Apache Spark Training in Bhutan

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

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

• Establish a big data environment using Google Colab and Spark.
• Process and analyse large datasets efficiently with Apache Spark.
• Visualise big data in a collaborative environment.
• Integrate Apache Spark with cloud-based tools.

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 sophisticated analytics within enterprise settings.

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

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

• Navigate and effectively utilize the Stratio platform, specifically the Rocket and Intelligence modules.
• Apply PySpark techniques for data ingestion, transformation, and analysis.
• Utilize loops and conditional logic to manage data workflows and execute feature engineering tasks.
• Develop and manage user-defined functions (UDFs) to enable 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.

Customization Options

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

This instructor-led, live training in Bhutan (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.

This instructor-led, live training in Bhutan (online or onsite) is aimed at beginner-level to intermediate-level system administrators 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.

In this instructor-led live training Bhutan, participants will learn how to combine Python and Spark to analyze big data while working through practical, 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 Bhutan (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 ecoystem: 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 in Bhutan (onsite or remote), participants will learn how to set up and integrate various Stream Processing frameworks with existing big data storage systems, as well as related software applications and microservices.

Upon completing this training, participants will be able to:

• Install and configure various Stream Processing frameworks, including 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 more.
• Integrate the most appropriate stream processing library with enterprise applications and microservices.

This training offers a hands-on introduction to constructing scalable data processing and Machine Learning workflows using PySpark. Participants will gain insight 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, conducted in Bhutan (online or onsite), is tailored for data scientists who intend to utilize the SMACK stack to develop data processing platforms for big data solutions.

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

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

This instructor-led, live training in Bhutan (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.

Initially, the learning curve for Apache Spark can be steep, requiring significant effort before yielding tangible results. This course is designed to help you navigate that initial challenging phase. Upon completion, participants will grasp the fundamentals of Apache Spark, clearly distinguish between RDDs and DataFrames, and gain proficiency in the Python and Scala APIs. The curriculum covers essential concepts such as executors and tasks, alongside best practices with a strong emphasis on cloud deployment using Databricks and AWS. Students will also explore the distinctions between AWS EMR and AWS Glue, one of the latest Spark services offered by AWS.

AUDIENCE:

Data Engineers, DevOps Professionals, Data Scientists

OBJECTIVE:

This course provides an introduction to Apache Spark. Students will gain insights into how Spark integrates within the Big Data ecosystem and learn to leverage it for data analysis. The curriculum encompasses the Spark shell for interactive analysis, 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 Bhutan (online or onsite) is aimed at 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.).

Spark SQL serves as Apache Spark's dedicated module for handling both structured and unstructured data. It offers insights into the data's structure and the computations being executed, which can be leveraged to optimize performance. The primary applications of Spark SQL include:

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

Upon completing this training, participants will be capable of:

• Installing and setting up Spark SQL.
• Conducting data analysis with Spark SQL.
• Querying data sets across various formats.
• Visualizing data and query outcomes.

Course Format

• Interactive lectures and discussions.
• Extensive exercises and practice sessions.
• Practical implementation in a live-lab environment.

Course Customization Options

• To arrange a customized training session for this course, please get in touch with us.