tracking, comprehensive data governance, data sharing and operational analytics.

Data Warehouse vs. Data Lake vs. Data Lakehouse: The distinction between lakehouse and data lake or data warehouse is critical. While data warehouses have formed the basis for BI decisions for decades, their limitations lie in query speed and adaptability to changing data. Data lakes, on the other hand, store and process various data efficiently, but do not provide support for BI reports due to their non-validated nature.

The data lakehouse combines the benefits of data lakes and data warehouses, offering open access to data stored in standard formats. It employs optimized indexing for machine learning and data science tasks, ensuring low query latency and high reliability for business intelligence and advanced analytics.

Documentation

What Is a Lakehouse? Learn more about the new data management paradigm data lakehouses — its evolution, adoption, common use cases, and its…

Data Lakehouse Architecture: Lakehouse architecture combines the best of data lakes and data warehouses to help you reduce costs and deliver any AI…

What is a data lakehouse? Use Databricks in a data lakehouse paradigm for generative AI, ACID transactions, data governance, ETL, BI, and machine…

Ebooks

Lakehouse — Combine the Best of Data Warehouses and Data Lakes: The Delta Lake Series Learn how to combine the best elements of data warehouses and data lakes with a data lakehouse…

Rise of the Data Lakehouse: Explore why lakehouses are the data architecture of the future with the father of the data warehouse, Bill Inmon.

Delta Lake: The Definitive Guide: This early-release eBook “Delta Lake: The Definitive Guide” by O’Reilly, will help you simplify your toughest data…

Data has a new destination. Here's your guide: You might've heard that the lakehouse is opening up a new era of data and AI. But before you can run a data lakehouse…

Webinars

Advantage Lakehouse: Available on demand The lakehouse has emerged as the ideal data architecture for the new era where data, analytics and…

Delta Lake: The Foundation of Your Lakehouse: Available on-demand As an open format storage layer, Delta Lake delivers reliability, security and performance to data…

Build Your Lakehouse: Available on demand 3-part training series Get hands-on with the lakehouse Take a deeper dive into the lakehouse and…

Training & Accreditation

Earn a Lakehouse Fundamentals accreditation by watching four brief tutorial videos and passing the knowledge test. Videos cover topics like Data Lakehouse, Databricks Lakehouse Platform, Platform Architecture, and Security Fundamentals, as well as Supported Workloads on the Databricks Lakehouse Platform.

Free Training: Databricks Lakehouse Fundamentals: The Lakehouse architecture is quickly becoming the new industry standard for data, analytics, and AI. Get up to speed…

Data Engineering — The Basics

The fundamental knowledge areas that Databricks data engineers should possess can be divided into five sections: Lakehouse Platform, ELT with Apache Spark, Incremental Data Processing, Production Pipelines, and Data Governance.

Databricks Lakehouse Platform Data engineers should understand the relationship between the data lakehouse and the data warehouse, recognizing the improvement in data quality in the former over the latter. They must be able to compare and contrast silver and gold tables and identify which workloads utilize bronze silver or gold tables as sources. Familiarity with the elements of the Databricks platform architecture and differentiating between all-purpose clusters and job clusters is crucial. They should also know how to manage clusters, use multiple languages within notebooks, and utilize Databricks Repos for CI/CD workflows.

ELT with Apache Spark Proficiency in Extract, Load, Transform (ELT) processes with Apache Spark is essential. Data engineers should be able to extract data from various sources, create views and tables, and deduplicate rows. They should understand data validation techniques, data casting, and data manipulation functions. Knowledge of array functions, JSON parsing, and SQL UDFs is necessary. Additionally, they should comprehend data pivoting, security models for sharing SQL UDFs, and CASE/WHEN usage for custom control flow.

Incremental Data Processing Understanding incremental data processing is critical for data engineers. They should know where Delta Lake provides ACID transactions and the benefits of such transactions. Knowledge of managing and querying tables, inspecting directory structures, and reviewing transaction histories is vital. Data engineers should also understand the significance of Partitioning and Z-Ordering, vacuuming, and compaction in Delta Lake tables. They must be proficient in using MERGE commands, COPY INTO statements, and implementing Delta Live Table (DLT) pipelines. Troubleshooting skills for DLT syntax and change data capture are also necessary.

