Big Data Training in Pierre

This instructor-led, live training in Pierre (online or onsite) is aimed at intermediate-level data scientists and engineers who wish to utilize Google Colab and Apache Spark for big data processing and analytics for government.

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

• Configure a big data environment using Google Colab and 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 use.

Stratio is a data-centric platform that integrates big data, artificial intelligence (AI), and governance into a single solution. Its Rocket and Intelligence modules enable 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 wish to effectively utilize the Rocket and Intelligence modules in Stratio with PySpark, focusing on looping structures, user-defined functions, and advanced data logic. The training emphasizes practical application of these tools to enhance data workflows and feature engineering tasks.

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 in a structured manner.
• Utilize loops and conditional logic to manage data workflows and feature engineering tasks efficiently.
• Create and manage user-defined functions (UDFs) for reusable data operations in PySpark, enhancing productivity and consistency.

Format of the Course

• Interactive lecture and discussion to facilitate understanding and engagement.
• Extensive exercises and practice sessions to reinforce learning.
• Hands-on implementation in a live-lab environment to apply concepts directly.

Course Customization Options

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

Data Warehousing is the practice of designing, building, and operating centralized data repositories that support analytics, reporting, and decision-making for government.

This instructor-led, live training (online or onsite) is aimed at intermediate-level data professionals who wish to model dimensional data, build robust ETL pipelines, and optimize analytical workloads for government.

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

• Explain core data warehousing concepts and architectures.
• Design dimensional models and select between star and snowflake schemas.
• Build and orchestrate ETL and ELT pipelines reliably.
• Differentiate OLTP from OLAP workloads and optimize for analytics.

Format of the Course

• Interactive lecture and discussion.
• Extensive exercises and practice sessions.
• Hands-on implementation in a live-lab environment.

Course Customization Options

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

Participants who complete this instructor-led, live training in Pierre will gain a practical, real-world understanding of Big Data and its related technologies, methodologies, and tools for government use.

Participants will have the opportunity to apply this knowledge through hands-on exercises. Group interaction and instructor feedback are integral components of the class.

The course begins with an introduction to fundamental concepts of Big Data, then advances into the programming languages and methodologies used for Data Analysis. Finally, we explore the tools and infrastructure that support Big Data storage, Distributed Processing, and Scalability in government operations.

Greenplum is an open-source massively parallel processing (MPP) data platform designed for analytics and large-scale data environments.

This instructor-led, live training (online or onsite) is aimed at intermediate to advanced technical users who wish to understand the internal architecture of Greenplum, build optimized data models, and apply high-performance SQL in distributed environments.

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

• Understand Greenplum’s shared-nothing architecture and parallelism model for government applications.
• Design distributed table structures and partitions optimized for query performance in public sector workflows.
• Use indexing strategies, storage formats, and physical design techniques to enhance data management for government.
• Interpret EXPLAIN plans and optimize queries using best practices aligned with governance standards.

Format of the Course

• Interactive lecture and discussion focused on public sector needs.
• Extensive exercises and practice tailored to government data environments.
• Hands-on implementation in a live-lab environment to simulate real-world scenarios for government.

Course Customization Options

• To request a customized training for this course, specifically tailored for government use cases, please contact Govtra to arrange.

Greenplum is an open-source, massively parallel data platform built on PostgreSQL, designed for advanced analytics and large-scale data warehousing.

This instructor-led, live training (online or onsite) is aimed at intermediate-level administrators who wish to install, configure, and manage Greenplum environments effectively, including managing system updates and libraries for government use.

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

• Install and configure a production-ready Greenplum cluster.
• Manage system updates and apply patches safely.
• Handle Greenplum library management for analytics and extensions.
• Troubleshoot and monitor Greenplum systems.

Format of the Course

• Interactive lecture and discussion.
• Hands-on use of Greenplum in a controlled lab environment for government.
• Guided exercises focused on cluster setup, upgrade workflows, and system extensions.

Course Customization Options

• To request a customized training for this course based on your specific environment or data platform needs for government, please contact us to arrange.

This instructor-led, live training in Pierre (online or onsite) is aimed at advanced-level data professionals who wish to optimize data processing workflows, ensure data integrity, and implement robust data lakehouse solutions for government that can handle the complexities of modern big data applications.

