Big Data Training in the UAE

Stratio is a data-centric platform that integrates big data, 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 aimed at intermediate-level data professionals who wish to use the Rocket and Intelligence modules in Stratio effectively 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 Rocket and Intelligence modules.
• Apply PySpark in the context of 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.
• Lots of exercises and practice.
• 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 the UAE will gain a practical, real-world understanding of Big Data and its related technologies, methodologies and tools.

Participants will have the opportunity to put this knowledge into practice through hands-on exercises. Group interaction and instructor feedback make up an important component of the class.

The course starts with an introduction to elemental concepts of Big Data, then progresses into the programming languages and methodologies used to perform Data Analysis. Finally, we discuss the tools and infrastructure that enable Big Data storage, Distributed Processing, and Scalability.

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-level to advanced-level 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.
• Design distributed table structures and partitions optimized for query performance.
• Use indexing strategies, storage formats, and physical design techniques.
• Interpret EXPLAIN plans and optimize queries using best practices.

Format of the Course

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

Course Customization Options

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

Confluent Apache Kafka is a distributed event streaming platform designed for high-throughput, fault-tolerant data pipelines and real-time analytics.

This instructor-led, live training (online or onsite) is aimed at intermediate-level system administrators and DevOps professionals who wish to install, configure, monitor, and troubleshoot Confluent Apache Kafka clusters.

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

• Understand the components and architecture of Confluent Kafka.
• Deploy and manage Kafka brokers, Zookeeper quorums, and key services.
• Configure advanced features including security, replication, and performance tuning.
• Use management tools to monitor and maintain Kafka clusters.

Format of the Course

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

Course Customization Options

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

Greenplum is an open-source massively parallel data platform based 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.

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.
• 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 environment or data platform, please contact us to arrange.

This instructor-led, live training in the UAE (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 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.
• 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 the UAE (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.

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 of Iceberg tables.
• Understand the process of migrating data from other table formats to Iceberg.

This instructor-led, live training in the UAE (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.

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

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

This instructor-led, live training in the UAE (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.

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

• Understand the architecture and key features of Azure Data Lake Storage Gen2.
• Optimize data storage and access for cost and performance.
• Integrate Azure Data Lake Storage Gen2 with other Azure services for analytics and data processing.
• Develop solutions using the Azure Data Lake Storage Gen2 API.
• Troubleshoot common issues and optimize storage strategies.

This instructor-led, live training in the UAE (online or onsite) is aimed at intermediate-level IT professionals who wish to have a comprehensive understanding of IBM DataStage from both an administrative and a development perspective, allowing them to manage and utilize this tool effectively in their respective workplaces.

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

• Understand the core concepts of DataStage.
• Learn how to effectively install, configure, and manage DataStage environments.
• Connect to various data sources and extract data efficiently from databases, flat files, and external sources.
• Implement effective data loading techniques.

This instructor-led, live training in the UAE (online or onsite) is aimed at intermediate-level big data professionals who wish to utilize Apache Kylin for building real-time data warehouses and performing multidimensional analysis on large-scale datasets.

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

• Set up and configure Apache Kylin with real-time streaming data sources.
• Design and build OLAP cubes for both batch and streaming data.
• Perform complex queries with sub-second latency using Kylin's SQL interface.
• Integrate Kylin with BI tools for interactive data visualization.
• Optimize performance and manage resources effectively in Kylin.

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

Participants will have the opportunity to put this knowledge into practice through hands-on exercises. Group interaction and instructor feedback make up an important component of the class.

The course starts with an introduction to elemental concepts of Big Data, then progresses into the programming languages and methodologies used to perform Data Analysis. Finally, we discuss the tools and infrastructure that enable Big Data storage, Distributed Processing, and Scalability.

In this instructor-led, live training in the UAE, participants will learn how to use Python and Spark together to analyze big data as they work on 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 course focuses on utilizing the Hive SQL language (also known as Hive HQL, SQL on Hive, or HiveQL) for individuals who need to extract data from Hive.