Production Pipelines Proficiency in setting up and managing production pipelines is crucial for data engineers. They should understand the benefits of using multiple tasks in workflow jobs, setting up predecessor tasks, and scheduling with CRON. Troubleshooting failed tasks, setting up retry policies, and creating alerts for failures are essential skills. Data engineers should be capable of reviewing task execution history and sending alerts via email.

Data Governance Data governance is an essential aspect of data engineering. Data engineers should identify different areas of data governance and understand the differences between metastores and catalogs. Knowledge of Unity Catalog (UC) securables, service principals, and cluster security modes compatible with Unity Catalog is required. They should be proficient in creating UC-enabled clusters and DBSQL warehouses. Implementing data object access control and best practices such as colocating metastores with workspaces and using service principals for connections are essential for effective data governance.

To summarize, data engineers need to have a variety of skills that include platform management, ELT processes, incremental data processing, production pipelines, and data governance. Mastering these areas enables data engineers to design robust data solutions, optimize performance, ensure data integrity, and maintain system reliability. By continually expanding their knowledge and skills in these areas, data engineers can contribute significantly to the success of data-driven initiatives in organizations.

Documentations

Databricks - Data Engineering: Tens of millions of production workloads run daily on Databricks

Databricks data engineering: Learn how get started with the Databricks data engineering tools and features.

Run your first ETL workload on Databricks: Learn how to use Databricks to quickly develop and deploy your first ETL pipeline for data orchestration.

Data Governance: What is data governance? It describes the processes, policies, tech and more that organizations use to manage and get…

Ebooks

Translate raw data into actionable data: Get the latest data engineering best practices Keep up with the latest trends in data engineering by downloading your…

Big Book of Data Engineering: 2nd Edition: Learn about data engineering on the lakehouse Stay up to date with the latest technical guidance for data engineers by…

Build fast, reliable data pipelines: For data engineers looking to leverage the immense growth of Apache SparkTM and Delta Lake to build faster and more…

The Data Engineer's Guide to Apache Spark?: For data engineers looking to leverage Apache Spark?'s immense growth to build faster and more reliable data pipelines…

Webinars

Simplify ETL Pipelines on the Databricks Lakehouse: Available on demand Data reliability and performance Easily extract, transform and load both batch and streaming data…

Building Production-Ready Data Pipelines on the Lakehouse: Available on demand When you're running complex, multistage data pipelines in production, a lot can go wrong. Avoid…

The Best Data Engineering Platform Is a Lakehouse: Available on demand Easily ingest and transform batch and streaming data with reliable production workflows on a single…

Trainings & Certifications

Get Started With Data Engineering on Databricks Understand basic data engineering concepts in 90 minutes in a self-paced Databricks course and earn a certificate. The course consists of four concise tutorial videos covering the core components of the Databricks Lakehouse platform, workspace navigation, cluster management, Git integration, and Delta Lake table creation.

Get Started With Data Engineering on Databricks: Learn data engineering basics in 90 minutes There's an increasing demand for data, analytics and AI talent in every…

Databricks Certified Data Engineer Associate Exam The Databricks Certified Data Engineer Associate exam evaluates proficiency in introductory data engineering tasks using the Databricks Lakehouse Platform. It covers understanding the platform’s workspace, architecture, and capabilities. Candidates demonstrate skills in performing multi-hop architecture ETL tasks using Apache Spark? SQL and Python in batch and incremental processing. The exam assesses the ability to deploy basic ETL pipelines, Databricks SQL queries, and dashboards while managing entity permissions. Successful candidates are capable of completing basic data engineering tasks using Databricks and its associated tools.

