Online or onsite, instructor-led live Apache Spark training courses demonstrate through hands-on practice how Spark fits into the Big Data ecosystem, and how to use Spark for data analysis.
Apache Spark training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Michigan onsite live Apache Spark trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Detroit, MI - Renaissance Center
400 Renaissance Center, Detroit, United States, 48243
The GM Renaissance Center is conveniently located in downtown Detroit and easily accessed by car via Interstates 75 or 94, with secure underground parking available on site. Travelers flying into Detroit Metropolitan Airport (DTW) can expect a 25–30 minute trip by taxi or rideshare via I‑94. Public transit is efficient: the Detroit People Mover stops directly at the Renaissance Center station, and DDOT routes 3 and 9 serve nearby Jefferson Avenue. Pedestrian skywalks provide safe indoor access from downtown hotels, parking garages, and the riverwalk.
Ann Arbor, MI – Regus - South State Commons I
2723 S State St, Ann Arbor, United States, 48104
Regus South State Commons I is conveniently located off I‑94 via Exit 177 (State Street), with easy access to downtown Ann Arbor and surrounding suburbs. The building offers free on-site surface parking for guests. From Detroit Metropolitan Airport (DTW), the venue can be reached in approximately 20–25 minutes by taxi or rideshare via I‑94 West. Local public transit service (TheRide) operates Route 24 along South State Street, with a stop within a short 2-minute walk of the building.
Grand Rapids, MI - Regus – Calder Plaza
250 Monroe Ave NW, Grand Rapids, United States, 49503
The venue sits centrally at 250 Monroe Avenue NW in downtown Grand Rapids, easily accessed by car via US‑131 or I‑196—with connections via Monroe or Ottawa exits—and offers shared underground and surface parking. From Gerald R. Ford International Airport, take I‑96 East then I‑196 West into the city; the drive is about 20 minutes. Public transit through Rapid bus routes stops near Monroe or Ottawa Avenue, just a short walk from the Regus entrance; the downtown area is pedestrian-friendly.
Lansing, MI - Regus - One Michigan Avenue
120 North Washington Square, Lansing, United States, 48933
The venue is located in the heart of Lansing’s central business district at 120 North Washington Square, easily accessible by car via I‑496 or US‑127 with convenient street parking and a nearby parking ramp. From Capital Region International Airport (LAN), the location is approximately a 12‑minute drive west via I‑96 and US‑127, with taxis and rideshares readily available. Public transit users can take CATA bus routes that stop just a block away on Washington or Grand Avenue, offering seamless access to the venue.
This instructor-led, live training in Michigan (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.
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.
This instructor-led, live training in Michigan (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.
This instructor-led, live training in Michigan (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.
In this instructor-led, live training in Michigan, 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.
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 Michigan (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.
In this instructor-led, live training in Michigan (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.
This instructor-led, live training in Michigan (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.
This instructor-led, live training in Michigan (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.
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.
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.
This instructor-led, live training in Michigan (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.).
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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Testimonials (7)
The live examples
Ahmet Bolat - 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
Get to learn spark streaming , databricks and aws redshift
Lim Meng Tee - Jobstreet.com Shared Services Sdn. Bhd.
Course - Apache Spark in the Cloud
practice tasks
Pawel Kozikowski - GE Medical Systems Polska Sp. Zoo
Course - Python and Spark for Big Data (PySpark)
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
Richard is very calm and methodical, with an analytic insight - exactly the qualities needed to present this sort of course.
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