This instructor-led, live training in the UAE (online or onsite) is aimed at intermediate-level database administrators, developers, and analysts who wish to master advanced SQL functionalities for complex data operations and database management.

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

• Perform advanced querying techniques using unions, subqueries, and complex joins.
• Add, update, and delete data, tables, views, and indexes with precision.
• Ensure data integrity through transactions and manipulate database structures.
• Create and manage databases efficiently for robust data storage and retrieval.

Dremio is an open-source "self-service data platform" that speeds up querying across various types of data sources. It integrates with relational databases, Apache Hadoop, MongoDB, Amazon S3, ElasticSearch, and other data repositories. Dremio supports SQL and offers a web UI for constructing queries.

In this instructor-led live training session, participants will learn how to install, configure, and utilize Dremio as a unifying layer for data analysis tools and underlying data repositories.

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

• Install and set up Dremio
• Run queries against multiple data sources regardless of their location, size, or structure
• Integrate Dremio with BI tools and data repositories such as Tableau and Elasticsearch

Audience

• Data scientists
• Business analysts
• Data engineers

Format of the course

• The course includes lectures, discussions, exercises, and extensive hands-on practice.

Notes

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

This instructor-led, live training (online or onsite) is designed for software developers, managers, and business analysts who wish to leverage big data systems for storing and retrieving large volumes of data.

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

• Efficiently query large datasets.
• Comprehend how Big Data systems store and retrieve information.
• Utilize the most recent big data technologies available.
• Transform data from various systems into reporting tools.
• Master writing SQL queries in:
  • MySQL
  • Postgres
  • Hive Query Language (HiveQL/HQL)
  • Redshift

Course Format

• Interactive lectures and discussions.
• Absorbing exercises and ample practice opportunities.
• Practical implementation in a live-lab setting.

Customization Options for the Course

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

This instructor-led, live training in the UAE (online or onsite) is aimed at intermediate-level database professionals who wish to enhance their skills in Oracle SQL development and administration.

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

• Build and optimize complex SQL queries.
• Manage databases efficiently using Oracle SQL tools.
• Apply best practices in database development and maintenance.
• Administer user access and database security in an Oracle environment.

Apache Accumulo is a sorted, distributed key-value storage system that offers reliable and scalable data management. It draws its design inspiration from Google's BigTable and operates on the foundation of Apache Hadoop, Apache Zookeeper, and Apache Thrift.

This instructor-led course delves into the operational principles of Accumulo and guides participants through creating a sample application using Apache Accumulo.

Course Format

• The course includes lectures, discussions, practical development exercises, and occasional assessments to evaluate comprehension.

This course targets developers and data scientists interested in integrating AI into their applications. It places particular emphasis on Data Analysis, Distributed AI, and Natural Language Processing.

Amazon Redshift is a cloud-based data warehouse service in AWS designed to handle petabyte-scale datasets.

In this instructor-led live session, participants will gain an understanding of the basics of Amazon Redshift.

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

• Set up and configure Amazon Redshift
• Load, manage, deploy, query, and visualize data using Amazon Redshift

Audience

• Developers
• IT Professionals

Course Format

• The course includes lectures, discussions, exercises, and extensive hands-on practice.

Note

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

Technological advancements and the surge in information are revolutionizing business operations across various sectors, including government. The rise of mobile devices and applications, smart sensors and gadgets, cloud computing solutions, and citizen-centric portals is driving up data generation and digital archiving rates within governmental bodies. As digital information grows more extensive and intricate, managing, processing, storing, securing, and disposing of it becomes increasingly complex. New tools for capturing, searching, discovering, and analyzing unstructured data are aiding organizations in extracting valuable insights. The government sector is at a pivotal moment, recognizing that information is a strategic asset and that both structured and unstructured data must be protected, utilized, and analyzed to better serve citizens and meet mission requirements. As governmental leaders work towards transforming their organizations into data-driven entities to achieve their missions effectively, they are setting the stage for correlating dependencies across events, individuals, processes, and information.