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

• Gain an in-depth understanding of Iceberg’s architecture, including metadata management and file layout.
• Configure Iceberg for optimal performance in various environments and integrate it with multiple data processing engines for government.
• Manage large-scale Iceberg tables, perform complex schema changes, and handle partition evolution.
• Master techniques to optimize query performance and data scan efficiency for large datasets.
• Implement mechanisms to ensure data consistency, manage transactional guarantees, and handle failures in distributed environments.

This instructor-led, live training in Pierre (online or onsite) is designed for government data professionals at the beginner level who seek to acquire the knowledge and skills necessary to effectively utilize Apache Iceberg for managing large-scale datasets, ensuring data integrity, and optimizing data processing workflows.

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

• Gain a comprehensive understanding of Apache Iceberg's architecture, features, and benefits for government use.
• Learn about table formats, partitioning, schema evolution, and time travel capabilities.
• Install and configure Apache Iceberg in various environments.
• Create, manage, and manipulate Iceberg tables.
• Understand the process of migrating data from other table formats to Iceberg for government applications.

This instructor-led, live training in Pierre (online or onsite) is aimed at intermediate-level IT professionals who wish to enhance their skills in data architecture, governance, cloud computing, and big data technologies to effectively manage and analyze large datasets for data migration within their organizations for government.

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

• Understand the foundational concepts and components of various data architectures for government use.
• Gain a comprehensive understanding of data governance principles and their importance in regulatory environments for government operations.
• Implement and manage data governance frameworks such as Dama and Togaf for government applications.
• Leverage cloud platforms for efficient data storage, processing, and management in a government context.

This instructor-led, live training in Pierre (online or onsite) is aimed at intermediate-level data engineers who wish to learn how to use Azure Data Lake Storage Gen2 for effective data analytics solutions for government.

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

• Understand the architecture and key features of Azure Data Lake Storage Gen2 for government applications.
• Optimize data storage and access for cost efficiency and performance in public sector workflows.
• Integrate Azure Data Lake Storage Gen2 with other Azure services to enhance analytics and data processing capabilities for government operations.
• Develop solutions using the Azure Data Lake Storage Gen2 API, tailored to meet the specific needs of government agencies.
• Troubleshoot common issues and optimize storage strategies to ensure compliance with governance and accountability standards.

This instructor-led, live training in Pierre (online or onsite) is designed for government developers who wish to use and integrate Spark, Hadoop, and Python to process, analyze, and transform large and complex data sets for government purposes.

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 for government applications.
• Understand the features, core components, and architecture of Spark and Hadoop as they relate to public sector workflows.
• Learn how to integrate Spark, Hadoop, and Python for efficient big data processing in a government context.
• Explore the tools in the Spark ecosystem (Spark MlLib, Spark Streaming, Kafka, Sqoop, Flume) that are relevant for government data analysis.
• Build collaborative filtering recommendation systems similar to those used by Netflix, YouTube, Amazon, Spotify, and Google, tailored for government use cases.
• Use Apache Mahout to scale machine learning algorithms in a manner aligned with public sector governance and accountability requirements.

Big Data and Advanced Analytics involves the application of sophisticated techniques and tools to analyze large, complex datasets, providing actionable insights and strategic decision-making capabilities.

This instructor-led, live training (online or onsite) is designed for advanced-level data professionals who seek to leverage cutting-edge analytical methods and big data technologies for predictive, prescriptive, and real-time analytics in their public sector roles.

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

• Design and implement large-scale data processing pipelines for both structured and unstructured data.
• Apply advanced machine learning and deep learning techniques to massive datasets.
• Leverage distributed computing frameworks for real-time analytics and data streaming.
• Integrate big data analytics into business intelligence and decision-making systems, enhancing governance and accountability for government operations.

Format of the Course

• Interactive lecture and discussion tailored to public sector workflows.
• Extensive exercises and practice sessions to reinforce learning.
• Hands-on implementation in a live-lab environment, simulating real-world for government scenarios.

Course Customization Options

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

This instructor-led, live training in Pierre (online or onsite) is designed for intermediate-level IT professionals who seek a comprehensive understanding of IBM DataStage from both an administrative and development perspective. The goal is to enable participants to manage and utilize this tool effectively in their respective roles within government organizations.

