Apache Spark SQL Training Course

This instructor-led, live training (available online or onsite) is designed for intermediate-level data scientists and engineers who wish to utilize Google Colab and Apache Spark for big data processing and analytics. By the end of this training, participants will be able to: - Configure a big data environment using Google Colab and Apache Spark. - Efficiently process and analyze large datasets with Apache Spark. - Visualize big data in a collaborative setting. - Integrate Apache Spark with cloud-based tools for government.

Big data analytics involves the process of examining large volumes of diverse data sets to uncover correlations, hidden patterns, and other valuable insights.

The healthcare industry manages vast amounts of complex and heterogeneous medical and clinical data. Applying big data analytics to health data holds significant potential for enhancing the delivery of healthcare services. However, the scale and complexity of these datasets present substantial challenges in analysis and practical application within a clinical setting.

In this instructor-led, live training (remote), participants will learn how to perform big data analytics in the healthcare sector through a series of hands-on, live-lab exercises.

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

• Install and configure big data analytics tools such as Hadoop MapReduce and Spark
• Understand the unique characteristics of medical data
• Apply advanced big data techniques to manage and analyze medical data
• Examine big data systems and algorithms in the context of health applications

Audience

• Developers
• Data Scientists

Format of the Course

• Part lecture, part discussion, exercises, and extensive hands-on practice.

Note

• To request a customized training for government or other specific needs, please contact us to arrange.

This instructor-led, live training in US (online or onsite) is aimed at system administrators who wish to learn how to set up, deploy, and manage Hadoop clusters within their organization for government use. 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 a storage engine for on-premise Spark deployments. - Configure Spark to access alternative storage solutions such as Amazon S3 and NoSQL database systems like Redis, Elasticsearch, Couchbase, Aerospike, etc. - Perform administrative tasks such as provisioning, management, monitoring, and securing an Apache Hadoop cluster.

In this instructor-led, live training in US (onsite or remote), participants will learn how to set up and integrate various Stream Processing frameworks with existing big data storage systems and related software applications and microservices for government. By the end of this training, participants will be able to: - Install and configure different Stream Processing frameworks, such as Spark Streaming and Kafka Streams. - Understand and select the most appropriate 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, etc., ensuring seamless integration within government systems. - Choose and integrate the most suitable stream processing library with enterprise applications and microservices for government.

Python is a high-level programming language renowned for its clear syntax and code readability. Apache Spark is a powerful data processing engine utilized for querying, analyzing, and transforming large datasets. PySpark facilitates the integration of Spark with Python, enabling seamless data processing workflows. Target Audience: Intermediate-level professionals in the banking sector who are familiar with Python and Spark and aim to enhance their expertise in big data processing and machine learning techniques, specifically for government and private sector applications.

This instructor-led, live training in US (online or onsite) is aimed at data scientists who wish to utilize the SMACK stack to develop robust data processing platforms for government. By the end of this training, participants will be able to: - Implement a data pipeline architecture suitable for processing large-scale data. - Develop a cluster infrastructure using Apache Mesos and Docker. - Analyze data effectively with Spark and Scala. - Manage unstructured data efficiently with Apache Cassandra.

This instructor-led, live training in US (online or onsite) is aimed at engineers who wish to set up and deploy the 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. - Efficiently process and analyze extensive datasets. - Understand the distinctions between Apache Spark and Hadoop MapReduce, and determine which is more suitable for specific tasks. - Integrate Apache Spark with other machine learning tools to enhance data processing capabilities for government applications.

This instructor-led, live training in US (online or onsite) is designed for beginner to intermediate-level system administrators who aim to deploy, maintain, and optimize Spark clusters for government use. 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 effectively. - Optimize the performance of Spark clusters to meet operational requirements. - Implement security measures and ensure high availability for government systems. - Debug and troubleshoot common issues that may arise in Spark deployments.

Apache Spark has a steep initial learning curve that requires significant effort to overcome before yielding tangible results. This course is designed to help participants navigate this challenging first phase. Upon completion of the course, attendees will have a solid understanding of Apache Spark fundamentals, including the distinction between RDD and DataFrame, proficiency in using Python and Scala APIs, and knowledge of executors and tasks. The curriculum emphasizes best practices, with a strong focus on cloud deployment, particularly through platforms such as Databricks and AWS. Participants will also gain insights into the differences between AWS EMR and AWS Glue, one of AWS's latest Spark services. This training is tailored for government data engineers, DevOps professionals, and data scientists to enhance their capabilities in leveraging Apache Spark for government projects.

OBJECTIVE:

This course will introduce Apache Spark, focusing on its integration into the Big Data ecosystem and its application for data analysis. Participants will learn how to utilize the Spark shell for interactive data analysis, understand Spark internals, and work with Spark APIs, Spark SQL, Spark streaming, machine learning, and GraphX.

AUDIENCE:

Developers and Data Analysts for government

This instructor-led, live training in [location] (online or onsite) is designed for government data scientists and developers who wish to utilize 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 for government projects. - Understand the features, architecture, and benefits of using Spark NLP in a public sector context. - Utilize the pre-trained models available in Spark NLP to implement text processing solutions tailored to government needs. - Learn how to build, train, and scale Spark NLP models for production-grade projects within government agencies. - Apply classification, inference, and sentiment analysis on real-world use cases relevant to government operations (such as public health data, citizen feedback, etc.).

In this instructor-led, live training in [location], participants will learn how to leverage Python and Spark together to analyze large datasets as they engage in hands-on exercises. By the end of this training, participants will be able to: - Utilize Spark with Python for comprehensive big data analysis. - Engage in exercises that simulate real-world scenarios. - Apply various tools and techniques for big data analysis using PySpark, enhancing their capabilities for government projects.

This instructor-led, live training in US (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 for government. 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 efficient big data processing. - Explore tools in the Spark ecosystem, including Spark MlLib, Spark Streaming, Kafka, Sqoop, Flume, and others. - Build collaborative filtering recommendation systems similar to those used by Netflix, YouTube, Amazon, Spotify, and Google. - Use Apache Mahout to scale machine learning algorithms for government applications.

Stratio is a data-centric platform that integrates big data, AI, and governance into a single solution. Its Rocket and Intelligence modules facilitate rapid data exploration, transformation, and advanced analytics in enterprise environments. This instructor-led, live training (online or onsite) is designed for intermediate-level data professionals who aim to effectively utilize the Rocket and Intelligence modules in Stratio with PySpark, focusing on looping structures, user-defined functions, and advanced data logic. By the end of this training, participants will be able to: - Navigate and work within the Stratio platform using the Rocket and Intelligence modules. - Apply PySpark for data ingestion, transformation, and analysis. - Use loops and conditional logic to control data workflows and feature engineering tasks. - Create and manage user-defined functions (UDFs) for reusable data operations in PySpark. **Format of the Course** - Interactive lecture and discussion. - Extensive exercises and practice sessions. - Hands-on implementation in a live-lab environment. **Course Customization Options for Government** To request a customized training for this course, please contact us to arrange.