High-value government solutions will emerge from a blend of disruptive technologies:

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

IDC forecasts that by 2020, the IT industry will reach $5 trillion, which is approximately $1.7 trillion more than today’s figures, with around 80% of this growth attributed to third-platform technologies. In the long run, these technologies will be essential for managing the complexity brought about by increased digital information. Big Data stands out as one of the intelligent industry solutions, enabling governments to make informed decisions based on patterns identified through the analysis of large volumes of data — whether related or unrelated, structured or unstructured.

However, achieving these goals requires more than just accumulating vast amounts of data. "Interpreting such extensive Big Data necessitates cutting-edge tools and technologies capable of analyzing and extracting valuable insights from diverse information streams," as noted by Tom Kalil and Fen Zhao in a post on the White House Office of Science and Technology Policy blog.

The White House took a significant step towards assisting agencies in finding these technologies when it launched 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 required for its analysis.

Big Data presents challenges that are nearly as formidable as its promise is promising. Efficient data storage is one such challenge. With tight budgets, agencies must reduce per-megabyte storage costs while ensuring easy access to the data when needed. Backing up large volumes of data adds another layer of complexity.

Effectively analyzing the data is also a significant hurdle. Many agencies use commercial tools that help them sift through vast amounts of data and identify trends that can enhance operational efficiency (a recent MeriTalk study found that federal IT executives believe Big Data could save more than $500 billion while fulfilling mission objectives).

Custom-developed Big Data tools are also aiding agencies in analyzing their data. 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 helped medical researchers identify a link that can alert doctors to potential aortic aneurysms before they occur. It is also used for more routine tasks like matching job candidates with hiring managers by sifting through résumés.

Apache Beam is an open-source, unified programming model designed for defining and executing parallel data processing pipelines. Its strength lies in its capability to handle both batch and streaming pipelines, with execution supported by various distributed processing back-ends such as Apache Apex, Apache Flink, Apache Spark, and Google Cloud Dataflow. Apache Beam is particularly useful for ETL tasks, including moving data between different storage systems and sources, transforming it into a more suitable format, and loading it into new systems.

In this instructor-led training session (held either on-site or remotely), participants will learn how to integrate the Apache Beam SDKs within a Java or Python application to define a data processing pipeline that breaks down large datasets into smaller segments for independent and parallel processing.

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

• Install and configure Apache Beam.
• Utilize a single programming model within their Java or Python application to perform both batch and stream processing.
• Run pipelines across multiple environments.

Course Format

• A combination of lectures, discussions, exercises, and extensive hands-on practice.

Note

• This course will be offered in Scala in the future. Please contact us to arrange.

This in-class training session will delve into the realm of Big Data. Participants will engage in hands-on computer-based examples and case studies using pertinent big data tools.

Day 1 – offers an extensive overview of key Big Data areas. The session is segmented into various sections, each complemented by practical exercises.

Day 2 – delves into a variety of themes related to analysis methodologies and tools for Big Data environments. It maintains a conceptual focus without diving into implementation or programming specifics, aiding participants in gaining a thorough understanding of the typical analysis capabilities provided by Big Data solutions.

Day 3 – presents an overview of foundational and critical topics concerning Big Data solution platform architecture. This includes mechanisms necessary for developing a Big Data solution platform and various architectural choices for constructing a data processing platform. Common use cases are also outlined to offer a fundamental grasp of how a Big Data solution platform is typically utilized.

Day 4 – expands on Day 3 by examining advanced subjects related to Big Data solution platform architecture. It introduces and discusses different architectural layers that constitute the Big Data solution platform, such as data sources, data ingress, data storage, data processing, and security.

Day 5 – includes several exercises and challenges aimed at assessing participants’ ability to apply knowledge from Days 3 and 4.

In this instructor-led, live training in the UAE, participants will learn the mindset with which to approach 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 organizations around the world will be examined to gain insights on their adoption approaches, challenges and results.

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

• Combine Big Data technology with traditional data gathering processes to piece together a story during an investigation.
• Implement industrial big data storage and processing solutions for data analysis.
• Prepare a proposal for the adoption of the most adequate tools and processes for enabling a data-driven approach to criminal investigation.

