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Suzhou, Jinghope Plaza

11/F, 11/F, Tower 2, 88 Hua Chi Street, SIP, Suzhou, China, 215021

Situated right in the core of the Suzhou industry zone, The Regus Centre is on the 11th floor of Jinghope Plaza. Jinghope Plaza is a mixed use complex comprising a high-end shopping mall, 5 star hotel and international Grade A office building. It is one of the most prestigious landmarks in the financial center of Suzhou. Advanced facilities and central location attract many Fortune 500 companies such as Nokia, Philips and 3M. Jinghope Plaza has easy access to excellent facilities and amenities in Suzhou. Metro Line 1 Times Square station is just in the basement of the complex. The high-speed train station which provides easy transportation to Shanghai and Nanjing is only a 5 minutes drive from the centre.

Jinhe Plaza

11th Floor, No. 2, 88 Huachi Street, Industrial Park, Suzhou, China, 215021

The center of Jinhe Plaza is located at the core of Suzhou Industrial Zone, on the east bank of Jinji Lake. Jinhe Plaza is a comprehensive building, including high-end shopping malls, five-star hotels, international Grade A office buildings, and Regus is located on the eleventh floor of the building. As the most famous landmark in Suzhou Financial Center, with its advanced facilities and central location, it has attracted many Fortune 500 companies such as Nokia, Philips and 3M. Jinhe Plaza provides easy access to all major core areas of Suzhou. Metro Line 1 Times Square Station is located on the basement of the building, while the High Speed ​​Rail Station to Shanghai and Nanjing is a 5-minute drive away.

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PySpark

PySpark

Explore Our Courses

Big Data Analytics with Google Colab and Apache Spark

 14 Hours

This instructor-led, live training in SuZhou (online or onsite) is aimed at intermediate-level data scientists and engineers who wish to use Google Colab and Apache Spark for big data processing and analytics.

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

  • Set up a big data environment using Google Colab and Spark.
  • Process and analyze large datasets efficiently with Apache Spark.
  • Visualize big data in a collaborative environment.
  • Integrate Apache Spark with cloud-based tools.
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Stratio: Rocket and Intelligence Modules with PySpark

 14 Hours

Stratio is a data-centric platform that integrates big data, AI, and governance into a single solution. Its Rocket and Intelligence modules enable rapid data exploration, transformation, and advanced analytics in enterprise environments.

This instructor-led, live training (online or onsite) is aimed at intermediate-level data professionals who wish to use the Rocket and Intelligence modules in Stratio effectively with PySpark, focusing on looping structures, user-defined functions, and advanced data logic.

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

  • Navigate and work within the Stratio platform using Rocket and Intelligence modules.
  • Apply PySpark in the context of data ingestion, transformation, and analysis.
  • Use loops and conditional logic to control data workflows and feature engineering tasks.
  • Create and manage user-defined functions (UDFs) for reusable data operations in PySpark.

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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Python, Spark, and Hadoop for Big Data

 21 Hours

This instructor-led, live training in SuZhou (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 SuZhou (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 SuZhou, 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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PySpark and Machine Learning

 21 Hours

This training provides a practical introduction to building scalable data processing and Machine Learning workflows using PySpark. Participants learn how Apache Spark operates within modern Big Data ecosystems and how to efficiently process large datasets using distributed computing principles.

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Apache Spark Fundamentals

 21 Hours

This instructor-led, live training in SuZhou (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 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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Testimonials (3)

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

Having hands on session / assignments

Poornima Chenthamarakshan - Intelligent Medical Objects

Course - Apache Spark in the Cloud

Upcoming Courses

Apache Spark in the Cloud

2026-08-27 09:30
21 hours
Jinhe Plaza

PySpark and Machine Learning

2026-09-10 09:30
21 hours
Suzhou, Jinghope Plaza

Python, Spark, and Hadoop for Big Data

2026-09-24 09:30
21 hours
Jinhe Plaza

Administration of Apache Spark

2026-10-08 09:30
35 hours
Suzhou, Jinghope Plaza

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