Administration of Apache Spark Training Course

This instructor-led live training in Turkey (online or onsite) is tailored for intermediate-level data scientists and engineers who want to utilize 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.

This instructor-led live training (online or onsite) targets data engineers, analysts, and professionals seeking to leverage Databricks and PySpark for constructing scalable data pipelines and migrating legacy SQL workflows.

This course offers a hands-on introduction to creating scalable data processing and Machine Learning workflows with PySpark. Attendees will explore how Apache Spark functions within contemporary Big Data ecosystems and learn to efficiently process extensive datasets using distributed computing principles.

This intensive three-day workshop is dedicated to constructing and refining high-performance data-processing pipelines utilizing PySpark, Pandas, and Polars within Kubernetes environments.

Attendees will gain a hands-on grasp of Spark application execution on Kubernetes and how specific configuration choices directly impact performance, scalability, resource usage, and overall costs. The curriculum addresses critical optimization domains, such as executor sizing, memory management, dynamic allocation, partitioning logic, shuffle mechanics, small-file mitigation, and streamlined Parquet handling.

Additionally, the course tackles typical pitfalls associated with Pandas, such as memory constraints and OOM errors, while presenting Polars as a high-performance alternative for specific processing tasks. Through practical labs, participants will learn to diagnose performance and memory bottlenecks, evaluate distinct configuration strategies, and implement optimization techniques in realistic ETL and machine learning contexts.

The core focus of the training is practical decision-making: mastering the identification of bottlenecks, selecting the right tools, configuring Spark effectively, and striking a balance between performance and infrastructure costs.

This instructor-led live training in Turkey (online or onsite) is aimed at engineers who wish to set up and deploy an 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.

This practical training in Turkey simplifies Apache Spark, covering key components such as RDDs, DataFrames, and Python/Scala APIs. Learners will gain expertise in cloud deployment using Databricks, AWS EMR, and Glue, developing the practical skills needed for real-world data engineering and DevOps tasks.

In this instructor-led live training hosted in Turkey, participants will master the integration of Python and Spark for big data analysis, reinforced by hands-on exercises.

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

• Utilize Spark and Python together for Big Data analysis.
• Complete exercises that mirror real-world applications.
• Apply diverse tools and techniques for big data analysis using PySpark.

This instructor-led, live training in Turkey (online or onsite) is designed for developers who wish to use and integrate Spark, Hadoop, and Python to process, analyze, and transform large and complex datasets.

Upon completion of this training, participants will be able to:

• Configure the necessary environment to begin processing big data using Spark, Hadoop, and Python.
• Gain a comprehensive understanding of the features, core components, and architecture of Spark and Hadoop.
• Master the integration of Spark, Hadoop, and Python for efficient big data processing.
• Explore key tools within the Spark ecosystem, including Spark MLlib, Spark Streaming, Kafka, Sqoop, and Flume.
• Develop collaborative filtering recommendation systems akin to those used by Netflix, YouTube, Amazon, Spotify, and Google.
• Utilize Apache Mahout to scale machine learning algorithms.

This instructor-led live training in Turkey delves into the Stratio platform, with a specific focus on the Rocket and Intelligence modules integrated with PySpark. Participants will gain proficiency in data ingestion, transformation, and advanced analytics, while developing practical expertise in loops, UDFs, and enterprise-grade data workflows.