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

• Understand the fundamental concepts of DataStage.
• Effectively install, configure, and manage DataStage environments for government use.
• Connect to various data sources and efficiently extract data from databases, flat files, and external sources.
• Implement effective data loading techniques for government applications.

This instructor-led, live training in Pierre (online or onsite) is aimed at beginner to intermediate-level system administrators who wish 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 for government operations.
• Manage cluster resources and monitor Spark applications to ensure efficient public sector workflows.
• Optimize the performance of Spark clusters to meet government standards and requirements.
• Implement security measures and ensure high availability to support robust governance and accountability.
• Debug and troubleshoot common Spark issues to maintain reliable service delivery for government agencies.

Participants who complete this instructor-led, live training in Pierre will gain a practical, real-world understanding of Big Data and its related technologies, methodologies, and tools for government.

Participants will have the opportunity to apply this knowledge through hands-on exercises. Group interaction and instructor feedback are integral components of the class.

The course begins with an introduction to foundational concepts of Big Data, then progresses into the programming languages and methodologies used for Data Analysis. Finally, we discuss the tools and infrastructure that enable Big Data storage, Distributed Processing, and Scalability in public sector environments.

In this instructor-led, live training in Pierre, 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:

• Understand how to use Spark with Python for big data analysis.
• Work through exercises that simulate real-world scenarios.
• Utilize various tools and techniques for big data analysis using PySpark, tailored for government applications.

This instructor-led, live training (online or onsite) is aimed at software developers, managers, and business analysts who wish to use big data systems to store and retrieve large amounts of data for government applications.

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

• Efficiently query large datasets.
• Comprehend how big data systems manage and access data.
• Leverage the latest big data systems available for government use.
• Transform data from various systems into reporting formats.
• Master writing SQL queries in:
  • MySQL
  • Postgres
  • Hive Query Language (HiveQL/HQL)
  • Redshift

Course Format

• Interactive lecture and discussion.
• Extensive exercises and practice sessions.
• Hands-on implementation in a live-lab environment.

Customization Options for Government

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

Audience:

This course is designed for IT specialists seeking solutions to store and process large data sets in a distributed system environment, specifically tailored for government.

Goal:

To provide deep knowledge on Hadoop cluster administration for government.

Advances in technologies and the increasing amount of information are transforming how business is conducted in many industries, including government. Government data generation and digital archiving rates are on the rise due to the rapid growth of mobile devices and applications, smart sensors and devices, cloud computing solutions, and citizen-facing portals. As digital information expands and becomes more complex, information management, processing, storage, security, and disposition become more intricate as well. New capture, search, discovery, and analysis tools are helping organizations gain insights from their unstructured data. The government market is at a tipping point, recognizing that information is a strategic asset, and the public sector needs to protect, leverage, and analyze both structured and unstructured information to better serve and meet mission requirements. As government leaders strive to evolve into data-driven organizations to successfully accomplish missions, they are laying the groundwork to correlate dependencies across events, people, processes, and information.

High-value solutions for government will be created from a combination of the most disruptive technologies:

• Mobile devices and applications
• Cloud services
• Social business technologies and networking
• Big Data and analytics

Big Data is one of the intelligent industry solutions that allows government to make better decisions by taking action based on patterns revealed by analyzing large volumes of data — related and unrelated, structured and unstructured.

But accomplishing these feats takes far more than simply accumulating massive quantities of data. "Making sense of these volumes of Big Data requires cutting-edge tools and technologies that can analyze and extract useful knowledge from vast and diverse streams of information," Tom Kalil and Fen Zhao of the White House Office of Science and Technology Policy wrote in a post on the OSTP Blog.

The White House took a step toward helping agencies find these technologies when it established the National Big Data Research and Development Initiative in 2012. The initiative included more than $200 million to make the most of the explosion of Big Data and the tools needed to analyze it for government.

The challenges that Big Data poses are nearly as daunting as its promise is encouraging. Storing data efficiently is one of these challenges. As always, budgets are tight, so agencies must minimize the per-megabyte price of storage and keep the data within easy access so that users can retrieve it when they need it and how they require it. Backing up massive quantities of data heightens the challenge.

Analyzing the data effectively is another major challenge. Many agencies employ commercial tools that enable them to sift through the mountains of data, spotting trends that can help them operate more efficiently. (A recent study by MeriTalk found that federal IT executives think Big Data could help agencies save more than $500 billion while also fulfilling mission objectives.)

