Databricks and PySpark for Data Engineering Training Course

Azure Databricks functions as a cloud-based collaborative infrastructure that facilitates big data analytics and machine learning initiatives leveraging Apache Spark and the Microsoft Azure environment.

This instructor-led live training program, available in online or on-site formats, is targeted at intermediate-level data professionals seeking to design, construct, and deploy machine learning models for financial applications using Azure Databricks.

Upon completion of this training, participants will possess the capability to:

• Develop and train machine learning models utilizing financial data within Azure Databricks.
• Utilize MLflow for experiment tracking, model governance, and solution deployment.
• Apply models to identify anomalies, evaluate risks, or project trends within the financial domain.
• Integrate machine learning models into scalable pipelines for ongoing assessment.

Instructional Methodology

• Interactive lectures and facilitated discussions.
• Extensive exercises and practical application tasks.
• Direct hands-on implementation within a live-laboratory environment.

Options for Course Customization

• For inquiries regarding a customized training program for this course, please contact our office to coordinate arrangements.

This instructor-led live training, delivered in US (online or onsite), is designed for data scientists and developers responsible for establishing, deploying, and managing data analytics solutions for government using Databricks.

Upon completion of this training, participants will be equipped to:

• Configure and provision Databricks environments.
• Analyze the functional integration between Databricks and Apache Spark.
• Apply methods for loading and transforming data within Databricks.

Databricks is a unified Lakehouse platform that integrates Spark, Delta Lake, and governance features (Unity Catalog) to support scalable data engineering and analytics workflows for government operations.

This instructor-led, live training (online or onsite) is designed for intermediate-level technology managers with a data engineering background who intend to migrate complex procedural OLAP logic to a Lakehouse architecture using Databricks, Spark, Delta Lake, Unity Catalog, and native workflows.

Upon completion of this training, participants will be able to:

• Explain Lakehouse architecture and the Bronze→Silver→Gold (Medallion) pattern.
• Translate stored-procedure logic into Spark DataFrame and notebook implementations.
• Design and implement incremental ingestion, merge, and optimization routines using Delta Lake.
• Build end-to-end orchestrated pipelines with Databricks Workflows, version control, testing, and governance.

Format of the Course

• Intensive, instructor-led sessions with focused demonstrations and explanations.
• Daily hands-on labs using representative datasets and migration exercises.
• Guided code reviews, performance tuning clinics, and workflow orchestration practice.

Course Customization Options

• This course can be tailored to your environment, datasets, and governance requirements; please contact us to arrange customization.

Databricks serves as a unified analytics platform engineered for big data processing and machine learning within collaborative operational environments.

This instructor-led training session, available online or in-person, is tailored for entry-level to intermediate data analysts and financial professionals seeking to execute large-scale data analysis using Databricks for financial applications relevant to government operations.

Upon completion of this training, participants will be equipped to:

• Leverage Databricks notebooks to interrogate and analyze financial datasets for government use cases.
• Sanitize and transform raw financial data to facilitate accurate reporting and insight generation.
• Develop dashboards and visualizations to support performance monitoring and accountability.
• Engage in real-time collaboration with team members using shared notebook environments.

Instructional Methodology

• Interactive instruction and facilitated discussion.
• Extensive practical exercises and proficiency-building activities.
• Practical implementation within a live laboratory environment.

Customization of Course Content

• To initiate a request for customized training aligned with specific agency needs, please contact our coordination team.

Python serves as a high-level programming language recognized for its syntactic clarity and code readability. Apache Spark functions as a distributed data processing engine designed for querying, analyzing, and transforming large-scale datasets. PySpark enables developers to leverage these capabilities by interfacing with the Spark engine through the Python programming language.

Target Audience: Mid-level professionals within the banking sector who possess working knowledge of Python and Spark, and who aim to enhance their competencies in big data processing and machine learning for government applications.

This training provides a practical foundation for establishing scalable data processing and Machine Learning workflows utilizing PySpark. Participants will examine the function of Apache Spark within contemporary Big Data ecosystems and acquire methods for efficiently processing extensive datasets through distributed computing principles.

This three-day practical course focuses on building and optimizing efficient data-processing workloads for government using PySpark, Pandas, and Polars in Kubernetes-based environments.

Participants will develop a practical understanding of how Spark applications execute on Kubernetes and how application-level configuration decisions influence performance, scalability, resource consumption, and cost. The course covers key optimization areas including executor sizing, memory allocation, dynamic allocation, partitioning strategies, shuffle behavior, small-file problems, and efficient Parquet processing.

The course also addresses common challenges when working with Pandas, including memory limitations and out-of-memory failures, and introduces Polars as a high-performance alternative for selected data-processing workloads. Through hands-on exercises, participants will diagnose performance and memory issues, compare different configuration strategies, and apply optimization techniques to realistic ETL and machine learning scenarios.

The emphasis throughout the course is on practical decision-making: understanding how to identify bottlenecks, select the appropriate tool, configure Spark efficiently, and balance performance with infrastructure resource consumption and cost.

In this instructor-led live training held in US, participants will acquire the skills to utilize Python and Spark for the analysis of big data, supported by practical, hands-on exercises.

Upon completion, participants will be equipped to:

• Apply Spark and Python for the analysis of Big Data for government purposes.
• Execute exercises that replicate real-world operational scenarios.
• Utilize specialized tools and methodologies for big data analysis using PySpark.

Stratio is a comprehensive data management platform that consolidates big data, artificial intelligence, and governance frameworks into a unified solution. Its Rocket and Intelligence modules facilitate rapid data exploration, transformation, and advanced analytics for enterprise-scale operations.

This instructor-led training, available online or in-person, is designed for intermediate-level data professionals seeking to proficiently utilize the Rocket and Intelligence modules within Stratio. The curriculum emphasizes the application of PySpark, with specific focus on iterative structures, user-defined functions, and complex data logic for government and public sector applications.

Upon completion of this training, participants will demonstrate the ability to:

• Operate and navigate the Stratio platform utilizing the Rocket and Intelligence modules.
• Apply PySpark techniques for data ingestion, transformation, and analytical processes.
• Employ iterative logic and conditional structures to manage data workflows and feature engineering tasks.
• Develop and maintain user-defined functions (UDFs) to support reusable data operations in PySpark.

Training Delivery Format

• Interactive instruction and facilitated discussion.
• Extensive practice exercises and applied scenarios.
• Practical implementation within a live laboratory environment.

Curriculum Customization Options

• To request a customized training program for this course, please contact the administration for arrangements.