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Duration 5 Days 30 CPD hours This course is intended for This intermediate and beyond level course is geared for experienced technical professionals in various roles, such as developers, data analysts, data engineers, software engineers, and machine learning engineers who want to leverage Scala and Spark to tackle complex data challenges and develop scalable, high-performance applications across diverse domains. Practical programming experience is required to participate in the hands-on labs. Overview Working in a hands-on learning environment led by our expert instructor you'll: Develop a basic understanding of Scala and Apache Spark fundamentals, enabling you to confidently create scalable and high-performance applications. Learn how to process large datasets efficiently, helping you handle complex data challenges and make data-driven decisions. Gain hands-on experience with real-time data streaming, allowing you to manage and analyze data as it flows into your applications. Acquire practical knowledge of machine learning algorithms using Spark MLlib, empowering you to create intelligent applications and uncover hidden insights. Master graph processing with GraphX, enabling you to analyze and visualize complex relationships in your data. Discover generative AI technologies using GPT with Spark and Scala, opening up new possibilities for automating content generation and enhancing data analysis. Embark on a journey to master the world of big data with our immersive course on Scala and Spark! Mastering Scala with Apache Spark for the Modern Data Enterprise is a five day hands on course designed to provide you with the essential skills and tools to tackle complex data projects using Scala programming language and Apache Spark, a high-performance data processing engine. Mastering these technologies will enable you to perform a wide range of tasks, from data wrangling and analytics to machine learning and artificial intelligence, across various industries and applications.Guided by our expert instructor, you?ll explore the fundamentals of Scala programming and Apache Spark while gaining valuable hands-on experience with Spark programming, RDDs, DataFrames, Spark SQL, and data sources. You?ll also explore Spark Streaming, performance optimization techniques, and the integration of popular external libraries, tools, and cloud platforms like AWS, Azure, and GCP. Machine learning enthusiasts will delve into Spark MLlib, covering basics of machine learning algorithms, data preparation, feature extraction, and various techniques such as regression, classification, clustering, and recommendation systems. Introduction to Scala Brief history and motivation Differences between Scala and Java Basic Scala syntax and constructs Scala's functional programming features Introduction to Apache Spark Overview and history Spark components and architecture Spark ecosystem Comparing Spark with other big data frameworks Basics of Spark Programming SparkContext and SparkSession Resilient Distributed Datasets (RDDs) Transformations and Actions Working with DataFrames Spark SQL and Data Sources Spark SQL library and its advantages Structured and semi-structured data sources Reading and writing data in various formats (CSV, JSON, Parquet, Avro, etc.) Data manipulation using SQL queries Basic RDD Operations Creating and manipulating RDDs Common transformations and actions on RDDs Working with key-value data Basic DataFrame and Dataset Operations Creating and manipulating DataFrames and Datasets Column operations and functions Filtering, sorting, and aggregating data Introduction to Spark Streaming Overview of Spark Streaming Discretized Stream (DStream) operations Windowed operations and stateful processing Performance Optimization Basics Best practices for efficient Spark code Broadcast variables and accumulators Monitoring Spark applications Integrating External Libraries and Tools, Spark Streaming Using popular external libraries, such as Hadoop and HBase Integrating with cloud platforms: AWS, Azure, GCP Connecting to data storage systems: HDFS, S3, Cassandra, etc. Introduction to Machine Learning Basics Overview of machine learning Supervised and unsupervised learning Common algorithms and use cases Introduction to Spark MLlib Overview of Spark MLlib MLlib's algorithms and utilities Data preparation and feature extraction Linear Regression and Classification Linear regression algorithm Logistic regression for classification Model evaluation and performance metrics Clustering Algorithms Overview of clustering algorithms K-means clustering Model evaluation and performance metrics Collaborative Filtering and Recommendation Systems Overview of recommendation systems Collaborative filtering techniques Implementing recommendations with Spark MLlib Introduction to Graph Processing Overview of graph processing Use cases and applications of graph processing Graph representations and operations Introduction to Spark GraphX Overview of GraphX Creating and transforming graphs Graph algorithms in GraphX Big Data Innovation! Using GPT and Generative AI Technologies with Spark and Scala Overview of generative AI technologies Integrating GPT with Spark and Scala Practical applications and use cases Bonus Topics / Time Permitting Introduction to Spark NLP Overview of Spark NLP Preprocessing text data Text classification and sentiment analysis Putting It All Together Work on a capstone project that integrates multiple aspects of the course, including data processing, machine learning, graph processing, and generative AI technologies.