Custom-developed Big Data tools are also allowing agencies to address the need to analyze their data. For example, the Oak Ridge National Laboratory’s Computational Data Analytics Group has made its Piranha data analytics system available to other agencies. The system has helped medical researchers find a link that can alert doctors to aortic aneurysms before they strike. It is also used for more routine tasks, such as sifting through resumes to connect job candidates with hiring managers.

Day 1 - provides a high-level overview of essential Big Data topic areas for government. The module is divided into a series of sections, each of which is accompanied by a hands-on exercise.

Day 2 - explores a range of topics that relate to analysis practices and tools for Big Data environments in the public sector. It does not delve into implementation or programming details but maintains coverage at a conceptual level, focusing on topics that enable participants to develop a comprehensive understanding of the common analysis functions and features offered by Big Data solutions for government.

Day 3 - provides an overview of the fundamental and essential topic areas relating to Big Data solution platform architecture. It covers Big Data mechanisms required for the development of a Big Data solution platform and architectural options for assembling a data processing platform. Common scenarios are also presented to provide a basic understanding of how a Big Data solution platform is generally used in government settings.

Day 4 - builds upon Day 3 by exploring advanced topics related to Big Data solution platform architecture. In particular, different architectural layers that make up the Big Data solution platform are introduced and discussed, including data sources, data ingress, data storage, data processing, and security for government.

Day 5 - covers a number of exercises and problems designed to test the delegates' ability to apply knowledge of topics covered in Days 3 and 4. These exercises are tailored to ensure that participants can effectively implement Big Data solutions within their respective agencies.

Big data analytics involves the process of examining large amounts of varied data sets in order to uncover correlations, hidden patterns, and other useful insights.

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

In this instructor-led, live training (remote), participants will learn how to perform big data analytics in healthcare as they step 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 characteristics of medical data
• Apply big data techniques to manage and analyze medical data
• Study big data systems and algorithms in the context of healthcare 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.

In this instructor-led, live training in Pierre, participants will gain a comprehensive understanding of Big Data technologies, assess their impact on existing processes and policies, and implement these technologies for the purpose of identifying criminal activity and preventing crime. Case studies from law enforcement agencies around the world will be examined to provide insights into their adoption approaches, challenges, and outcomes.

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

• Integrate Big Data technology with traditional data gathering methods to construct a coherent narrative during an investigation.
• Implement scalable big data storage and processing solutions for effective data analysis.
• Develop a proposal for the adoption of the most suitable tools and processes to enable a data-driven approach to criminal investigations, tailored specifically for government use.

Big Data is a term that refers to solutions designed for storing and processing large data sets. Initially developed by Google, these Big Data solutions have since evolved and inspired other similar projects, many of which are available as open-source options for government use. R is a popular programming language in the financial industry.

When traditional storage technologies are unable to manage the volume of data required, numerous alternative solutions are available. This course is designed to guide participants through the various options for storing and analyzing Big Data, highlighting their respective advantages and disadvantages.

This course primarily focuses on discussions and presentations of these solutions, with hands-on exercises available upon request to further enhance understanding and practical application for government contexts.

The course is part of the Data Scientist skill set for government (Domain: Data and Technology).

This classroom-based training session will delve into the realm of Big Data. Participants will engage in computer-based exercises and case studies utilizing relevant big data tools, designed to enhance skills and knowledge for government applications.

Audience

If you are tasked with interpreting the data available to your agency or analyzing unstructured data from public sources (such as Twitter, LinkedIn, etc.), this course is for government professionals like you.

It is primarily designed for decision-makers and individuals who need to determine which data is worth collecting and analyzing within a governmental context.

While the course is not intended for those configuring the technical solutions, participants in these roles will still gain valuable insights into the broader picture of data management for government operations.

Delivery Mode

During the course, delegates will be presented with working examples of primarily open-source technologies that are relevant to public sector workflows and governance.

Short lectures will be followed by presentations and simple exercises to engage participants actively in the learning process.

Content and Software Used

All software used is updated each time the course is conducted, ensuring the use of the most current versions available.

The curriculum covers the entire data lifecycle, from acquisition and formatting to processing and analysis, with a focus on automating decision-making processes using machine learning techniques for government applications.

