Spark for Developers Training Course

This instructor-led, live training in Bulgaria (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.

Big data analytics refers to the process of analyzing large volumes of diverse data sets to identify correlations, hidden patterns, and other valuable insights.

The healthcare sector generates vast amounts of complex, heterogeneous medical and clinical data. Leveraging big data analytics on this information offers significant potential for deriving insights that can enhance healthcare delivery. However, the sheer scale of these datasets presents substantial challenges for analysis and practical implementation in clinical environments.

Through this instructor-led live training (conducted remotely), participants will learn how to perform big data analytics in healthcare by completing a series of hands-on 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 manage and analyze medical data
• Study big data systems and algorithms within the context of healthcare applications

Audience

• Developers
• Data Scientists

Format of the Course

• A combination of lectures, discussions, exercises, and extensive hands-on practice.

Note

• To request customized training for this course, please contact us to arrange.

This instructor-led live training in Bulgaria (online or onsite) is designed for system administrators who wish to learn how to set up, deploy, and manage Hadoop clusters within their organizations.

Upon completing this training, participants will be capable of:

• Installing and configuring Apache Hadoop.
• Gaining a comprehensive understanding of the four core components of the Hadoop ecosystem: HDFS, MapReduce, YARN, and Hadoop Common.
• Leveraging the Hadoop Distributed File System (HDFS) to scale a cluster to hundreds or thousands of nodes.
• Configuring HDFS to serve as the storage engine for on-premise Spark deployments.
• Configuring Spark to access alternative storage solutions, including Amazon S3 and NoSQL databases such as Redis, Elasticsearch, Couchbase, and Aerospike.
• Performing essential administrative tasks such as provisioning, management, monitoring, and securing an Apache Hadoop cluster.

During this instructor-led live training in Bulgaria (onsite or remote), participants will learn how to set up and integrate various Stream Processing frameworks with existing big data storage systems, related software applications, and microservices.

Upon completing 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 suitable framework for specific tasks.
• Process data continuously, concurrently, and on a record-by-record basis.
• Integrate Stream Processing solutions with existing databases, data warehouses, data lakes, and other systems.
• Connect the most appropriate stream processing library with enterprise applications and microservices.

This course offers a hands-on introduction to developing scalable data processing and Machine Learning workflows with PySpark. Attendees will gain insight into how Apache Spark functions within contemporary Big Data ecosystems and how to effectively manage large datasets by leveraging distributed computing principles.

This instructor-led, live training in Bulgaria (online or onsite) is designed for 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.

This instructor-led live training in Bulgaria (online or onsite) is designed for engineers aiming to set up and deploy an Apache Spark system for processing massive data volumes.

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

• Install and configure Apache Spark.
• Efficiently process and analyze extensive data sets.
• Distinguish between Apache Spark and Hadoop MapReduce and understand their respective use cases.
• Integrate Apache Spark with external machine learning tools.

This instructor-led live training in Bulgaria (online or onsite) is designed for beginner to intermediate-level system administrators who want to deploy, maintain, and optimize Spark clusters.

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

• Install and configure Apache Spark in various environments.
• Manage cluster resources and monitor Spark applications.
• Enhance the performance of Spark clusters.
• Implement security measures and ensure high availability.
• Debug and resolve common Spark issues.

The initial learning curve for Apache Spark can be steep, requiring significant effort to achieve early results. This course is designed to help learners overcome that challenging first phase. Upon completion, participants will grasp the fundamentals of Apache Spark, clearly distinguish between RDDs and DataFrames, become proficient in both Python and Scala APIs, and gain a solid understanding of executors, tasks, and related concepts. Aligning with industry best practices, the curriculum places a strong emphasis on cloud deployment strategies, specifically within Databricks and AWS environments. Students will also explore the distinctions between AWS EMR and AWS Glue, one of AWS's most recent Spark-based services.

AUDIENCE:

Data Engineers, DevOps Professionals, Data Scientists

This instructor-led, live training in Bulgaria (online or onsite) is designed for 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:

• Configure the necessary development environment to begin building NLP pipelines with Spark NLP.
• Understand the features, architecture, and advantages of using Spark NLP.
• Leverage pre-trained models available in Spark NLP to implement text processing tasks.
• Learn how to build, train, and scale Spark NLP models for production-grade projects.
• Apply classification, inference, and sentiment analysis to real-world use cases (such as clinical data and customer behavior insights).

In this instructor-led, live training in Bulgaria, 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.

This instructor-led, live training in Bulgaria (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.

Spark SQL is the Apache Spark module designed for handling both structured and unstructured data. It provides insights into data structure and the computations being executed, which can be leveraged for performance optimizations. Spark SQL is commonly used to:
- Run SQL queries.
- Access data from an existing Hive deployment.

During this instructor-led live training (available onsite or remotely), participants will learn to analyze diverse datasets using Spark SQL.

Upon completion, participants will be able to:

• Install and set up Spark SQL.
• Conduct data analysis with Spark SQL.
• Query datasets in various formats.
• Visualize data and query outcomes.

Course Format

• Interactive lectures and discussions.
• Extensive exercises and practical work.
• Hands-on implementation in a live lab environment.

Customization Options

• To request a customized training for this course, please contact us to arrange.

Stratio is a comprehensive, data-driven platform that unifies big data capabilities, artificial intelligence, and governance into a single, cohesive solution. Its Rocket and Intelligence modules empower organizations to perform rapid data exploration, transformation, and sophisticated analytics within enterprise settings.

This instructor-led live training, available both online and onsite, is designed for intermediate-level data professionals looking to master the Rocket and Intelligence modules in Stratio using PySpark. The curriculum emphasizes looping structures, user-defined functions, and complex data logic.

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

• Efficiently navigate and utilize the Stratio platform, specifically the Rocket and Intelligence modules.
• Apply PySpark for data ingestion, transformation, and analytical processes.
• Implement loops and conditional logic to manage data workflows and execute feature engineering tasks.
• Develop and manage user-defined functions (UDFs) to create reusable data operations within PySpark.

Training Format

• Interactive lectures and discussions.
• Extensive exercises and practical application.
• Hands-on implementation in a live lab environment.

Customization Options

• For customized training arrangements for this course, please contact us directly.