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 Hadoop is an open, distributed and scalable platform created for storing, processing and analyzing large data sets. Hadoop allows large amounts of data to be stored on a cluster of computers, so petabytes of data can be handled. The Hadoop framework includes the Hadoop Distributed File System (HDFS) and the MapReduce distributed processing framework. This allows for parallel data processing on many machines in the cluster. The platform Hadoop is easy to scale. It can be expanded by adding new nodes to the cluster, which increases throughput and data processing. It consists of various modules such as HDFS, MapReduce, YARN (Yet Another Resource Negotiator) and additional tools and libraries that can be used depending on the needs of the project. Hadoop allows you to work with a variety of data types, including structured, semi-structured and unstructured. Jest used for big data analysis, reporting, trend analysis, prediction, and machine learning. Hadoop has a rich ecosystem of tools and frameworks such as Apache Hive, Apache Pig, Apache Spark, Apache HBase that support various aspects of data processing and analysis. It provides security, access control, and user management mechanisms to protect data stored and processed in the cluster Hadoop. Big Data Hadoop is a popular tool for processing and analyzing large data sets, used in various industries, including market analysis, medicine, finance, industry and many others, where it is necessary to manage and analyze large amounts of data.

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

• 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 Poland (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.

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

Course goal:

Getting knowledge regarding Hadoop cluster administration

This instructor-led, live training in Poland (online or onsite) introduces Hortonworks Data Platform (HDP) and walks participants through the deployment of Spark + Hadoop solution.

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

• Use Hortonworks to reliably run Hadoop at a large scale.
• Unify Hadoop's security, governance, and operations capabilities with Spark's agile analytic workflows.
• Use Hortonworks to investigate, validate, certify and support each of the components in a Spark project.
• Process different types of data, including structured, unstructured, in-motion, and at-rest.

This course covers how to use Hive SQL language (AKA: Hive HQL, SQL on Hive, HiveQL) for people who extract data from Hive

Cloudera Impala is an open source massively parallel processing (MPP) SQL query engine for Apache Hadoop clusters.

Impala enables users to issue low-latency SQL queries to data stored in Hadoop Distributed File System and Apache Hbase without requiring data movement or transformation.

Audience

This course is aimed at analysts and data scientists performing analysis on data stored in Hadoop via Business Intelligence or SQL tools.

After this course delegates will be able to

• Extract meaningful information from Hadoop clusters with Impala.
• Write specific programs to facilitate Business Intelligence in Impala SQL Dialect.
• Troubleshoot Impala.

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

• 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.