Big data refers to data sets that are so large and intricate that conventional data processing applications are insufficient for managing them. For government, the challenges associated with big data include capturing data, data storage, data analysis, search capabilities, sharing, transfer, visualization, querying, updating, and ensuring information privacy.

Apache Hadoop is the most widely used framework for processing Big Data on clusters of servers. In this three (optionally four) day course, participants will learn about the business benefits and use cases for Hadoop and its ecosystem, how to plan cluster deployment and growth, and how to install, maintain, monitor, troubleshoot, and optimize Hadoop. They will also practice bulk data load on clusters, become familiar with various Hadoop distributions, and practice installing and managing Hadoop ecosystem tools. The course concludes with a discussion on securing the cluster using Kerberos, ensuring robust security measures for government applications.

“…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 for government

Format

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

Apache Hadoop is the leading framework for processing big data on clusters of servers. This course will introduce developers to various components of the Hadoop ecosystem, including HDFS, MapReduce, Pig, Hive, and HBase, designed to enhance data processing capabilities for government.

Apache Hadoop is one of the most widely used frameworks for processing Big Data on clusters of servers. This course delves into data management in HDFS, advanced Pig, Hive, and HBase. These advanced programming techniques are designed to benefit experienced Hadoop developers for government applications.

Audience: developers

Duration: three days

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

Audience:

This course is designed to clarify big data/Hadoop technology for government audiences, demonstrating that it is accessible and comprehensible.

This instructor-led, live training in Pierre (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 for government applications.
• 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 within a government infrastructure.
• Set up HDFS to operate as a storage engine for on-premise Spark deployments in government environments.
• Configure Spark to access alternative storage solutions such as Amazon S3 and NoSQL database systems like Redis, Elasticsearch, Couchbase, Aerospike, etc., for government data management.
• Perform administrative tasks such as provisioning, management, monitoring, and securing an Apache Hadoop cluster in a government setting.

This course introduces HBase – a NoSQL store built on top of Hadoop. The course is designed for developers who will be using HBase to develop applications and administrators who will manage HBase clusters.

The curriculum will guide developers through HBase architecture, data modeling, and application development on HBase. It will also cover the integration of MapReduce with HBase and various administration topics, including performance optimization. The course is highly hands-on, featuring numerous lab exercises to reinforce learning.

Duration: 3 days

Audience: Developers & Administrators for government

The Informatica with Big Data (BDM) course is designed to equip data professionals for government with the skills necessary to develop, manage, and analyze large data sets, utilizing the latest technologies and architectures in the Big Data domain. This comprehensive course covers the full lifecycle of data management, from ingestion and integration through cleansing, curation, analytics, and the exposure and consumption of big data services.
Participants will delve into solutions that process extensive datasets using Big Data technologies and architectures such as Apache Hive, Apache Hadoop, and Apache Spark. The course also provides hands-on experience with Informatica tools like Bloombox, Big Data Management, and iData Fabric, enabling learners to gain a thorough understanding of big data technologies including Map Reduce and Hadoop. By the conclusion of this course, participants will be proficient in creating end-to-end data solutions using Informatica and its associated Big Data solutions for government.

In this instructor-led, live training in Pierre (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, and other systems.
• Integrate the most suitable stream processing library with enterprise applications and microservices for government use.

This instructor-led, live training in Pierre (online or onsite) is aimed at technical personnel who wish to learn how to implement a machine learning strategy while maximizing the use of big data for government.

By the end of this training, participants will:

• Understand the evolution and trends in machine learning.
• Be aware of how machine learning is being utilized across various industries.
• Become familiar with the tools, skills, and services available to implement machine learning within an organization for government use.
• Comprehend how machine learning can enhance data mining and analysis efforts.
• Learn about the concept of a data middle backend and its applications in business operations.
• Understand the role that big data and intelligent applications are playing across different sectors.

Apache NiFi is an open-source, flow-based data integration and event-processing platform designed to facilitate automated, real-time data routing, transformation, and system mediation between diverse systems. It features a web-based user interface and granular control mechanisms.

This instructor-led, live training (onsite or remote) is tailored for intermediate-level administrators and engineers who aim to deploy, manage, secure, and optimize NiFi dataflows in production environments for government operations.

