Data Warehouse Training in Radom

This instructor-led, live training in Radom (online or onsite) is aimed at advanced-level data professionals who wish to optimize data processing workflows, ensure data integrity, and implement robust data lakehouse solutions that can handle the complexities of modern big data applications. By the end of this training, participants will be able to:

• Gain an in-depth understanding of Iceberg’s architecture, including metadata management and file layout.
• Configure Iceberg for optimal performance in various environments and integrate it with multiple data processing engines.
• Manage large-scale Iceberg tables, perform complex schema changes, and handle partition evolution.
• Master techniques to optimize query performance and data scan efficiency for large datasets.
• Implement mechanisms to ensure data consistency, manage transactional guarantees, and handle failures in distributed environments.

This instructor-led, live training in Radom (online or onsite) is aimed at beginner-level data professionals who wish to acquire the knowledge and skills necessary to effectively utilize Apache Iceberg for managing large-scale datasets, ensuring data integrity, and optimizing data processing workflows. By the end of this training, participants will be able to:

• Gain a thorough understanding of Apache Iceberg's architecture, features, and benefits.
• Learn about table formats, partitioning, schema evolution, and time travel capabilities.
• Install and configure Apache Iceberg in different environments.
• Create, manage, and manipulate of Iceberg tables.
• Understand the process of migrating data from other table formats to Iceberg.

This instructor-led, live training in Radom (online or onsite) is aimed at intermediate-level IT professionals who wish to enhance their skills in data architecture, governance, cloud computing, and big data technologies to effectively manage and analyze large datasets for data migration within their organizations. By the end of this training, participants will be able to:

• Understand the foundational concepts and components of various data architectures.
• Gain a comprehensive understanding of data governance principles and their importance in regulatory environments.
• Implement and manage data governance frameworks such as Dama and Togaf.
• Leverage cloud platforms for efficient data storage, processing, and management.

This instructor-led, live training in Radom (online or onsite) is aimed at intermediate-level data engineers who wish to learn how to use Azure Data Lake Storage Gen2 for effective data analytics solutions. By the end of this training, participants will be able to:

• Understand the architecture and key features of Azure Data Lake Storage Gen2.
• Optimize data storage and access for cost and performance.
• Integrate Azure Data Lake Storage Gen2 with other Azure services for analytics and data processing.
• Develop solutions using the Azure Data Lake Storage Gen2 API.
• Troubleshoot common issues and optimize storage strategies.

This instructor-led, live training in Radom (online or onsite) is aimed at intermediate-level IT professionals who wish to have a comprehensive understanding of IBM DataStage from both an administrative and a development perspective, allowing them to manage and utilize this tool effectively in their respective workplaces. By the end of this training, participants will be able to:

• Understand the core concepts of DataStage.
• Learn how to effectively install, configure, and manage DataStage environments.
• Connect to various data sources and extract data efficiently from databases, flat files, and external sources.
• Implement effective data loading techniques.

This instructor-led, live training in Radom (online or onsite) is aimed at intermediate-level big data professionals who wish to utilize Apache Kylin for building real-time data warehouses and performing multidimensional analysis on large-scale datasets.By the end of this training, participants will be able to:

• Set up and configure Apache Kylin with real-time streaming data sources.
• Design and build OLAP cubes for both batch and streaming data.
• Perform complex queries with sub-second latency using Kylin's SQL interface.
• Integrate Kylin with BI tools for interactive data visualization.
• Optimize performance and manage resources effectively in Kylin.

This instructor-led, live training in Radom (online or onsite) is aimed at intermediate-level database administrators, developers, and analysts who wish to master advanced SQL functionalities for complex data operations and database management. By the end of this training, participants will be able to:

• Perform advanced querying techniques using unions, subqueries, and complex joins.
• Add, update, and delete data, tables, views, and indexes with precision.
• Ensure data integrity through transactions and manipulate database structures.
• Create and manage databases efficiently for robust data storage and retrieval.

Amazon Redshift is a petabyte-scale cloud-based data warehouse service in AWS. In this instructor-led, live training, participants will learn the fundamentals of Amazon Redshift. By the end of this training, participants will be able to:

• Install and configure Amazon Redshift
• Load, configure, deploy, query, and visualize data with Amazon Redshift

Audience

• Developers
• IT Professionals

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

Note

• To request a customized training for this course, please contact us to arrange.

The course is part of the Data Scientist skill set (Domain: Data and Technology).

In this instructor-led, live training in Radom, participants will learn how to build a Data Vault. By the end of this training, participants will be able to:

• Understand the architecture and design concepts behind Data Vault 2.0, and its interaction with Big Data, NoSQL and AI.
• Use data vaulting techniques to enable auditing, tracing, and inspection of historical data in a data warehouse.
• Develop a consistent and repeatable ETL (Extract, Transform, Load) process.
• Build and deploy highly scalable and repeatable warehouses.

Apache Druid is an open-source, column-oriented, distributed data store written in Java. It was designed to quickly ingest massive quantities of event data and execute low-latency OLAP queries on that data. Druid is commonly used in business intelligence applications to analyze high volumes of real-time and historical data. It is also well suited for powering fast, interactive, analytic dashboards for end-users. Druid is used by companies such as Alibaba, Airbnb, Cisco, eBay, Netflix, Paypal, and Yahoo. In this instructor-led, live course we explore some of the limitations of data warehouse solutions and discuss how Druid can compliment those technologies to form a flexible and scalable streaming analytics stack. We walk through many examples, offering participants the chance to implement and test Druid-based solutions in a lab environment. Format of the Course

• Part lecture, part discussion, heavy hands-on practice, occasional tests to gauge understanding

This instructor-led, live training in Radom (online or onsite) is aimed at administrators who wish to set up Greenplum Database for business intelligence and data warehousing solutions. By the end of this training, participants will be able to:

• Address processing needs with Greenplum.
• Perform ETL operations for data processing.
• Leverage existing query processing infrastructures.

Snowflake is a cloud-based data storage platform that offers extensive capabilities for processing and analyzing large datasets. It is a Software-as-a-Service (SaaS) solution that combines the flexibility and scalability of the cloud with the capabilities of an advanced data warehouse. Key features and functions of Snowflake include: Cloud-Based Architecture: Snowflake is built in the cloud and optimized for flexibility and performance, allowing for easy scalability of data storage and processing resources. Separation of Storage and Compute Workloads: Snowflake's unique architecture separates the data storage workload from the compute workload, allowing for independent scaling and optimization of these two aspects. Support for Diverse Data: Snowflake enables the storage and analysis of various data types, including structured, semi-structured (e.g., JSON, XML), and unstructured data. High Performance and Query Optimization: The system offers fast query processing and automatic optimization, enabling efficient execution of complex analyses on large datasets. Security and Access Management: Snowflake provides advanced security features, including data encryption, role-based access management, and auditing. Easy Integration: The platform easily integrates with popular ETL, BI, and data analysis tools and services, allowing for the creation of comprehensive analytical solutions. Cost Flexibility and Scalability: A usage-based payment model allows for cost optimization and paying only for the resources actually used. Versatility of Applications: Snowflake is suitable for various use cases, from simple BI analyses to advanced data processing, including big data analyses, machine learning, and more. Snowflake is a popular choice among organizations of all sizes due to its flexibility, performance, ease of use, and the ability to adapt to various business and technological needs.