Talend Big Data Integration 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.

Apache Hadoop stands as the leading framework for processing Big Data across server clusters. Over the course of three days (with an optional fourth day), participants will explore the business advantages and practical applications of Hadoop and its surrounding ecosystem. The training covers essential skills such as planning cluster deployment and scaling, installing, maintaining, monitoring, troubleshooting, and optimizing Hadoop environments. Attendees will also gain hands-on experience with bulk data loading, explore various Hadoop distributions, and learn to install and manage tools within the Hadoop ecosystem. The course concludes with a session 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

A combination of lectures and hands-on labs, with an approximate split of 60% instruction and 40% practical lab work.

Informatica with Big Data (BDM) is a program aimed at empowering data professionals to develop, manage, and analyze large datasets by leveraging the most advanced technologies and architectures in the Big Data sector. The curriculum covers the entire lifecycle, from data ingestion, integration, cleansing, and curation, to data analytics and the exposure and consumption of big data services.
Participants will explore solutions that process massive datasets using Big Data technologies and architectures 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 your understanding of big data technologies like MapReduce and Hadoop. Upon completion, learners will be equipped to create 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 diverse systems, supported by a web-based UI and granular control mechanisms.

This instructor-led training, available both onsite and remotely, targets intermediate-level administrators and engineers looking 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 across various sources and sinks.
• Implementing logic for flow automation, routing, and data transformation.
• Optimizing performance, monitoring operations, and troubleshooting issues.

Course Format

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

Customization Options

• For customized training tailored to your needs, please contact us to arrange.

In this instructor-led live training in Serbia, 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 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 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.

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.