Databricks - Databricks Certified Data Engineer Associate: The Databricks Certified Data Engineer Associate certification exam assesses an individual's ability to use the…

Udemy: Databricks Certified Data Engineer Associate — Preparation Learn to utilize the Databricks Lakehouse Platform and its tools effectively with this course of Derar Alhussein. Develop ETL pipelines employing Apache Spark SQL and Python, processing data incrementally in batch and streaming modes. Orchestrate production pipelines seamlessly and ensure adherence to best security practices within the Databricks environment.

Data Engineering — Advanced Techniques

Professional data engineers play a crucial role in all modern organizations, ensuring efficient data processing, modeling, security, governance, and monitoring. Essential knowledge areas that professional Databricks data engineers should master covers Databricks tooling, data processing, data modeling, security & governance, monitoring & logging, and testing & deployment.

Databricks Tooling Data engineers should have a solid understanding of Databricks tooling, particularly Delta Lake, a powerful data storage layer. They should comprehend how Delta Lake uses the transaction log and cloud object storage to ensure atomicity and durability. They should be familiar with optimistic concurrency control and basic functionalities like Delta clone. Mastery of common Delta Lake indexing optimizations, partitioning strategies, and optimization for Databricks SQL service is also required.

Data Processing Proficiency in data processing techniques is vital for advanced data engineers. This section covers batch and incremental processing methods and optimization techniques. It emphasizes understanding partitioning strategies and applying partition hints like coalesce, repartition, and rebalance. Advanced data engineers should be capable of updating records in Spark tables and implementing design patterns using Structured Streaming and Delta Lake. They must also know how to leverage Change Data Feed (CDF) on Delta Lake tables for efficient processing.

Data Modeling Data modeling is an important aspect of data engineering. Data engineers should grasp the objectives of data transformations during promotion from bronze to silver stages. They should understand how Change Data Feed (CDF) addresses challenges in propagating updates and deletes within Lakehouse architecture. Implementing Delta Lake clone, designing multiplexed bronze tables, and applying incremental processing, quality enforcement, and deduplication techniques are essential skills. Proficiency in designing Slowly Changing Dimension tables using Delta Lake is also required.

Security & Governance Data security and governance are paramount in data engineering. Advanced data engineers should know how to create dynamic views for data masking and access control to rows and columns. Understanding compliance requirements and implementing appropriate security measures, such as table constraints to prevent bad data from being written, is essential. They must ensure that data access and manipulation adhere to organizational policies and regulatory standards.

Data engineering on Databricks benefits from foundational components like Unity Catalog and Delta Lake. Delta Lake optimizes raw data storage, providing reliability through ACID transactions and scalable metadata handling with high performance. Unity Catalog ensures fine-grained governance for all data assets, simplifying data discovery, access, and sharing across clouds. It also supports Delta Sharing, an open protocol for secure data sharing between organizations.

Monitoring & Logging Effective monitoring and logging are essential for maintaining data pipelines and ensuring system reliability. Advanced data engineers should be proficient in analyzing performance metrics and event timelines using tools like the Spark UI, Ganglia UI, and Cluster UI. They should be able to diagnose performance issues, debug failing applications, and design systems that meet cost and latency SLAs. Deploying and monitoring streaming and batch jobs to ensure smooth operation is also required.

Testing & Deployment Testing and deployment are critical phases in the data engineering lifecycle. Advanced data engineers should understand notebook dependency patterns, Python file dependencies, and job configurations. They must be proficient in using the Databricks CLI and REST API for job management and deployment. They should be capable of repairing and rerunning failed jobs and creating multi-task jobs with dependencies. Adhering to best practices in testing and deployment ensures the reliability and efficiency of data pipelines.

Documentation

Databricks -Data Engineering: Tens of millions of production workloads run daily on Databricks

Databricks - Migrate to Databricks: Migrate to Databricks to reduce costs, innovate faster and simplify your data platform.

Databricks data engineering: Learn how get started with the Databricks data engineering tools and features.