Duration 4 Days 24 CPD hours This course is intended for This course is designed for data analysts, business intelligence specialists, developers, system architects, and database administrators. Overview Skills gained in this training include:The features that Pig, Hive, and Impala offer for data acquisition, storage, and analysisThe fundamentals of Apache Hadoop and data ETL (extract, transform, load), ingestion, and processing with HadoopHow Pig, Hive, and Impala improve productivity for typical analysis tasksJoining diverse datasets to gain valuable business insightPerforming real-time, complex queries on datasets Cloudera University?s four-day data analyst training course focusing on Apache Pig and Hive and Cloudera Impala will teach you to apply traditional data analytics and business intelligence skills to big data. Hadoop Fundamentals The Motivation for Hadoop Hadoop Overview Data Storage: HDFS Distributed Data Processing: YARN, MapReduce, and Spark Data Processing and Analysis: Pig, Hive, and Impala Data Integration: Sqoop Other Hadoop Data Tools Exercise Scenarios Explanation Introduction to Pig What Is Pig? Pig?s Features Pig Use Cases Interacting with Pig Basic Data Analysis with Pig Pig Latin Syntax Loading Data Simple Data Types Field Definitions Data Output Viewing the Schema Filtering and Sorting Data Commonly-Used Functions Processing Complex Data with Pig Storage Formats Complex/Nested Data Types Grouping Built-In Functions for Complex Data Iterating Grouped Data Multi-Dataset Operations with Pig Techniques for Combining Data Sets Joining Data Sets in Pig Set Operations Splitting Data Sets Pig Troubleshoot & Optimization Troubleshooting Pig Logging Using Hadoop?s Web UI Data Sampling and Debugging Performance Overview Understanding the Execution Plan Tips for Improving the Performance of Your Pig Jobs Introduction to Hive & Impala What Is Hive? What Is Impala? Schema and Data Storage Comparing Hive to Traditional Databases Hive Use Cases Querying with Hive & Impala Databases and Tables Basic Hive and Impala Query Language Syntax Data Types Differences Between Hive and Impala Query Syntax Using Hue to Execute Queries Using the Impala Shell Data Management Data Storage Creating Databases and Tables Loading Data Altering Databases and Tables Simplifying Queries with Views Storing Query Results Data Storage & Performance Partitioning Tables Choosing a File Format Managing Metadata Controlling Access to Data Relational Data Analysis with Hive & Impala Joining Datasets Common Built-In Functions Aggregation and Windowing Working with Impala How Impala Executes Queries Extending Impala with User-Defined Functions Improving Impala Performance Analyzing Text and Complex Data with Hive Complex Values in Hive Using Regular Expressions in Hive Sentiment Analysis and N-Grams Conclusion Hive Optimization Understanding Query Performance Controlling Job Execution Plan Bucketing Indexing Data Extending Hive SerDes Data Transformation with Custom Scripts User-Defined Functions Parameterized Queries Choosing the Best Tool for the Job Comparing MapReduce, Pig, Hive, Impala, and Relational Databases Which to Choose?

The leadership role of the project manager is increasingly recognised as a key determinant in delivering success. These skills can often be critical in project situations, where tight budgets and deadlines demand the highest levels of team performance and where the working environment presents fresh challenges on a daily basis. This programme focuses on the leadership skills required of project managers and will benefit anyone involved in managing projects large or small wishing to extend or enhance those skills. The objectives of this programme are to help the participants: Understand the significance of leadership skills to the project manager and the impact of these skills on project performance Review the key skills needed to be an effective 'multi-dimensional' project leader and learn how to develop, adapt and apply them in practice Learn how to identify the preferred leadership style for the context and organisational culture of a project and how to develop personal style versatility Understand the role of the project leader in building an effective team and the skills required to promote and sustain team performance Gain a better understanding of the interpersonal skills needed to motivate individual team members and harness the full potential of the team DAY ONE 1 Introduction Aims and objectives Personal objectives 2 Project management and leadership What is a leader? How much can leadership be learned? The project environment and the impact of leadership skills The characteristics of high performance project teams and their leaders 3 Principles of effective leadership Some useful models and theories of leadership explored Types of leadership; choosing how to use leadership power Evaluating personal leadership style; how to develop style flexibility 4 Syndicate case study: Leadership in action Review of the role of leadership in a contemporary project Feedback and plenary discussion: effective project leadership 5 Team exercise: Leadership skills of the project manager Teams compete in performing a project simulation Project review and feedback Discussion of the outcome: role and skills of the project leader 6 Leadership skills for project managers The 3 dimensions of project leadership: inwards, outwards, and upwards The vital role of communication skills and how to develop them Developing a 'project vision': strategic thinking skills Understanding others; emotional intelligence skills Being a visible leader; behavioural and influencing