IBM Datastage For Administrators and Developers Training Course

This instructor-led live training in Serbia (available online or onsite) is targeted at intermediate-level data scientists and engineers interested in employing Google Colab and Apache Spark for big data processing and analytics.

By the conclusion of this training, participants will be equipped to:

• Configure a big data environment using Google Colab and Spark.
• Efficiently process and analyze large datasets via Apache Spark.
• Integrate Apache Spark with cloud-based tools.

Big data analytics is the process of scrutinizing extensive and diverse datasets to reveal correlations, latent patterns, and other valuable insights.

The healthcare sector generates vast volumes of complex, heterogeneous medical and clinical data. Leveraging big data analytics within this domain holds immense potential for deriving insights that enhance healthcare delivery. However, the sheer scale of these datasets presents significant challenges for analysis and practical implementation in clinical environments.

Through this instructor-led, live remote training, participants will learn how to conduct big data analytics in healthcare by completing a series of hands-on live-lab exercises.

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

• Install and configure big data analytics tools, including Hadoop MapReduce and Spark
• Comprehend the characteristics of medical data
• Apply big data techniques to manage medical data
• Explore big data systems and algorithms in the context of healthcare applications

Target Audience

• Developers
• Data Scientists

Course Format

• A blend of lectures, discussions, exercises, and intensive hands-on practice.

Note

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

This instructor-led live training in Serbia (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 ecosystem: 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 at Serbia (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 course offers a hands-on introduction to developing scalable data processing and Machine Learning workflows using PySpark. Participants will gain insights into how Apache Spark functions within contemporary Big Data ecosystems and learn to process large datasets efficiently by applying distributed computing principles.

This instructor-led, live training in Serbia (online or onsite) is designed for data scientists who intend to use the SMACK stack to build data processing platforms for big data solutions.

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

• Implement a data pipeline architecture for processing big data.
• Develop cluster infrastructure using Apache Mesos and Docker.
• Analyze data with Spark and Scala.
• Manage unstructured data with Apache Cassandra.

This instructor-led, live training in Serbia (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.

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

Upon completing this training, participants will be able to:

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

The initial learning curve for Apache Spark is steep, requiring significant effort before yielding tangible results. This course is designed to help you navigate that challenging first phase. Upon completion, participants will grasp the fundamental concepts of Apache Spark, clearly distinguish between RDDs and DataFrames, master the Python and Scala APIs, and comprehend the roles of executors and tasks. Adhering to industry best practices, the curriculum places a strong emphasis on cloud deployment, 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 services.

AUDIENCE:

Data Engineer, DevOps, Data Scientist

OBJECTIVE:

This course provides an introduction to Apache Spark. Students will learn how Spark integrates into the Big Data ecosystem and how to utilize it for data analysis. The curriculum covers the Spark shell for interactive data exploration, Spark internals, APIs, Spark SQL, Spark Streaming, as well as Machine Learning and GraphX.

AUDIENCE:

Developers and Data Analysts

This instructor-led, live training in Serbia (online or onsite) targets 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 begin building NLP pipelines with Spark NLP.
• Grasp the features, architecture, and benefits of employing Spark NLP.
• Utilize 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 to real-world use cases (clinical data, customer behavior insights, etc.).

In this instructor-led, live training in Serbia, participants will learn how to combine Python and Spark to analyze big data while engaging in 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 Serbia (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 serves as Apache Spark's dedicated module for handling both structured and unstructured data. It exposes metadata regarding data structure and the computations being executed, enabling the engine to apply performance optimizations. The primary applications of Spark SQL include:
- Executing SQL queries.
- Accessing data from an existing Hive deployment.

During this instructor-led live training (available onsite or remotely), participants will acquire the skills necessary to analyze diverse data sets using Spark SQL.

Upon completion of this course, participants will be capable of:

• Installing and setting up Spark SQL.
• Conducting data analysis with Spark SQL.
• Querying data sets in various formats.
• Visualizing data and the outcomes of queries.

Course Format

• Interactive lectures and discussions.
• Extensive exercises and practical activities.
• Practical implementation within a live-lab environment.

Customization Options for the Course

• To arrange a customized training session for this course, please reach out to us.

Stratio is a comprehensive, data-centric platform that unifies big data capabilities, artificial intelligence, and governance into a single, cohesive solution. Its Rocket and Intelligence modules facilitate rapid data exploration, transformation, and advanced analytics, making it ideal for enterprise environments.

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

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

• Efficiently navigate and operate within the Stratio platform, utilizing both Rocket and Intelligence modules.
• Apply PySpark effectively for data ingestion, transformation, and analytical tasks.
• Utilize loops and conditional logic to orchestrate data workflows and execute feature engineering.
• Develop and manage user-defined functions (UDFs) to create reusable data operations in PySpark.

Course Format

• Interactive lectures and group discussions.
• Extensive exercises and practical practice sessions.
• Hands-on implementation within a live laboratory environment.

Course Customization Options

• For customized training tailored to your specific needs, please reach out to us to make arrangements.