Ebooks

Big Book of Data Engineering: 2nd Edition: Learn about data engineering on the lakehouse Stay up to date with the latest technical guidance for data engineers by…

Delta Lake: The Definitive Guide: This early-release eBook "Delta Lake: The Definitive Guide" by O'Reilly, will help you simplify your toughest data…

Delta Lake: Up & Running by O'Reilly: Learn how to create your first Delta table. You'll also tackle challenges such as concurrent reads and writes, data…

Webinars

Modern Data Engineering with Azure Databricks: Available On-Demand Organizations across Latin America want to leverage the wealth of data accumulated in their data…

Data Engineering in the Age of AI: Data engineers will be the heroes of the AI revolution because they deliver the data for AI initiatives. Learn how at…

Trainings & Certifications

Databricks Certified Data Engineer Professional Exam The Databricks Certified Data Engineer Professional exam evaluates proficiency in advanced data engineering tasks using the Databricks platform and associated tools. It covers Apache Spark?, Delta Lake, MLflow, and the Databricks CLI and REST API. Candidates demonstrate skills in building optimized ETL pipelines, modeling data into a lakehouse, and ensuring pipeline security, reliability, monitoring, and testing. Successful candidates are proficient in performing advanced data engineering tasks using Databricks and associated tools.

Databricks - Databricks Certified Data Engineer Professional: The Databricks Certified Data Engineer Professional certification exam assesses an individual's ability to use the…

Databricks Academy: Data Engineering with Databricks (60 lessons, 12h) This course prepares data professionals to leverage the Databricks Lakehouse Platform to productionalize ETL pipelines. Students will use Delta Live Tables to define and schedule pipelines that incrementally process new data from a variety of data sources into the Lakehouse. Students will also orchestrate tasks with Databricks Workflows and promote code with Databricks Repos.

Udemy: Databricks Certified Data Engineer Professional -Preparation Learn to model data solutions on Databricks Lakehouse and create processing pipelines using Spark and Delta Lake APIs with this course of Derar Alhussein. Explore the benefits of the Databricks platform and its tools, while adhering to best practices for secure and governed production pipelines. Gain insights into monitoring and logging production jobs, and learn best practices for deploying code on Databricks efficiently.

Udemy: Databricks Data Engineer Professional — Practice Exams This course offers practice tests for the Databricks Data Engineer Professional certification exam. With 180 questions in 3 tests aligned to the Databricks syllabus, they simulate the actual exam experience. Each question is followed by a detailed explanation that provides insight into the topic and concept. Additionally, the code-based questions include Databricks Notebooks for hands-on practice.

Creating Your Resume

A well-crafted CV is crucial for prospective data engineers as it makes a great first impression on potential employers. It succinctly highlights their relevant skills, experience and achievements and emphasizes their suitability for the job. A compelling resume can catch the attention of recruiters, increasing the likelihood of getting an interview and ultimately landing a coveted data engineer position in the competitive job market.

15 Data Engineer Resume Examples That Work in 2024

As data engineering roles vary from entry-level to senior and lead positions, resumes must adapt to showcase the appropriate qualifications and experience for each level.

• An entry-level computing resume highlights educational achievements, such as a bachelor's degree in computer science and a high grade point average. It may also include relevant awards, internships and hobbies to show skills and dedication.
• A mid-level data engineer resume focuses on career history and specialization, highlighting valuable experience and skills gained over several years.
• A senior data engineer resume focuses on leadership, management and specialization and often includes detailed career summaries and quantifiable metrics that demonstrate extensive expertise.

Optimize your resume by highlighting potential leadership skills and specializations, tailoring it to the job, quantifying your impact and highlighting relevant expertise in a concise format.

• Tailor your resume: Customize your resume for each job by aligning your skills and experiences with the job description, showcasing relevant projects, and highlighting your qualifications.
• ?????????????? ???????????????????? ?????? ??????????: Project experience is vital. Clearly outline each project's scope, your role, and the technologies used. This showcases your expertise in handling various data engineering tasks to hiring managers.
• Quantify impact: Emphasize how your data engineering work has benefited the business. Show increased sales, cost reductions, or improved efficiency. This demonstrates your ability to align technical skills with company objectives.
• Include relevant skills: Focus on hard skills specific to data engineering, like programming languages (Python, Scala), data processing tools (Spark), databases (SQL, NoSQL), cloud platforms (AWS, Azure), and orchestration tools (Airflow).

