120 Apache courses

This is a comprehensive and practical Apache Spark course. In this course, you will learn and master the art of framing data analysis problems as Spark problems through 20+ hands-on examples, and then scale them up to run on cloud computing services. Explore Spark 3, IntelliJ, Structured Streaming, and a stronger focus on the DataSet API.

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 1 Days 6 CPD hours This course is intended for The primary audience for this course is data professionals who are familiar with data modeling, extraction, and analytics. It is designed for professionals who are interested in gaining knowledge about Lakehouse architecture, the Microsoft Fabric platform, and how to enable end-to-end analytics using these technologies. Job role: Data Analyst, Data Engineer, Data Scientist Overview Describe end-to-end analytics in Microsoft Fabric Describe core features and capabilities of lakehouses in Microsoft Fabric Create a lakehouse Ingest data into files and tables in a lakehouse Query lakehouse tables with SQL Configure Spark in a Microsoft Fabric workspace Identify suitable scenarios for Spark notebooks and Spark jobs Use Spark dataframes to analyze and transform data Use Spark SQL to query data in tables and views Visualize data in a Spark notebook Understand Delta Lake and delta tables in Microsoft Fabric Create and manage delta tables using Spark Use Spark to query and transform data in delta tables Use delta tables with Spark structured streaming Describe Dataflow (Gen2) capabilities in Microsoft Fabric Create Dataflow (Gen2) solutions to ingest and transform data Include a Dataflow (Gen2) in a pipeline This course is designed to build your foundational skills in data engineering on Microsoft Fabric, focusing on the Lakehouse concept. This course will explore the powerful capabilities of Apache Spark for distributed data processing and the essential techniques for efficient data management, versioning, and reliability by working with Delta Lake tables. This course will also explore data ingestion and orchestration using Dataflows Gen2 and Data Factory pipelines. This course includes a combination of lectures and hands-on exercises that will prepare you to work with lakehouses in Microsoft Fabric. Introduction to end-to-end analytics using Microsoft Fabric Explore end-to-end analytics with Microsoft Fabric Data teams and Microsoft Fabric Enable and use Microsoft Fabric Knowledge Check Get started with lakehouses in Microsoft Fabric Explore the Microsoft Fabric Lakehouse Work with Microsoft Fabric Lakehouses Exercise - Create and ingest data with a Microsoft Fabric Lakehouse Use Apache Spark in Microsoft Fabric Prepare to use Apache Spark Run Spark code Work with data in a Spark dataframe Work with data using Spark SQL Visualize data in a Spark notebook Exercise - Analyze data with Apache Spark Work with Delta Lake Tables in Microsoft Fabric Understand Delta Lake Create delta tables Work with delta tables in Spark Use delta tables with streaming data Exercise - Use delta tables in Apache Spark Ingest Data with DataFlows Gen2 in Microsoft Fabric Understand Dataflows (Gen2) in Microsoft Fabric Explore Dataflows (Gen2) in Microsoft Fabric Integrate Dataflows (Gen2) and Pipelines in Microsoft Fabric Exercise - Create and use a Dataflow (Gen2) in Microsoft Fabric

UNIX networking training course description A course covering the complete range of standard UNIX networking products from the basic TCP/IP configuration through DNS, NIS, NFS and Samba. Hands-on exercises follow most theory sessions. What will you learn Install and configure fundamental network services. Describe TCP/IP, Apache, DNS, NIS, NIS+, NFS, Samba and sendmail. Configure and administrate TCP/IP. Install and administrate a DNS server. Configure and administrate a NIS+ network. Administrate NFS. Setup a sendmail server. UNIX networking training course details Who will benefit: System Administrators. Network Administrators. Prerequisites: Linux engineer certification 1 (LPIC-2) Duration 5 days UNIX networking training course contents Organizing Email Services The UNIX Mail System, Mail Transfer Agent, Mail Delivery Agent, Mail User Agent, Email Protocols, SMTP, POP, IMAP, Using Email Servers, Sendmail, Postfix, Local Email Delivery, Procmail Basics, Sieve, Remote Email Delivery, Courier, Dovecot. DNS DNS and BIND, Configuring a DNS Server, Starting, Stopping, and Reloading BIND, Configuring BIND Logging, Creating and Maintaining DNS Zones, BIND Zone Files, Managing BIND Zones, Securing a DNS Server, ailing BIND, DNSSEC, TSIG, Employing DANE. Offering Web Services Web Servers, HTTP, The Apache Web Server, Installing and configuring Apache, Hosting Dynamic Web Applications, Secure Web Servers, Proxy Servers, Installing and configuring Squid, Configuring Clients, Nginx Server, Installing Nginx, Configuring Nginx. Sharing Files Samba, Configuring Samba, Troubleshooting Samba, NFS, Configuring NFS, Securing NFS, Troubleshooting NFS, FTP Servers, Configuring vsftpd, Configuring Pure-FTPd. Managing Network Clients Assigning Network Addresses, DHCP, UNIX DHCP Software, Installing and configuring a DHCP Server and clients, Authentication Service, PAM Basics, Configuring PAM, PAM Application Files, Network Directories, LDAP Basics, OpenLDAP Server, LDAP Clients. Setting Up System Security Server Network Security, Port Scanning, Intrusion Detection Systems, External Network Security, iptables, Routing in UNIX, Connecting Securely to a Server, OpenSSH, OpenVPN, Security Resources, US-CERT, SANS Institute, Bugtraq.

