IBM Datastage For Administrators and Developers Training Course

This instructor-led, live training in India (available online or onsite) is targeted at advanced-level data professionals seeking to optimize data processing workflows, ensure data integrity, and implement robust data lakehouse solutions capable of handling the complexities of modern big data applications.

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

• Acquire a deep understanding of Iceberg’s architecture, including metadata management and file layout.
• Configure Iceberg for optimal performance across 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 India (online or onsite) is designed for 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.

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 in India (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.

Technological advancements and the exponential growth of information are reshaping business operations across numerous sectors, including government. The rate at which governments generate data and archive it digitally is accelerating, driven by the proliferation of mobile devices and apps, smart sensors, cloud computing solutions, and public-facing portals. As digital information expands in volume and complexity, managing, processing, storing, securing, and disposing of it becomes increasingly intricate. Emerging tools for capturing, searching, discovering, and analyzing data are enabling organizations to extract valuable insights from unstructured sources. The government sector is reaching a critical juncture, recognizing information as a strategic asset. Agencies must protect, leverage, and analyze both structured and unstructured data to better serve citizens and meet mission objectives. As government leaders evolve toward data-driven organizations to successfully achieve their missions, they are establishing the framework to correlate dependencies across events, personnel, processes, and information.

High-impact government solutions are emerging from the integration of the most disruptive technologies:

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

Big Data serves as a key intelligent industry solution, empowering governments to make superior decisions by acting upon patterns revealed through the analysis of vast volumes of data—whether related or unrelated, structured or unstructured.

However, achieving these outcomes requires far more than merely accumulating large amounts 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 noted in a post on the OSTP Blog.

The White House took a significant step toward assisting agencies in identifying these technologies by establishing the National Big Data Research and Development Initiative in 2012. This initiative allocated over $200 million to maximize the potential of the Big Data explosion and the tools necessary to analyze it.

The challenges posed by Big Data are nearly as formidable as its promise is encouraging. Efficiently storing data is one such challenge. With budgets often tight, agencies must minimize the cost per megabyte of storage while ensuring data remains easily accessible so users can retrieve it as needed. Backing up massive data volumes further complicates this task.

Effectively analyzing data presents another major challenge. Many agencies utilize commercial tools to sift through vast amounts of data, identifying trends that enhance operational efficiency. (A recent MeriTalk study revealed that federal IT executives believe Big Data could help agencies save over $500 billion while also fulfilling mission objectives.)

Custom-developed Big Data tools are also enabling agencies to meet their analytical needs. For instance, the Oak Ridge National Laboratory’s Computational Data Analytics Group has made its Piranha data analytics system available to other agencies. This system has assisted medical researchers in identifying links that alert doctors to aortic aneurysms before they occur. It is also employed for routine tasks, such as filtering resumes to match job candidates with hiring managers.

Upon completing this instructor-led, live training in India, participants will develop a practical, real-world understanding of Big Data, along with its associated technologies, methodologies, and tools.

Participants will have the chance to apply this knowledge through hands-on exercises. Group interaction and instructor feedback form a crucial part of the class experience.

The course begins with an introduction to the foundational concepts of Big Data, then moves on to the programming languages and methodologies employed for Data Analysis. Finally, we explore the tools and infrastructure that support Big Data storage, Distributed Processing, and Scalability.

Apache NiFi is an open-source platform for data integration and event processing that relies on flow-based architecture. It facilitates automated, real-time data routing, transformation, and system mediation between disparate systems, featuring a web-based UI and fine-grained control capabilities.

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

Upon completion of this training, participants will be capable of:

• Installing, configuring, and maintaining Apache NiFi clusters.
• Designing and managing dataflows connecting various sources and sinks.
• Implementing logic for flow automation, routing, and transformation.
• Optimizing performance, monitoring operations, and resolving issues.

Course Format

• Interactive lectures accompanied by discussions on real-world architecture.
• Hands-on labs focused on building, deploying, and managing flows.
• Scenario-based exercises conducted within a live-lab environment.

Course Customization Options

• For custom training requests regarding this course, please reach out to us to make arrangements.

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 in India (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.

This instructor-led, live training in India (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.

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

In this instructor-led live training India, 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 India (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.

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.

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.