Big Data encompasses solutions designed for managing and analyzing extensive datasets. Initially developed by Google, these Big Data solutions have advanced significantly and have spurred numerous comparable initiatives, many of which are now offered as open-source options. In the financial sector, R is a widely-used programming language.

When conventional storage technologies fall short in managing your data volume, numerous alternatives exist. This course aims to guide participants through the various options for storing and analyzing Big Data, along with their respective advantages and disadvantages.

The course primarily emphasizes discussions and presentations of solutions, although hands-on exercises can be provided upon request.

This course is included in the Data Scientist skill set (Domain: Data and Technology).

This instructor-led, live training (delivered online or at your site) is designed for engineers who want to leverage Confluent (a Kafka distribution) to develop and manage a real-time data processing platform for their applications.

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

• Set up and configure the Confluent Platform.
• Utilize Confluent's management tools and services to simplify Kafka operations.
• Store and process incoming data streams.
• Optimize and manage Kafka clusters effectively.
• Secure their data streams.

Course Format

• Engaging lectures and discussions.
• Ample exercises and practice sessions.
• Practical implementation in a live-lab setting.

Customization Options for the Course

• The course is based on the open-source version of Confluent: Confluent Open Source.
• To arrange for customized training, please contact us to discuss your requirements.

Audience

This course is ideal for you if you are trying to interpret the data at your disposal or wish to analyze unstructured data available online (such as from Twitter and LinkedIn).

It primarily targets decision-makers and individuals who must determine which data is valuable to collect and analyze.

While it is not specifically designed for those configuring solutions, they can still gain a comprehensive understanding of the big picture.

Delivery Mode

Throughout the course, participants will be shown practical examples using mostly open-source technologies.

Brief lectures will be complemented by presentations and straightforward exercises led by the attendees.

Content and Software Used

The software utilized in this course is updated with each session to ensure we are working with the latest versions available.

The curriculum encompasses the entire process from acquiring, formatting, processing, and analyzing data, to automating decision-making processes through machine learning.

In this instructor-led, live training in the UAE, participants will learn how to build a Data Vault.

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

• Understand the architecture and design concepts behind Data Vault 2.0, and its interaction with Big Data, NoSQL and AI.
• Use data vaulting techniques to enable auditing, tracing, and inspection of historical data in a data warehouse.
• Develop a consistent and repeatable ETL (Extract, Transform, Load) process.
• Build and deploy highly scalable and repeatable warehouses.

This instructor-led, live training in the UAE (online or onsite) is aimed at architects, developers, and administrators who wish to use Denodo Platform to optimize and accelerate data management through data virtualization.

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

• Install and configure Denodo Platform.
• Understand the features and architecture of Denodo Platform.
• Understand the key concepts, benefits, and use cases for data virtualization.
• Learn how to configure and manage the Denodo Platform server.
• Implement data security, user access, and services authentication.
• Apply the tools and techniques for operations monitoring and performance optimization.

Apache Druid is an open-source, column-oriented, distributed data storage system developed in Java. It is designed for rapid ingestion of large volumes of event data and the execution of low-latency OLAP queries on that data. Druid is frequently utilized in business intelligence applications to analyze extensive amounts of real-time and historical data. Additionally, it excels at supporting fast, interactive analytical dashboards for end-users. Companies such as Alibaba, Airbnb, Cisco, eBay, Netflix, PayPal, and Yahoo employ Druid.

In this instructor-led course, we delve into the limitations of traditional data warehouse solutions and explore how Druid can complement these technologies to create a flexible and scalable streaming analytics environment. Through numerous examples, participants will have the opportunity to implement and test Druid-based solutions in a lab setting.

Course Format

• The course includes lectures, discussions, extensive hands-on practice, and occasional assessments to evaluate comprehension.