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

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

Format of the Course

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

Course Customization Options

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

In this instructor-led, live training in Pierre, participants will gain a comprehensive understanding of flow-based programming as they develop various demo extensions, components, and processors using Apache NiFi for government applications.

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

• Comprehend NiFi's architecture and dataflow principles.
• Develop extensions utilizing NiFi and third-party APIs.
• Create custom Apache NiFi processors tailored to specific needs.
• Ingest and process real-time data from diverse and uncommon file formats and data sources, enhancing data management for government workflows.

Python is a high-level programming language renowned for its clarity of syntax and readability of code. Apache Spark is a powerful data processing engine designed for querying, analyzing, and transforming large datasets. PySpark enables users to integrate Spark with Python, facilitating efficient big data operations.

Target Audience: Intermediate-level professionals in the banking sector who are familiar with Python and Spark and are looking to enhance their expertise in big data processing and machine learning techniques, specifically for government applications.

This instructor-led, live training in Pierre (online or onsite) is aimed at data scientists who wish to utilize the SMACK stack to develop robust data processing platforms for big data solutions for government.

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

• Design and implement a data pipeline architecture for efficient big data processing.
• Establish a cluster infrastructure using Apache Mesos and Docker to support scalable operations.
• Perform advanced data analysis with Spark and Scala to derive actionable insights.
• Effectively manage unstructured data with Apache Cassandra to ensure data integrity and accessibility.

This instructor-led, live training in Pierre (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 for government applications.

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

• Install and configure Apache Spark in a secure and scalable manner.
• Efficiently process and analyze extensive data sets to support public sector workflows.
• Understand the differences between Apache Spark and Hadoop MapReduce, and determine which is more suitable for specific government use cases.
• Integrate Apache Spark with other machine learning tools to enhance data analysis capabilities for government projects.

The initial learning curve for Apache Spark can be steep, requiring significant effort to achieve early results. This course is designed to help participants navigate the initial challenges and gain a solid foundation in Apache Spark. Upon completion of the course, attendees will understand the fundamental concepts of Apache Spark, differentiate between RDD and DataFrame, and learn the Python and Scala APIs. They will also grasp the roles of executors and tasks. Following best practices for government, this course places a strong emphasis on cloud deployment, particularly with Databricks and AWS. Students will gain insights into the differences 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 will introduce Apache Spark for government. Participants will learn how Spark fits into the Big Data ecosystem and how to utilize Spark for data analysis in public sector environments. The curriculum covers interactive data analysis using the Spark shell, Spark internals, Spark APIs, Spark SQL, Spark streaming, and machine learning with GraphX.

AUDIENCE:

Developers / Data Analysts

This instructor-led, live training in Pierre (online or onsite) is aimed at 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 for government.

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.
• Understand the features, architecture, and benefits of using Spark NLP in a public sector context.
• Leverage the pre-trained models available in Spark NLP to implement text processing solutions.
• 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 such as clinical data and customer behavior insights for enhanced public service delivery.

Spark SQL is a module of Apache Spark designed for processing structured and unstructured data. It provides insights into the structure of the data and the computations being executed, enabling optimization opportunities. Common applications of Spark SQL include:
- Executing SQL queries.
- Reading data from an existing Hive installation.

In this instructor-led, live training (onsite or remote), participants will learn how to analyze various types of datasets using Spark SQL for government.

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

• Install and configure Spark SQL.
• Perform data analysis using Spark SQL.
• Query datasets in different formats.
• Visualize data and query results.

Format of the Course

• Interactive lecture and discussion.
• Extensive exercises and practice sessions.
• Hands-on implementation in a live-lab environment.

Course Customization Options

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

This instructor-led, live training in Pierre (online or onsite) is designed for technical personnel who wish to deploy Talend Open Studio for Big Data to streamline the process of analyzing and processing large datasets.

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

• Install and configure Talend Open Studio for Big Data.
• Connect with big data systems such as Cloudera, HortonWorks, MapR, Amazon EMR, and Apache.
• Understand and set up the big data components and connectors within Open Studio.
• Configure parameters to automatically generate MapReduce code.
• Use Open Studio's intuitive drag-and-drop interface to execute Hadoop jobs.
• Prototype big data pipelines for government applications.
• Automate big data integration projects for enhanced efficiency and accuracy in public sector workflows.