Apache Spark Streaming with Scala Training Course

In this instructor-led, live training in Argentina, participants will learn how to use Python and Spark together to analyze big data as they work on hands-on exercises.

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

• Learn how to use Spark with Python to analyze Big Data.
• Work on exercises that mimic real world cases.
• Use different tools and techniques for big data analysis using PySpark.

In this instructor-led, live training in Argentina, participants will learn about the technology offerings and implementation approaches for processing graph data. The aim is to identify real-world objects, their characteristics and relationships, then model these relationships and process them as data using a Graph Computing (also known as Graph Analytics) approach. We start with a broad overview and narrow in on specific tools as we step through a series of case studies, hands-on exercises and live deployments.

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

• Understand how graph data is persisted and traversed.
• Select the best framework for a given task (from graph databases to batch processing frameworks.)
• Implement Hadoop, Spark, GraphX and Pregel to carry out graph computing across many machines in parallel.
• View real-world big data problems in terms of graphs, processes and traversals.

This course is aimed at developers and data scientists who wish to understand and implement AI within their applications. Special focus is given to Data Analysis, Distributed AI and NLP.

MLlib is Spark’s machine learning (ML) library. Its goal is to make practical machine learning scalable and easy. It consists of common learning algorithms and utilities, including classification, regression, clustering, collaborative filtering, dimensionality reduction, as well as lower-level optimization primitives and higher-level pipeline APIs.

It divides into two packages:

• spark.mllib contains the original API built on top of RDDs.

• spark.ml provides higher-level API built on top of DataFrames for constructing ML pipelines.

Audience

This course is directed at engineers and developers seeking to utilize a built in Machine Library for Apache Spark

This training outline is intended to bring attendees from a beginner to an intermediate/advanced level in the understanding and knowledge of the Akka framework.

The entire course is hands on, mostly driven by the trainer in the beginning and progressively shifting towards the attendees producing code themselves (and downloading implementations/solutions written by the trainer).

Attendees are not expected to have prior knowledge of Scala, the trainer will take the necessary time to explain and clarify all the intricacies that relate to the Scala code used.

The aim of this course is to provide a basic proficiency in applying Machine Learning methods in practice. Through the use of the Scala programming language and its various libraries, and based on a multitude of practical examples this course teaches how to use the most important building blocks of Machine Learning, how to make data modeling decisions, interpret the outputs of the algorithms and validate the results.

Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications.

Scala is a concise, object-oriented language with functional programming features, including currying, type inference, immutability, lazy evaluation, and pattern matching. Scala code runs on a JVM and was designed to address some of the shortcomings of Java.

In this instructor-led, live training participants will learn how to use Scala's advanced features, including its type system, to write better code using an object-functional approach.  

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

• Leverage Scala's type system to maximum benefit
• Handle exceptions functionally
• Create libraries and DSLs with Scala
• Use Scala's advanced features for pattern matching

Format of the course

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

Scala is a concise, object-oriented language with functional programming features, including currying, type inference, immutability, lazy evaluation, and pattern matching.

In this instructor-led, live training participants will learn how to use Scala's advanced features to develop well-constructed software that is easy to write, read, test, and change. The focus of this training is on functional programming, using real-world case studies and discussion as a reference for lab exercises and activities.

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

• Implement advanced functional programming techniques
• Make sense of complex type signatures
• Understand code through types and substitution
• Handle exceptions functionally
• Create libraries and DSLs with Scala
• Use Scala's advanced features for pattern matching

Format of the course

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

The training aims to provide opportunities Scala language, learning the syntax of programming paradigms, and space applications.

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.

This instructor-led, live training in Argentina (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.

This instructor-led, live training in Argentina (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.

In this instructor-led, live training in Argentina (onsite or remote), participants will learn how to set up and integrate different Stream Processing frameworks with existing big data storage systems and related software applications and microservices.

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

• Install and configure different Stream Processing frameworks, such as Spark Streaming and Kafka Streaming.
• Understand and select the most appropriate framework for the job.
• Process of data continuously, concurrently, and in a record-by-record fashion.
• Integrate Stream Processing solutions with existing databases, data warehouses, data lakes, etc.
• Integrate the most appropriate stream processing library with enterprise applications and microservices.

Magellan is an open-source distributed execution engine for geospatial analytics on big data. Implemented on top of Apache Spark, it extends Spark SQL and provides a relational abstraction for geospatial analytics.

This instructor-led, live training introduces the concepts and approaches for implementing geospacial analytics and walks participants through the creation of a predictive analysis application using Magellan on Spark.

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

• Efficiently query, parse and join geospatial datasets at scale
• Implement geospatial data in business intelligence and predictive analytics applications
• Use spatial context to extend the capabilities of mobile devices, sensors, logs, and wearables

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

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

This instructor-led, live training in Argentina (online or onsite) is aimed at developers who wish to carry out big data analysis using Apache Spark in their .NET applications.

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

• Install and configure Apache Spark.
• Understand how .NET implements Spark APIs so that they can be accessed from a .NET application.
• Develop data processing applications using C# or F#, capable of handling data sets whose size is measured in terabytes and pedabytes.
• Develop machine learning features for a .NET application using Apache Spark capabilities.
• Carry out exploratory analysis using SQL queries on big data sets.