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Cambridge, MA – Regus at Harvard Square

One Mifflin Place, Suite 400, Cambridge, United States, 02138

The venue is ideally situated in the heart of Harvard Square at One Mifflin Place, easily accessed by car via Route 2, MA‑2A, or Memorial Drive, with pay-to-park garages and metered street spaces nearby. From Boston Logan International Airport (BOS), taxis or rideshares take approximately 25–30 minutes via I‑90 West and Cambridge Street. Alternatively, public transit offers seamless connections: take the MBTA Red Line to Harvard station, and the entrance is a 3-minute walk from the square, providing effortless access for attendees without cars.

Subcategories (1)

PySpark

PySpark

Explore Our Courses

Big Data Analytics with Google Colab and Apache Spark

 14 Hours

This instructor-led, live training in Cambridge (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 Cambridge (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 Cambridge (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 Cambridge, 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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Hadoop and Spark for Administrators

 35 Hours

This instructor-led, live training in Cambridge (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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A Practical Introduction to Stream Processing

 21 Hours

In this instructor-led, live training in Cambridge (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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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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SMACK Stack for Data Science

 14 Hours

This instructor-led, live training in Cambridge (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 Cambridge (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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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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Scaling Data Pipelines with Spark NLP

 14 Hours

This instructor-led, live training in Cambridge (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 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.
Read more...

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Testimonials (6)

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

Doing similar exercises different ways really help understanding what each component (Hadoop/Spark, standalone/cluster) can do on its own and together. It gave me ideas on how I should test my application on my local machine when I develop vs when it is deployed on a cluster.

Thomas Carcaud - IT Frankfurt GmbH

Course - Spark for Developers

Upcoming Courses

Spark for Developers

2026-06-24 9:30 am
21 hours
Cambridge, MA – Regus at Harvard Square
$ 4054 (Online)
$ 5854 (Classroom)

Python and Spark for Big Data (PySpark)

2026-07-08 9:30 am
21 hours
Cambridge, MA – Regus at Harvard Square
$ 3330 (Online)
$ 5130 (Classroom)

Administration of Apache Spark

2026-07-22 9:30 am
35 hours
Cambridge, MA – Regus at Harvard Square
$ 6757 (Online)
$ 9757 (Classroom)

Scaling Data Pipelines with Spark NLP

2026-08-05 9:30 am
14 hours
Cambridge, MA – Regus at Harvard Square
$ 2703 (Online)
$ 3903 (Classroom)

Scaling Data Pipelines with Spark NLP

2026-08-19 9:30 am
14 hours
Cambridge, MA – Regus at Harvard Square
$ 2703 (Online)
$ 3903 (Classroom)

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