15 Data Engineer Resume Examples That Work in 2024

Crafting Your Data Engineering Resume: Tips + Examples

Creating a strong data engineer resume is critical to showcasing expertise and securing job opportunities. This comprehensive guide provides valuable tips on structuring and presenting your resume to effectively highlight your skills and experience. From choosing the right format, such as reverse chronological or functional, to highlighting in-demand technical skills such as programming languages and data warehousing platforms, each section offers actionable advice tailored specifically to data engineers.

In addition, the resource emphasizes the importance of quantifying accomplishments and tailoring the resume to the specific job application to ensure it is of interest to potential employers. By following these guidelines, data engineers can create resumes that effectively convey their qualifications and secure desired positions.

Crafting Your Data Engineering Resume: Tips + Examples

Further Resources

21 Data Engineer Resume Examples & Guide for 2024

17 Data Engineer Resume Examples to Land You a Role in 2023

How To Create a Data Engineering Resume (With Template and Example)

Preparing for Interviews

Comprehensive preparation for job interviews is essential for data engineers. Mastery of data architectures, programming languages and database management systems is essential. In addition, it is crucial to demonstrate problem-solving skills, adaptability to new technologies and the ability to collaborate. Careful preparation enables data engineers to present their expertise effectively and improve their prospects of getting the jobs they want. This not only enhances their personal and professional growth, but also ensures that they are able to make a significant contribution to the success of the organizations in which they choose to work.

How Do Data Engineering Interview Works in Big Companies?

Data Engineering Interview Questions

14 Data Engineer Interview Questions and How to Answer Them

The Top 21 Data Engineering Interview Questions and Answers

Top 80+ Data Engineer Interview Questions and Answers

Data Engineer Interview Questions And Answers (2024)

Top 101+ Data Engineer Interview Questions and Answers

How to Make People Respect You in Seconds

Conclusion

Databricks offers a wealth of learning resources for aspiring data engineers, providing industry-leading expertise, hands-on experience and cutting-edge technologies to keep pace with industry trends. Through vibrant community support, certification opportunities and advanced learning materials, data engineers can gain valuable skills and qualifications.

Databricks learning resources cover essential techniques in platform management, ELT processes, incremental data processing, production pipelines and data governance, enabling engineers to develop robust data solutions, optimize performance, ensure data integrity and maintain system reliability. Whether it's mastering fundamental concepts or keeping up to date with the latest technologies, Databricks empowers data engineers to drive innovation, make informed decisions and contribute significantly to the success of data-driven initiatives in organizations.

Becoming a Data Engineer is not just a career choice, it is an opportunity to shape the future of data-driven innovation.

In part two, learn about advanced data processing optimization capabilities with Databricks Best Practices covering GDPR, CCPA compliance, data streaming, warehousing and Databricks’ role in real-time analytics. Discover industry-specific tools and partner-developed solutions through Databricks Solution Accelerators and the Brickbuilder program.

Further References

Databricks Blog: Get product updates, Apache Spark best-practices, use cases, and more from the Databricks team.

Databricks - Learn: Let us help you on your lakehouse journey with Databricks. With our online resources, training and certification…

Databricks - Resources: Read more of Databricks' resources that include customer stories, ebooks, newsletters, product videos and webinars.

Learning: Hi,I was taking databricks data engineering associate exam today.While taking exam , I got 2 pops saying internet is…

Databricks - Training & Certification: With training and certification through Databricks Academy, you will master the Data Intelligence Platform for all your…

Certification Exam Tips

just some of the many exam preparation tips that can be found on the Internet

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Top Tips to Pass the Databricks Certified Data Engineer Professional Exam: This is a guide to passing the Databricks Certified Data Engineer Professional Exam, including top tips and recommended…