Big data refers to extensive and intricate datasets that conventional data processing applications cannot effectively manage. The challenges associated with big data encompass capturing the data, storing it, analyzing it, searching through it, sharing it, transferring it, visualizing it, querying it, updating it, and ensuring information privacy.

This instructor-led, live training in the UAE (online or onsite) introduces the principles and approaches behind distributed stream and batch data processing, and walks participants through the creation of a real-time, data streaming application in Apache Flink.

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

• Set up an environment for developing data analysis applications.
• Understand how Apache Flink's graph-processing library (Gelly) works.
• Package, execute, and monitor Flink-based, fault-tolerant, data streaming applications.
• Manage diverse workloads.
• Perform advanced analytics.
• Set up a multi-node Flink cluster.
• Measure and optimize performance.
• Integrate Flink with different Big Data systems.
• Compare Flink capabilities with those of other big data processing frameworks.

In this instructor-led, live training in the UAE, participants will learn about the technology offerings and implementation approaches for processing graph data. The aim is to identify real-world objects, their characteristics and relationships, then model these relationships and process them as data using a Graph Computing (also known as Graph Analytics) approach. We start with a broad overview and narrow in on specific tools as we step through a series of case studies, hands-on exercises and live deployments.

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

• Understand how graph data is persisted and traversed.
• Select the best framework for a given task (from graph databases to batch processing frameworks.)
• Implement Hadoop, Spark, GraphX and Pregel to carry out graph computing across many machines in parallel.
• View real-world big data problems in terms of graphs, processes and traversals.

This instructor-led, live training in the UAE (online or onsite) is aimed at administrators who wish to set up Greenplum Database for business intelligence and data warehousing solutions.

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

• Address processing needs with Greenplum.
• Perform ETL operations for data processing.
• Leverage existing query processing infrastructures.

This instructor-led, live training in the UAE (online or onsite) introduces Hortonworks Data Platform (HDP) and walks participants through the deployment of Spark + Hadoop solution.

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

• Use Hortonworks to reliably run Hadoop at a large scale.
• Unify Hadoop's security, governance, and operations capabilities with Spark's agile analytic workflows.
• Use Hortonworks to investigate, validate, certify and support each of the components in a Spark project.
• Process different types of data, including structured, unstructured, in-motion, and at-rest.

Cloudera Impala is an open-source, massively parallel processing (MPP) SQL query engine designed for Apache Hadoop clusters.

Impala allows users to execute low-latency SQL queries on data stored in the Hadoop Distributed File System and Apache HBase without needing to move or transform the data.

Audience

This course is intended for analysts and data scientists who analyze data stored in Hadoop using Business Intelligence or SQL tools.

Upon completion of this course, participants will be able to

• Extract valuable insights from Hadoop clusters using Impala.
• Create specific programs to support Business Intelligence with Impala's SQL dialect.
• Troubleshoot issues in Impala.

In this instructor-led, live training in the UAE (onsite or remote), participants will learn how to set up and integrate different Stream Processing frameworks with existing big data storage systems and related software applications and microservices.

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

• Install and configure different Stream Processing frameworks, such as Spark Streaming and Kafka Streaming.
• Understand and select the most appropriate framework for the job.
• Process of data continuously, concurrently, and in a record-by-record fashion.
• Integrate Stream Processing solutions with existing databases, data warehouses, data lakes, etc.
• Integrate the most appropriate stream processing library with enterprise applications and microservices.

This instructor-led, live training in the UAE (online or onsite) is aimed at data engineers, data scientists, and programmers who wish to use Apache Kafka features in data streaming with Python.

By the end of this training, participants will be able to use Apache Kafka to monitor and manage conditions in continuous data streams using Python programming.

Kafka Streams is a client-side library designed for developing applications and microservices that exchange data with a Kafka messaging system. Traditionally, Apache Kafka has depended on Apache Spark or Apache Storm for processing data between message producers and consumers. However, by utilizing the Kafka Streams API within an application, data can be processed directly inside Kafka, eliminating the need to send it to another cluster for processing.

In this instructor-led live training session, participants will learn how to incorporate Kafka Streams into a series of sample Java applications that exchange data with Apache Kafka for stream processing.