skills Building effective relationships; the importance of trust and respect DAY TWO 7 Leading the project team The role of leadership in developing team performance Understanding individual strengths; recognising team role preferences Managing conflict and promoting positive team dynamics Setting standards, maintaining discipline and rewarding performance Harnessing team potential: building motivation within the team Promoting team learning; the team leader as coach / mentor 8 Leading through the organisation Gaining the support others; developing effective influencing skills Getting empowerment from key stakeholders Knowing when and how to take the initiative and lead Building and maintaining rapport with key partners Becoming an effective team player in leadership teams Becoming a business leader; leading colleagues and co-workers 9 Team exercise: Leadership and negotiation Teams engage in a negotiation exercise Exercise review and feedback Discussion of the outcome: negotiation skills of the project leader 10 Negotiation skills for project leaders Characteristics of effective negotiators Classic problem behaviours and mind-sets to avoid Getting to win-win; building partnership and trust 11 Leading more senior stakeholders The challenges and skills of leading and managing upwards Communicating with more senior stakeholders; building credibility Negotiating upwards: knowing when and how The role of networking skills; building and maintaining rapport Handling disagreements; the art of diplomacy Handling personality and style conflicts with more senior people

Duration 4 Days 24 CPD hours This course is intended for Primary target audiences for this training are developers and development consultants but also other roles involved with implementing or reviewing program code to optimize ABAP based applications for SAP HANA. This course will prepare students to develop and optimize ABAP applications that access data stored in the SAP HANA Database. Course Outline Technical concepts of SAP HANA Introduction to HANA Studio Introduction to ADT (ABAP Development Tools, also known as ABAP in Eclipse) Tools to detect potential functional and performance issues when migrating to SAP HANA: Code Inspector and ABAP Test Cockpit, ABAP Trace and ABAP Profiler, SQL Trace Tools to prioritize performance issues: SQL Monitor, SQL Performance Tuning Worklist Performance Rules and Guidelines for ABAP in the context of SAP HANA Optimizing ABAP by accessing SAP HANA as a Secondary Database Optimizing ABAP using SAP HANA as Primary Database Using SAP HANA Fuzzy Search in ABAP Enabling input fields for Type-Ahead search. SAP List Viewer (ALV) for SAP HANA Additional course details: Nexus Humans HA400 SAP ABAP Programming for SAP HANA training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the HA400 SAP ABAP Programming for SAP HANA course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 3 Days 18 CPD hours This course is intended for This course is intended for: Database architects Database administrators Database developers Data analysts and scientists Overview This course is designed to teach you how to: Discuss the core concepts of data warehousing, and the intersection between data warehousing and big data solutions Launch an Amazon Redshift cluster and use the components, features, and functionality to implement a data warehouse in the cloud Use other AWS data and analytic services, such as Amazon DynamoDB, Amazon EMR, Amazon Kinesis, and Amazon S3, to contribute to the data warehousing solution Architect the data warehouse Identify performance issues, optimize queries, and tune the database for better performance Use Amazon Redshift Spectrum to analyze data directly from an Amazon S3 bucket Use Amazon QuickSight to perform data analysis and visualization tasks against the data warehouse Data Warehousing on AWS introduces you to concepts, strategies, and best practices for designing a cloud-based data warehousing solution using Amazon Redshift, the petabyte-scale data warehouse in AWS. This course demonstrates how to collect, store, and prepare data for the data warehouse by using other AWS services such as Amazon DynamoDB, Amazon EMR, Amazon Kinesis, and Amazon S3. Additionally, this course demonstrates how to use Amazon QuickSight to perform analysis on your data Module 1: Introduction to Data Warehousing Relational databases Data warehousing concepts The intersection of data warehousing and big data Overview of data management in AWS Hands-on lab 1: Introduction to Amazon Redshift Module 2: Introduction to Amazon Redshift Conceptual overview Real-world use cases Hands-on lab 2: Launching an Amazon Redshift cluster Module 3: Launching clusters Building the cluster Connecting to the cluster Controlling access Database security Load data Hands-on lab 3: Optimizing database schemas Module 4: Designing the database schema Schemas and data types Columnar compression Data distribution styles Data sorting methods Module 5: Identifying data sources Data sources overview Amazon S3 Amazon DynamoDB Amazon EMR Amazon Kinesis Data Firehose AWS Lambda Database Loader for Amazon Redshift Hands-on lab 4: Loading real-time data into an Amazon Redshift database Module 6: Loading data Preparing Data Loading data using COPY Data Warehousing on AWS AWS Classroom Training Concurrent write operations Troubleshooting load issues Hands-on lab 5: Loading data with the COPY command Module 7: Writing queries and tuning for performance Amazon Redshift SQL User-Defined Functions (UDFs) Factors that affect query performance The EXPLAIN command and query plans Workload Management (WLM) Hands-on lab 6: Configuring workload management Module 8: Amazon Redshift Spectrum Amazon Redshift Spectrum Configuring data for Amazon Redshift Spectrum Amazon Redshift Spectrum Queries Hands-on lab 7: Using Amazon Redshift Spectrum Module 9: Maintaining clusters Audit logging Performance monitoring Events and notifications Lab 8: Auditing and monitoring clusters Resizing clusters Backing up and restoring clusters Resource tagging and limits and constraints Hands-on lab 9: Backing up, restoring and resizing clusters Module 10: Analyzing and visualizing data Power of visualizations Building dashboards Amazon QuickSight editions and feature