In this course, we will process massive streams of real-time data using Spark Streaming and create Spark applications using the Scala programming language (v2.12). We will also get our hands-on with some real live Twitter data, simulated streams of Apache access logs, and even data used to train machine learning models.

Learn how to program using Apache Groovy.

A complete course on Sqoop, Flume, and Hive: Ideal for achieving CCA175 and Hortonworks Spark Certification

In this course, you will learn to create Kafka Streams microservices using the Spring cloud framework. This is an example-driven course, and you will learn to use Confluent Kafka distribution for all the examples. By the end of this course, you will learn to create Kafka Streams microservices using different types of serializations, Confluent schema registry, and creating stateless and stateful event processing applications.

Linux network administration 2 course description LPIC-2 is the second certification in LPI's multi level professional certification program. This course teaches the skills necessary to pass the LPI 202 exam; the second of two LPIC-2 exams. Specifically, the course covers the administration of Linux systems in small to medium sized mixed networks. What will you learn Install and configure fundamental network services. Linux network administration 2 course details Who will benefit: Linux administrators. Prerequisites: Linux engineer certification 1 (LPIC-2) Duration 5 days Linux network administration 2 course contents Part II The LPI 202 Exam Organizing Email Services The Linux Mail System, Mail Transfer Agent, Mail Delivery Agent, Mail User Agent, Email Protocols, SMTP, POP, IMAP, Using Email Servers, Sendmail, Postfix, Local Email Delivery, Procmail Basics, Sieve, Remote Email Delivery, Courier, Dovecot. DNS DNS and BIND, Configuring a DNS Server, Starting, Stopping, and Reloading BIND, Configuring BIND Logging, Creating and Maintaining DNS Zones, BIND Zone Files, Managing BIND Zones, Securing a DNS Server, ailing BIND, DNSSEC, TSIG, Employing DANE. Offering Web Services Web Servers, HTTP, The Apache Web Server, Installing and configuring Apache, Hosting Dynamic Web Applications, Secure Web Servers, Proxy Servers, Installing and configuring Squid, Configuring Clients, Nginx Server, Installing Nginx, Configuring Nginx. Sharing Files Samba, Configuring Samba, Troubleshooting Samba, NFS, Configuring NFS, Securing NFS, Troubleshooting NFS, FTP Servers, Configuring vsftpd, Configuring Pure-FTPd. Managing Network Clients Assigning Network Addresses, DHCP, Linux DHCP Software, Installing and configuring a DHCP Server and clients, Authentication Service, PAM Basics, Configuring PAM, PAM Application Files, Network Directories, LDAP Basics, OpenLDAP Server, LDAP Clients. Setting Up System Security Server Network Security, Port Scanning, Intrusion Detection Systems, External Network Security, iptables, Routing in Linux, Connecting Securely to a Server, OpenSSH, OpenVPN, Security Resources, US-CERT, SANS Institute, Bugtraq.

This course brings together all the important topics related to modern distributed applications and systems in one place. Explore the common challenges that appear while designing and implementing large-scale distributed systems, and how big-tech companies solve those problems. Throughout the course, we are going to build a distributed URL shortening service.