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

• Comprehend the features and benefits of Kafka Streams compared to other stream processing frameworks
• Process streaming data directly within a Kafka cluster
• Create Java or Scala applications or microservices that integrate with Kafka and Kafka Streams
• Write succinct code that converts input Kafka topics into output Kafka topics
• Construct, package, and deploy the application

Audience

• Developers

Course Format

• The course includes lectures, discussions, exercises, and extensive hands-on practice.

Notes

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

This instructor-led, live training in the UAE (online or onsite) is aimed at developers who wish to implement Apache Kafka stream processing without writing code.

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

• Install and configure Confluent KSQL.
• Set up a stream processing pipeline using only SQL commands (no Java or Python coding).
• Carry out data filtering, transformations, aggregations, joins, windowing, and sessionization entirely in SQL.
• Design and deploy interactive, continuous queries for streaming ETL and real-time analytics.

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

By the end of this training, participants will:

• Understand the evolution and trends for machine learning.
• Know how machine learning is being used across different industries.
• Become familiar with the tools, skills and services available to implement machine learning within an organization.
• Understand how machine learning can be used to enhance data mining and analysis.
• Learn what a data middle backend is, and how it is being used by businesses.
• Understand the role that big data and intelligent applications are playing across industries.

In this instructor-led, live training in the UAE (onsite or remote), participants will learn how to deploy and manage Apache NiFi in a live lab environment.

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

• Install and configure Apachi NiFi.
• Source, transform and manage data from disparate, distributed data sources, including databases and big data lakes.
• Automate dataflows.
• Enable streaming analytics.
• Apply various approaches for data ingestion.
• Transform Big Data and into business insights.

In this instructor-led, live training in the UAE, participants will learn the fundamentals of flow-based programming as they develop a number of demo extensions, components and processors using Apache NiFi.

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

• Understand NiFi's architecture and dataflow concepts.
• Develop extensions using NiFi and third-party APIs.
• Custom develop their own Apache Nifi processor.
• Ingest and process real-time data from disparate and uncommon file formats and data sources.

This instructor-led, live training in the UAE (online or onsite) is aimed at data engineers, data scientists, and programmers who wish to use Spark Streaming features in processing and analyzing real-time data.

By the end of this training, participants will be able to use Spark Streaming to process live data streams for use in databases, filesystems, and live dashboards.

MLlib serves as the machine learning (ML) library for Spark, aiming to make scalable and user-friendly practical machine learning accessible. It encompasses various learning algorithms and utilities such as classification, regression, clustering, collaborative filtering, dimensionality reduction, along with foundational optimization tools and advanced pipeline APIs.

The library is divided into two main packages:

• spark.mllib includes the original API constructed using RDDs.

• spark.ml offers a more sophisticated API based on DataFrames, facilitating the creation of ML pipelines.

Audience

This course is tailored for engineers and developers looking to leverage an integrated Machine Learning Library within Apache Spark.

This instructor-led, live training in the UAE (online or onsite) is aimed at technical persons who wish to deploy Talend Open Studio for Big Data to simplifying the process of reading and crunching through Big Data.

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 Open Studio's big data components and connectors.
• Configure parameters to automatically generate MapReduce code.
• Use Open Studio's drag-and-drop interface to run Hadoop jobs.
• Prototype big data pipelines.
• Automate big data integration projects.

This instructor-led, live training introduces the concepts behind interactive data analytics and walks participants through the deployment and usage of Zeppelin in a single-user or multi-user environment.

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

• Install and configure Zeppelin
• Develop, organize, execute and share data in a browser-based interface
• Visualize results without referring to the command line or cluster details
• Execute and collaborate on long workflows
• Work with any of a number of plug-in language/data-processing-backends, such as Scala (with Apache Spark), Python (with Apache Spark), Spark SQL, JDBC, Markdown and Shell.
• Integrate Zeppelin with Spark, Flink and Map Reduce
• Secure multi-user instances of Zeppelin with Apache Shiro