Data Streaming and Real Time Data Processing Training Course

This instructor-led, live training (online or onsite) is designed for advanced-level data professionals who aim to optimize data processing workflows, ensure data integrity, and implement robust data lakehouse solutions that can address the complexities of modern big data applications for government. By the end of this training, participants will be able to: - Gain a comprehensive 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. - 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 [location] (online or onsite) is aimed at beginner-level data professionals who wish 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 for government. By the end of this training, participants will be able to: - Gain a thorough understanding of Apache Iceberg's architecture, features, and benefits. - Learn about table formats, partitioning, schema evolution, and time travel capabilities. - Install and configure Apache Iceberg in different environments. - Create, manage, and manipulate Iceberg tables. - Understand the process of migrating data from other table formats to Iceberg.

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.

Advances in technology and the proliferation of information are transforming how business is conducted across various industries, including operations for government. The rate of data generation and digital archiving within government agencies is increasing due to the rapid adoption of mobile devices and applications, smart sensors and devices, cloud computing solutions, and citizen-facing portals. As the volume and complexity of digital information grow, the challenges related to information management, processing, storage, security, and disposition also become more intricate. New capture, search, discovery, and analysis tools are enabling organizations to derive valuable insights from their unstructured data. The government market is at a critical juncture, recognizing that information is a strategic asset. Government entities need to protect, leverage, and analyze both structured and unstructured information to better serve the public and meet mission requirements. As leaders in government strive to evolve into data-driven organizations, they are laying the groundwork to correlate dependencies across events, people, processes, and information.

High-value solutions for government will be created through 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 enable government to make better decisions by acting on patterns revealed through the analysis of large volumes of data—whether related or unrelated, structured or unstructured.

Achieving these outcomes requires more than just accumulating vast amounts of data. "Making sense of these volumes of Big Data necessitates cutting-edge tools and technologies that can analyze and extract useful knowledge from diverse streams of information," according to Tom Kalil and Fen Zhao of the White House Office of Science and Technology Policy.

The White House took a significant step in 2012 by establishing the National Big Data Research and Development Initiative. This initiative allocated more than $200 million to capitalize on the explosion of Big Data and the tools needed for its analysis.

The challenges posed by Big Data are as formidable as its promise is encouraging. Efficient data storage is one such challenge. Budget constraints require agencies to minimize per-megabyte storage costs while ensuring easy access to data for users. Backing up massive quantities of data further complicates this issue.

Effective data analysis presents another major challenge. Many agencies use commercial tools to sift through large datasets, identifying trends that can enhance operational efficiency. A recent study by MeriTalk found that federal IT executives believe Big Data could help agencies save more than $500 billion while also fulfilling mission objectives.

Custom-developed Big Data tools are also enabling agencies to analyze their data effectively. For example, the Oak Ridge National Laboratory’s Computational Data Analytics Group has made its Piranha data analytics system available to other government entities. This system has aided medical researchers in identifying a link that can alert doctors to aortic aneurysms before they occur. It is also used for more routine tasks, such as filtering through resumes to connect job candidates with hiring managers.

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 course covers the entire lifecycle from data ingestion, integration, cleansing, and curation to advanced data analytics and the delivery 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 deep understanding of big data technologies including MapReduce and Hadoop. By the end of this course, participants will be proficient in creating comprehensive end-to-end data solutions using Informatica and its associated Big Data tools.

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 provides fine-grained control over data flows. 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 use. 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 lecture with real-world architecture discussions relevant to government operations. - Hands-on labs: building, deploying, and managing flows in a government context. - Scenario-based exercises in a live-lab environment aligned with public sector workflows. **Course Customization Options** - To request a customized training for this course, tailored specifically for government needs, please contact us to arrange.

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 section is intentionally left blank for government use.

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.

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.