Talend Big Data Integration Training Course

This instructor-led, live training in the UAE (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.

Apache Hadoop stands as the leading framework for processing Big Data across server clusters. This three-day (or four-day optional) course equips participants with a comprehensive understanding of Hadoop's business advantages and practical use cases. Attendees will learn how to plan for cluster deployment and scalability, and master the installation, maintenance, monitoring, troubleshooting, and optimization of Hadoop systems. The curriculum includes hands-on practice with bulk data loading, exploration of various Hadoop distributions, and the installation and management of ecosystem tools. The course concludes with an in-depth discussion on securing clusters using Kerberos.

“...The materials were very well prepared and covered thoroughly. The Lab was very helpful and well organized”
— Andrew Nguyen, Principal Integration DW Engineer, Microsoft Online Advertising

Audience

Hadoop administrators

Format

Lectures and hands-on labs, approximate balance 60% lectures, 40% labs.

Informatica with Big Data (BDM) is a specialized program aimed at empowering data professionals to develop, manage, and analyze extensive datasets by leveraging cutting-edge technologies and architectures in the Big Data landscape. The training emphasizes the complete data lifecycle, encompassing ingestion, integration, cleansing, curation, analytics, and the delivery and consumption of big data services.
Participants will explore solutions for processing large datasets using prominent Big Data technologies such as Apache Hive, Apache Hadoop, and Apache Spark. The course also offers hands-on experience with Informatica tools like Bloombox, Big Data Management, and iData Fabric to deepen understanding of underlying big data concepts like MapReduce and Hadoop. Upon completion, learners will be capable of building comprehensive, end-to-end data solutions using Informatica and its associated Big Data offerings.

Apache NiFi is an open-source platform designed for flow-based data integration and event processing. It facilitates automated, real-time data routing, transformation, and system mediation between disparate systems, featuring a web-based UI and fine-grained control.

This instructor-led live training (available onsite or remotely) targets intermediate-level administrators and engineers who aim to deploy, manage, secure, and optimize NiFi dataflows within production environments.

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

• Installing, configuring, and maintaining Apache NiFi clusters.
• Designing and managing dataflows from diverse sources and destinations.
• Implementing flow automation, routing, and transformation logic.
• Optimizing performance, monitoring operations, and troubleshooting issues.

Course Format

• Interactive lectures with real-world architecture discussions.
• Hands-on labs focused on building, deploying, and managing flows.
• Scenario-based exercises conducted in a live-lab environment.

Course Customization Options

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

In this instructor-led, live training in the UAE, participants will learn the fundamentals of flow-based programming as they develop a number of demo extensions, components and processors using Apache NiFi.

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

• Understand NiFi's architecture and dataflow concepts.
• Develop extensions using NiFi and third-party APIs.
• Custom develop their own Apache Nifi processor.
• Ingest and process real-time data from disparate and uncommon file formats and data sources.

This training offers a hands-on introduction to constructing 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 leveraging distributed computing principles.

This instructor-led, live training in the UAE (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 instructor-led live training in the UAE (online or onsite) is designed for system administrators at the beginner to intermediate level 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.

The learning curve for Apache Spark can be steep initially, often requiring significant effort before yielding tangible results. This course is designed to help you navigate that challenging early phase. By the end of the course, participants will grasp the fundamentals of Apache Spark, clearly distinguish between RDDs and DataFrames, and gain proficiency in both Python and Scala APIs. Learners will also develop a solid understanding of executors, tasks, and other core concepts. Aligning with industry best practices, the curriculum places a strong emphasis on cloud deployment, specifically focusing on Databricks and AWS. Additionally, students will learn to differentiate between AWS EMR and AWS Glue, highlighting one of AWS's most recent Spark-related services.

AUDIENCE:

Data Engineers, DevOps Professionals, Data Scientists

OBJECTIVE:

This course provides an introduction to Apache Spark. Participants will gain insights into where Spark fits within the Big Data ecosystem and how to leverage it for data analysis. The curriculum includes hands-on training with the Spark shell for interactive analysis, an exploration of Spark internals, API usage, Spark SQL, Spark Streaming, as well as Machine Learning and GraphX.

AUDIENCE :

Developers and Data Analysts

This instructor-led live training, conducted in the UAE (either 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:

• 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.).

In this instructor-led, live training in the UAE, 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 the UAE (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.

Stratio serves as a comprehensive, data-centric platform that unifies big data, artificial intelligence, and governance into a single solution. Its Rocket and Intelligence modules facilitate rapid data exploration, transformation, and advanced analytics within enterprise settings.

This instructor-led live training (available online or on-site) targets intermediate-level data professionals aiming to leverage the Rocket and Intelligence modules in Stratio effectively with PySpark. The curriculum focuses on looping structures, user-defined functions, and advanced data logic.

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

• Navigating and operating within the Stratio platform using the Rocket and Intelligence modules.
• Applying PySpark for data ingestion, transformation, and analysis.
• Utilizing loops and conditional logic to manage data workflows and feature engineering tasks.
• Creating and managing user-defined functions (UDFs) for reusable data operations in PySpark.

Course Format

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

Customization Options

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