HBase for Developers Training Course

Target Audience:

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

Learning Objectives:

Gain in-depth knowledge of Hadoop cluster administration.

Big data analytics refers to the process of inspecting vast amounts of diverse data sets to identify correlations, uncover hidden patterns, and generate valuable insights.

The healthcare sector generates enormous volumes of complex, heterogeneous medical and clinical data. Applying big data analytics to health data holds significant potential for deriving insights that enhance healthcare delivery. However, the sheer scale of these datasets presents substantial challenges for analysis and practical implementation within clinical environments.

Through this instructor-led, live remote training, participants will learn how to conduct big data analytics in the health sector by completing a series of hands-on live laboratory exercises.

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

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

Audience

• Developers
• Data Scientists

Course Format

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

Note

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

When conventional storage technologies struggle to accommodate the volume of data you need to manage, there are numerous alternatives available. This course aims to guide participants in exploring options for storing and analyzing Big Data, along with an analysis of their respective advantages and disadvantages.

The curriculum primarily emphasizes discussion and the presentation of solutions, although hands-on exercises are available upon request.

This instructor-led, live training in Norway (online or onsite) targets beginner and intermediate 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 Norway (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. During this three-day course (with an optional fourth day), participants will explore the business advantages and practical applications of Hadoop and its surrounding ecosystem. The curriculum covers cluster deployment planning, expansion strategies, and the installation, maintenance, monitoring, troubleshooting, and optimization of Hadoop. Attendees will gain hands-on experience with bulk data loading on clusters, become familiar with various Hadoop distributions, and practice installing and managing ecosystem tools. 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

The course combines lectures with hands-on labs, maintaining an approximate balance of 60% lectures and 40% labs.

Apache Hadoop stands as the leading framework for processing Big Data across server clusters. This course provides developers with an introduction 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 managing Big Data across server clusters. This course offers an in-depth exploration of data management within HDFS, alongside advanced applications of Pig, Hive, and HBase. These sophisticated programming techniques are designed to provide significant value to experienced Hadoop developers.

Audience: developers

Duration: three days

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

Target Audience:

This course aims to simplify big data and Hadoop technology, demonstrating that it is accessible and not overly complex to learn.

This instructor-led live training in Norway (online or onsite) is designed for system administrators who wish to learn how to set up, deploy, and manage Hadoop clusters within their organizations.

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 Norway, 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 set of patterns (models). While this approach is highly effective for standardized data that is understood in advance (for instance, a hospital check-in application managing patient records with consistent pre-defined fields such as patient ID, first name, last name, and date of the last visit), it has certain limitations. For organizations dealing with undefined incoming data (such as an online inquiry form for a startup experimenting with different fields to collect visitor data, constantly adding or removing fields to adapt to the evolving business needs), any established definitions for how data fits into an existing database would need frequent re-definition. This would require recreating the data model (schema) that dictates the structure and allowed data types to support various data inputs before any new data could be saved to the database.

Enter NoSQL (Not Only SQL) databases. NoSQL databases free users from the need to predefine the structure of incoming data, allowing them to insert and update new data on the fly. NoSQL databases are often faster than relational databases and can handle very large amounts of data with ease. They also scale better than relational databases due to their ability to efficiently partition data across many servers (clusters) and load balance access to this data. NoSQL databases integrate particularly well with applications that support real-time analytics, site personalization, IoT, and mobile apps.

In this instructor-led, live training, participants will understand the architecture, design principles, and functionality of the most popular NoSQL databases as they set up, operate, and assess a number of NoSQL databases in a live lab environment. The goal of this training is to enable participants to intelligently evaluate, propose, and implement a suitable NoSQL database solution within their organization.

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

• Install and configure different types of NoSQL databases, including MongoDB, Cassandra, Redis, and Neo4j.
• Understand the benefits and disadvantages of NoSQL databases compared to relational databases.
• Understand the underlying data formats used by NoSQL databases and how these formats can be used to an advantage when developing modern applications (desktop, mobile, cloud, IoT).
• Perform create, insert, update, delete operations in a NoSQL database.
• Set up a mixed environment with both a relational database and NoSQL working in tandem.
• Configure a cluster of NoSQL databases to distribute the processing of very large datasets.
• Understand the security implications of using a NoSQL database.
• Deploy and scale a NoSQL database in a production environment.

Audience

• Database professionals.
• Data architects.
• Data strategists.
• Project managers.
• Application developers wishing to integrate a flexible database solution in their application.

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.

redis

nosql

Scylla is an open-source distributed NoSQL data store. It is compatible with Apache Cassandra but performs at significantly higher throughputs and lower latencies.

In this course, participants will learn about Scylla's features and architecture while obtaining practical experience with setting up, administering, monitoring, and troubleshooting Scylla.  

Audience

• Database administrators
• Developers
• System Engineers

Format of the course

• The course is interactive and includes discussions of the principles and approaches for deploying and managing Scylla distributed databases and clusters.
• The course includes a heavy component of hands-on exercises and practice.