Digital 01 Building

No. 12, Guanghua Road, Chaoyang District, Beijing, China, 100000
Between the China International Trade Center and the Kerry Centre, it is backed by the International Trade Center and is acro...

Subcategories (3)

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GraphX
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Apache Spark MLlib
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PySpark

Explore Our Courses

Python, Spark, and Hadoop for Big Data

 21 Hours
This instructor-led, live training in Beijing Chaoyang (online or onsite) is aimed at developers who wish to use and integrate Spark, Hadoop, and Python to process, analyze, and transform large and complex data sets. By the end of this training, participants will be able to:
  • Set up the necessary environment to start processing big data with Spark, Hadoop, and Python.
  • Understand the features, core components, and architecture of Spark and Hadoop.
  • Learn how to integrate Spark, Hadoop, and Python for big data processing.
  • Explore the tools in the Spark ecosystem (Spark MlLib, Spark Streaming, Kafka, Sqoop, Kafka, and Flume).
  • Build collaborative filtering recommendation systems similar to Netflix, YouTube, Amazon, Spotify, and Google.
  • Use Apache Mahout to scale machine learning algorithms.
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Administration of Apache Spark

 35 Hours
This instructor-led, live training in Beijing Chaoyang (online or onsite) is aimed at beginner-level to intermediate-level system administrators who wish to deploy, maintain, and optimize Spark clusters.By the end of this training, participants will be able to:
  • Install and configure Apache Spark in various environments.
  • Manage cluster resources and monitor Spark applications.
  • Optimize the performance of Spark clusters.
  • Implement security measures and ensure high availability.
  • Debug and troubleshoot common Spark issues.
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Python and Spark for Big Data (PySpark)

 21 Hours
In this instructor-led, live training in Beijing Chaoyang, 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.
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Spark for Developers

 21 Hours
OBJECTIVE: This course will introduce Apache Spark. The students will learn how  Spark fits  into the Big Data ecosystem, and how to use Spark for data analysis.  The course covers Spark shell for interactive data analysis, Spark internals, Spark APIs, Spark SQL, Spark streaming, and machine learning and graphX. AUDIENCE : Developers / Data Analysts
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Hortonworks Data Platform (HDP) for Administrators

 21 Hours
This instructor-led, live training in Beijing Chaoyang (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.
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Magellan: Geospatial Analytics on Spark

 14 Hours
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.
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Apache Spark SQL

 7 Hours
Spark SQL is Apache Spark's module for working with structured and unstructured data. Spark SQL provides information about the structure of the data as well as the computation being performed. This information can be used to perform optimizations. Two common uses for Spark SQL are:
- to execute SQL queries.
- to read data from an existing Hive installation. In this instructor-led, live training (onsite or remote), participants will learn how to analyze various types of data sets using Spark SQL. By the end of this training, participants will be able to:
  • Install and configure Spark SQL.
  • Perform data analysis using Spark SQL.
  • Query data sets in different formats.
  • Visualize data and query results.
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.
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A Practical Introduction to Stream Processing

 21 Hours
In this instructor-led, live training in Beijing Chaoyang (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.
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Big Data Analytics in Health

 21 Hours
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.
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Apache Spark in the Cloud

 21 Hours
Apache Spark's learning curve is slowly increasing at the begining, it needs a lot of effort to get the first return. This course aims to jump through the first tough part. After taking this course the participants will understand the basics of Apache Spark , they will clearly differentiate RDD from DataFrame, they will learn Python and Scala API, they will understand executors and tasks, etc.  Also following the best practices, this course strongly focuses on cloud deployment, Databricks and AWS. The students will also understand the differences between AWS EMR and AWS Glue, one of the lastest Spark service of AWS.     AUDIENCE: Data Engineer, DevOps, Data Scientist
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Apache Spark Streaming with Scala

