Big Data - Data Science Training Course

Audience:

This course is designed for IT professionals seeking a robust solution for storing and processing large volumes of data within a distributed system environment.

Goal:

To provide in-depth knowledge regarding the administration of Hadoop clusters.

This instructor-led live training in Mexico (online or onsite) is tailored for intermediate-level data scientists and engineers looking to utilize Google Colab and Apache Spark for big data processing and analytics.

By the conclusion 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 most widely adopted framework for processing Big Data across server clusters. This course, spanning three days with an optional fourth day, equips participants with a deep understanding of Hadoop’s business advantages and practical applications within its ecosystem. Attendees will learn how to plan for cluster deployment and scalability, as well as how to install, maintain, monitor, troubleshoot, and optimize Hadoop environments. Hands-on practice will include bulk data loading, exploring various Hadoop distributions, and managing ecosystem tools. The curriculum concludes with a focus on enhancing cluster security through Kerberos.

“The course materials were exceptionally well-prepared and thorough. The laboratory sessions were highly beneficial and professionally organized.”
— Andrew Nguyen, Principal Integration DW Engineer, Microsoft Online Advertising

Audience

Hadoop system administrators

Format

The course combines lectures with hands-on labs, maintaining an approximate balance of 60% instructional content and 40% practical laboratory exercises.

Apache Hadoop is the leading framework for processing Big Data across server clusters. This course introduces developers to key components of the Hadoop ecosystem, including HDFS, MapReduce, Pig, Hive, and HBase.

Apache Hadoop stands as one of the most widely adopted frameworks for processing Big Data across server clusters. This course explores data management within HDFS, alongside advanced techniques in Pig, Hive, and HBase. These sophisticated programming methods are designed to benefit experienced Hadoop developers.

Audience: developers

Duration: three days

Format: lectures (50%) and hands-on labs (50%).

Target Audience:

This course aims to demystify big data and Hadoop technologies, demonstrating that they are accessible and straightforward to master.

This instructor-led, live training in Mexico (online or onsite) targets 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.
• Grasp the four key components of the Hadoop ecosystem: HDFS, MapReduce, YARN, and Hadoop Common.
• Leverage the Hadoop Distributed File System (HDFS) to scale a cluster to hundreds or thousands of nodes.
• Configure HDFS to function as the storage engine for on-premise Spark deployments.
• Configure Spark to access alternative storage solutions like Amazon S3 and NoSQL databases such as Redis, Elasticsearch, Couchbase, Aerospike, and others.
• Perform administrative tasks including provisioning, management, monitoring, and securing an Apache Hadoop cluster.

This course introduces HBase, a NoSQL database built on top of Hadoop. It is designed for developers who will be creating applications with HBase, as well as administrators responsible for managing HBase clusters.

Developers will learn about HBase architecture, data modeling, and application development. The course also covers using MapReduce with HBase and touches on administration topics related to performance optimization. It is highly practical, featuring numerous hands-on lab exercises.

Duration: 3 days

Audience: Developers & Administrators

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

This instructor-led, live training (onsite or remote) is aimed at intermediate-level administrators and engineers who wish to deploy, manage, secure, and optimize NiFi dataflows in production environments.

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

• Install, configure, and maintain Apache NiFi clusters.
• Design and manage dataflows from varied sources and sinks.
• Implement flow automation, routing, and transformation logic.
• Optimize performance, monitor operations, and troubleshoot issues.

Format of the Course

• Interactive lecture with real-world architecture discussion.
• Hands-on labs: building, deploying, and managing flows.
• Scenario-based exercises in a live-lab environment.

Course Customization Options

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

In this instructor-led, live training in Mexico, 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 creating scalable data processing and Machine Learning workflows using PySpark. Attendees will gain insights into how Apache Spark functions within contemporary Big Data ecosystems and learn to efficiently handle large datasets by applying distributed computing principles.

In this instructor-led, live training in Mexico, 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 Mexico (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-focused platform that unifies big data capabilities, artificial intelligence, and governance into one cohesive solution. Its Rocket and Intelligence modules facilitate swift data exploration, transformation, and sophisticated analytics tailored for enterprise needs.

This live training, led by an instructor and available either online or in-person, is designed for data professionals with an intermediate skill level. The goal is to help participants effectively leverage the Rocket and Intelligence modules in Stratio using PySpark, with a specific focus on loop structures, user-defined functions, and complex data logic.

Upon completing this training, participants will be able to:

• Navigate and operate within the Stratio platform using the Rocket and Intelligence modules.
• Apply PySpark for data ingestion, transformation, and analysis tasks.
• Utilize loops and conditional logic to manage data workflows and execute feature engineering.
• Develop and manage user-defined functions (UDFs) to enable reusable data operations in PySpark.

Course Format

• Interactive lectures and discussions.
• Numerous exercises and practical applications.
• Hands-on implementation within a live lab environment.

Customization Options

• For those interested in a customized version of this course, please reach out to us to arrange details.