HBase for Developers Training Course

Audience:

This course is designed for IT professionals seeking strategies to store and process large datasets within a distributed system environment.

Goal:

To develop in-depth expertise in administering Hadoop clusters.

Big data analytics entails the examination of vast and diverse datasets to identify correlations, uncover hidden patterns, and extract valuable insights.

The healthcare sector generates enormous volumes of complex, heterogeneous medical and clinical data. Leveraging big data analytics within this domain holds significant potential for generating insights that enhance healthcare delivery. Nevertheless, the sheer scale of these datasets presents substantial challenges for analysis and practical implementation in clinical settings.

In this instructor-led, live remote training, participants will learn how to conduct big data analytics in health through a series of hands-on live laboratory exercises.

By the end of this training, participants will be able to:

• Install and configure big data analytics tools such as Hadoop MapReduce and Spark
• Understand the key characteristics of medical data
• Apply big data techniques to manage medical data
• Study big data systems and algorithms within the context of health applications

Audience

• Developers
• Data Scientists

Format of the Course

• A blend of lectures, discussions, exercises, and intensive hands-on practice.

Note

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

When conventional storage technologies struggle to manage the volume of data you need to accommodate, there are hundreds of alternatives available. This course aims to guide participants in identifying suitable options for storing and analysing Big Data, alongside an examination of their respective advantages and disadvantages.

The course primarily focuses on discussing and presenting various solutions, although hands-on exercises can be provided upon request.

This instructor-led, live training in Portugal (online or onsite) is aimed at beginner-level and intermediate-level professionals who wish to use big data and database concepts to understand modern data platforms and make better technology and business decisions.

By the end of this training, participants will be able to: explain core database and big data concepts, compare major data platform options, describe how data is stored and processed, and identify practical use cases and governance needs.

This instructor-led, live training in Portugal (online or onsite) is aimed at database administrators or developers who wish to use Microsoft Azure Cosmos DB to develop and manage highly responsive and low latency applications.

By the end of this training, participants will be able to:

• Provision the necessary Cosmos DB resources to start building databases and applications.
• Scale application performance and storage by utilizing APIs in Cosmos DB.
• Manage database operations and reduce cost by optimizing Cosmos DB resources.

Apache Hadoop stands as the leading framework for processing Big Data across server clusters. Over the course of three days (with a fourth day available), participants will explore the business advantages and practical use cases of Hadoop and its ecosystem. The curriculum covers planning cluster deployment and scalability, as well as installing, maintaining, monitoring, troubleshooting, and optimising Hadoop. Attendees will also gain hands-on experience with bulk data loading on clusters, become familiar with various Hadoop distributions, and practice installing and managing tools within the Hadoop ecosystem. The course concludes with a discussion on securing clusters using Kerberos.

“The materials were very well prepared and covered thoroughly. The Lab was very helpful and well organized”
— Andrew Nguyen, Principal Integration DW Engineer, Microsoft Online Advertising

Audience

Hadoop administrators

Format

Lectures and hands-on labs, with an approximate balance of 60% lectures and 40% labs.

Apache Hadoop is the leading framework for processing Big Data across clusters of servers. This course introduces developers to the key components of the Hadoop ecosystem, including HDFS, MapReduce, Pig, Hive, and HBase.

Apache Hadoop stands as one of the most widely adopted frameworks for processing Big Data across server clusters. This course explores data management within HDFS, alongside advanced techniques in Pig, Hive, and HBase. These sophisticated programming methods are particularly advantageous for experienced Hadoop developers.

Audience: developers

Duration: three days

Format: lectures (50%) and hands-on labs (50%).

Target Audience:

This course aims to demystify big data and Hadoop technologies, demonstrating that they are accessible and straightforward to comprehend.

This instructor-led live training in Portugal (online or onsite) is aimed at system administrators who wish to learn how to set up, deploy and manage Hadoop clusters within their organisation.