 21 Hours
This instructor-led, live training in Beijing Chaoyang (online or onsite) is aimed at software engineers who wish to stream big data with Spark Streaming and Scala. By the end of this training, participants will be able to:
  • Create Spark applications with the Scala programming language.
  • Use Spark Streaming to process continuous streams of data.
  • Process streams of real-time data with Spark Streaming.
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SMACK Stack for Data Science

 14 Hours
This instructor-led, live training in Beijing Chaoyang (online or onsite) is aimed at data scientists who wish to use the SMACK stack to build data processing platforms for big data solutions. By the end of this training, participants will be able to:
  • Implement a data pipeline architecture for processing big data.
  • Develop a cluster infrastructure with Apache Mesos and Docker.
  • Analyze data with Spark and Scala.
  • Manage unstructured data with Apache Cassandra.
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Apache Spark Fundamentals

 21 Hours
This instructor-led, live training in Beijing Chaoyang (online or onsite) is aimed at engineers who wish to set up and deploy Apache Spark system for processing very large amounts of data. By the end of this training, participants will be able to:
  • Install and configure Apache Spark.
  • Quickly process and analyze very large data sets.
  • Understand the difference between Apache Spark and Hadoop MapReduce and when to use which.
  • Integrate Apache Spark with other machine learning tools.
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Apache Spark for .NET Developers

 21 Hours
This instructor-led, live training in Beijing Chaoyang (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.
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Hadoop and Spark for Administrators

 35 Hours
This instructor-led, live training in Beijing Chaoyang (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.
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Scaling Data Pipelines with Spark NLP

 14 Hours
This instructor-led, live training in Beijing Chaoyang (online or onsite) is aimed at data scientists and developers who wish to use Spark NLP, built on top of Apache Spark, to develop, implement, and scale natural language text processing models and pipelines. By the end of this training, participants will be able to:
  • Set up the necessary development environment to start building NLP pipelines with Spark NLP.
  • Understand the features, architecture, and benefits of using Spark NLP.
  • Use the pre-trained models available in Spark NLP to implement text processing.
  • Learn how to build, train, and scale Spark NLP models for production-grade projects.
  • Apply classification, inference, and sentiment analysis on real-world use cases (clinical data, customer behavior insights, etc.).
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Apache Spark MLlib

 35 Hours
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
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Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP

 21 Hours
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.
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Introduction to Graph Computing

 28 Hours
In this instructor-led, live training in Beijing Chaoyang, 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.
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Testimonials (10)

A lot of practical examples, different ways to approach the same problem, and sometimes not so obvious tricks how to improve the current solution

Rafał - Nordea
Course - Apache Spark MLlib

Exercises and exchanges during questions/answers

Antoine - Physiobotic
Course - Scaling Data Pipelines with Spark NLP

Machine Translated

Commitment and willingness to explain secondary topics.

Marek - Krajowy Rejestr Długów Biuro Informacji Gospodarczej S.A.
Course - Apache Spark Fundamentals

Machine Translated

I liked that it was practical. Loved to apply the theoretical knowledge with practical examples.

Aurelia-Adriana - Allianz Services Romania
Course - Python and Spark for Big Data (PySpark)

The fact that we were able to take with us most of the information/course/presentation/exercises done, so that we can look over them and perhaps redo what we didint understand first time or improve what we already did.

Raul Mihail Rat - Accenture Industrial SS
Course - Python, Spark, and Hadoop for Big Data

very interactive...

Richard Langford
Course - SMACK Stack for Data Science

Sufficient hands on, trainer is knowledgable

Chris Tan
Course - A Practical Introduction to Stream Processing

Having hands on session / assignments

Poornima Chenthamarakshan - Intelligent Medical Objects
Course - Apache Spark in the Cloud

The VM I liked very much The Teacher was very knowledgeable regarding the topic as well as other topics, he was very nice and friendly I liked the facility in Dubai.

Safar Alqahtani - Elm Information Security
Course - Big Data Analytics in Health

This is one of the best hands-on with exercises programming courses I have ever taken.

Laura Kahn
Course - Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP

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