HBase for Developers Training Course

Audience:

The course is intended for IT specialists looking for a solution to store and process large data sets in a distributed system environment

Goal:

Deep knowledge on Hadoop cluster administration.

Big data analytics involves the process of examining large amounts of varied data sets in order to uncover correlations, hidden patterns, and other useful insights.

The health industry has massive amounts of complex heterogeneous medical and clinical data. Applying big data analytics on health data presents huge potential in deriving insights for improving delivery of healthcare. However, the enormity of these datasets poses great challenges in analyses and practical applications to a clinical environment.

In this instructor-led, live training (remote), participants will learn how to perform big data analytics in health as they step through a series of hands-on live-lab 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 characteristics of medical data
• Apply big data techniques to deal with medical data
• Study big data systems and algorithms in the context of health applications

Audience

• Developers
• Data Scientists

Format of the Course also allows for the evaluation of participants.

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

Note

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

When traditional storage technologies don't handle the amount of data you need to store there are hundereds of alternatives. This course try to guide the participants what are alternatives for storing and analyzing Big Data and what are theirs pros and cons.

This course is mostly focused on discussion and presentation of solutions, though hands-on exercises are available on demand.

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

This instructor-led, live training in France (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 is the most popular framework for processing Big Data on clusters of servers. In this three (optionally, four) days course, attendees will learn about the business benefits and use cases for Hadoop and its ecosystem, how to plan cluster deployment and growth, how to install, maintain, monitor, troubleshoot and optimize Hadoop. They will also practice cluster bulk data load, get familiar with various Hadoop distributions, and practice installing and managing Hadoop ecosystem tools. The course finishes off with discussion of securing cluster with 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, approximate balance 60% lectures, 40% labs.

Apache Hadoop is the most popular framework for processing Big Data on clusters of servers. This course will introduce a developer to various components (HDFS, MapReduce, Pig, Hive and HBase) Hadoop ecosystem.

Apache Hadoop is one of the most popular frameworks for processing Big Data on clusters of servers. This course delves into data management in HDFS, advanced Pig, Hive, and HBase. These advanced programming techniques will be beneficial to experienced Hadoop developers.

Audience: developers

Duration: three days

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

Audience:

This course is intended to demystify big data/hadoop technology and to show it is not difficult to understand.

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

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 also allows for the evaluation of participants.

• 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 France, 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 been the technology of choice for storing, retrieving and querying data. Relational databases allow users to organize their data using a structured, well-defined set of patterns (model). While this approach works well for storing data that is standardized and well-understood in advance (think of a hospital check-in application that holds patient records with the same consistent set of pre-defined fields...patient id, first name, last name, date of last visit, etc.), there are limitations to this model. For organizations whose incoming data is not well-defined (think of an online inquiry form for a startup whose still in the process of experimenting with different fields for collecting visitor data, removing and adding fields as they go to accommodate for the changing nature of the business), any established definitions for how the data should fit into an existing database would need to be re-defined regularly. This would require recreating the data model (schema) that dictates the structure of the data and its allowed data types to support different types of data inputs, etc., before any new data could be saved to the database.

Enter NoSQL (Not Only SQL) databases. NoSQL databases free users from having to predefine the structure of the 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. NoSQL databases also scale better than relational databases, due to their ability to efficiently partition data across many servers (cluster) and load balance the 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 setup, operate and asses 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 vs 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
• Setup a mixed environment with both a relational database and NoSQL working in tandem
• Configure a cluster of NoSQL database 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 also allows for the evaluation of participants.

• 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.