By the end of this training, participants will be able to:

• Install and configure Apache Hadoop.
• Understand the four major components in the Hadoop ecoystem: HDFS, MapReduce, YARN, and Hadoop Common.
• Use Hadoop Distributed File System (HDFS) to scale a cluster to hundreds or thousands of nodes.  
• Set up HDFS to operate as storage engine for on-premise Spark deployments.
• Set up Spark to access alternative storage solutions such as Amazon S3 and NoSQL database systems such as Redis, Elasticsearch, Couchbase, Aerospike, etc.
• Carry out administrative tasks such as provisioning, management, monitoring and securing an Apache Hadoop cluster.

Apache NiFi is an open-source, flow-based data integration and event-processing platform. It enables automated, real-time data routing, transformation, and system mediation between disparate systems, with a web-based UI and fine-grained control.

This instructor-led, live training (onsite or remote) is aimed at intermediate-level administrators and engineers who wish to deploy, manage, secure, and optimize NiFi dataflows in production environments.

By the end of this training, participants will be able to:

• Install, configure, and maintain Apache NiFi clusters.
• Design and manage dataflows from varied sources and sinks.
• Implement flow automation, routing, and transformation logic.
• Optimize performance, monitor operations, and troubleshoot issues.

Format of the Course

• Interactive lecture with real-world architecture discussion.
• Hands-on labs: building, deploying, and managing flows.
• Scenario-based exercises in a live-lab environment.

Course Customization Options

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

In this instructor-led, live training in Portugal, participants will learn the fundamentals of flow-based programming as they develop a number of demo extensions, components and processors using Apache NiFi.

By the end of this training, participants will be able to:

• Understand NiFi's architecture and dataflow concepts.
• Develop extensions using NiFi and third-party APIs.
• Custom develop their own Apache Nifi processor.
• Ingest and process real-time data from disparate and uncommon file formats and data sources.

Relational databases have long been the preferred technology for storing, retrieving, and querying data. They enable users to organize information using a structured, well-defined model. While this approach is effective for standardized data with pre-defined requirements (such as a hospital check-in system maintaining patient records with consistent fields like ID, name, and visit date), it presents limitations for organizations dealing with less structured data. For instance, a startup experimenting with various fields in an online inquiry form must frequently redefine its schema to accommodate evolving business needs. This constant need to recreate data structures and allowed data types before saving new information can be cumbersome.

NoSQL (Not Only SQL) databases address these challenges by freeing users from the need to predefine data structures, allowing for immediate insertion and updates. NoSQL systems are typically faster than relational databases and handle vast volumes of data effortlessly. They also scale more effectively by efficiently partitioning data across server clusters and load balancing access. NoSQL databases integrate seamlessly with applications requiring real-time analytics, site personalization, IoT support, and mobile development.

In this instructor-led live training, participants will explore the architecture, design principles, and functionality of leading NoSQL databases. Through a live lab environment, attendees will set up, operate, and assess various NoSQL solutions. The objective is to equip participants with the skills to intelligently evaluate, propose, and implement suitable NoSQL database solutions within their organizations.

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

• Install and configure diverse NoSQL databases, including MongoDB, Cassandra, Redis, and Neo4j.
• Understand the advantages and disadvantages of NoSQL databases compared to relational databases.
• Grasp the underlying data formats used by NoSQL databases and how to leverage them for modern application development (desktop, mobile, cloud, IoT).
• Perform CRUD operations (create, read, update, delete) within a NoSQL environment.
• Set up a hybrid environment where relational and NoSQL databases work in tandem.
• Configure a NoSQL database cluster to distribute the processing of large datasets.
• Understand the security implications associated with using NoSQL databases.
• Deploy and scale a NoSQL database in a production environment.

Audience

• Database professionals
• Data architects
• Data strategists
• Project managers
• Application developers seeking to integrate flexible database solutions

Format of the Course

• A blend of lectures, discussions, exercises, and extensive hands-on practice.

Note

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

redis

nosql

Scylla is an open-source, distributed NoSQL data store designed to be compatible with Apache Cassandra, while delivering significantly higher throughput and lower latency.

In this course, participants will explore Scylla’s features and architecture, gaining practical experience in setting up, administering, monitoring, and troubleshooting Scylla.

Audience

• Database administrators
• Developers
• System Engineers

Course Format

• The course is interactive, covering the principles and approaches for deploying and managing Scylla distributed databases and clusters.
• A significant portion of the course is dedicated to hands-on exercises and practical application.