The aim of this programme is to provide current / prospective senior project managers with an in-depth review of the role and importance of project management in the organisation. It focuses on the strategic role of the project manager and aims to draw out the full scope of the role and how it impacts on project performance. The five key objectives of this programme are to enable the participants to: Identify the characteristics and attributes needed by project managers in ensuring the organisation is recognised as an industry leader in delivering successful projects Understand the full scope and impact of the project manager / leader's role in managing projects or programmes to maximise benefit to the business Define the hallmarks and skills required to manage significant business projects or project portfolios Develop an understanding of the skills required and their impact on the project through case study work and syndicate exercises Benefit from an effective forum for exchanging experience and fostering a sense of team spirit and mutual support between senior project managers DAY ONE 1 Introduction (Course sponsor) A vision of future opportunities and challenges The impact of project managers / leaders on future success 2 The determinants of success The contractor's perspective The client's perspective Success and failure: factors that determine the project outcome The impact of the project manager 3 Case study: Project Giotto How successful was this project? What were the primary factors that influenced the outcome? How relevant are these factors to current projects? Feedback and review 4 Project team exercise A practical exercise demonstrating the role of the project manager in managing the interfaces between the client, the project team and suppliers Review - what skills are required to be a 'world class' project manager? 5 The role of the Project Manager The role and skills of the 'world class' project manager The three key dimensions of effective project leadershipManaging influential stakeholdersManaging project performanceManaging the project team Project leadership skills appraisal (individual review) DAY TWO 6 Managing influential stakeholders Project management and the art of leadership Who are the influential stakeholders and how do they affect the project? What we need to do / not do, to build successful working relationships 7 Case study: Understanding the customer What are the likely problems the project manager will encounter? What should the project manager do to ensure an effective partnership? 8 Managing project performance Issues affecting commercial performance Joining up the project life-cycle: getting performance from inception to closure The roles of project manager, line manager and project sponsor 9 Project team exercise: Making the promise / delivering the promise An exercise demonstrating the commercial and team leadership skills needed by the project manager Review - what must the project manager do to optimise project performance? 10 Managing multi-functional project teams The challenges of building effective, multi-functional project teams Co-ordinating work across functional and organisational boundaries Maintaining strategic focus and balancing priorities 11 Making it happen Individual action planning Syndicate discussion 12 Course review and transfer planning (Course sponsor present) What will we do differently? How will we make it happen? Conclusion

Duration 1 Days 6 CPD hours This course is intended for The ProxySG 6.6 Diagnostics and Troubleshooting course is intended for students who wish to gain familiarity with ProxySG troubleshooting techniques and tools. Overview By the completion of this course, you will be able to:Describe the troubleshooting methodology as recommended by Symantec.Diagnose and solve a variety of access and performance issues using tools such as sysinfo files, packet captures, and policy traces. The ProxySG 6.6 Diagnostics and Troubleshooting course provides a structured approach to diagnosing and solving common troubleshooting issues related to ProxySG deployments. The course will emphasize hands-on experience, with extensive opportunities to build competency in the use of the powerful diagnostic tools available on the ProxySG. ProxySG Secure Web Gateway? Overview of Typical Troubleshooting Issues Overview of common issues related to internal resources, external dependencies, SSL interception, and policy efficiency. Introduction to how to use sysinfo files to gain useful troubleshooting information. Troubleshooting Issues Related to External Dependencies Methods for diagnosing and solving authentication issues. Methods for diagnosing and solving DNS issues. Troubleshooting SSL Interception Issues Methods for diagnosing and solving issues related to SSL interception. Troubleshooting Performance Issues Related to Policy Efficiency Methods for optimizing performance through tips for creating more efficient policies.

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