This 10-day bootcamp teaches participants data science skills using Cloudera. It covers topics like Hadoop, Spark, Hive, Pig, and Impala. Participants will learn how to ingest, store, process, analyze and visualize big data. The goal is to help participants understand the role of data scientists and become prepared for jobs in data science or related roles like data analyst or administrator.
I have studied on Big Data analysis and found Hadoop is the best technology and most popular as well for it's distributed data processing approaches. I have gathered all possible information about various Hadoop distributions available in the market and tried to describe most important tools and their functionality in the Hadoop echosystems in this slide show. I have also tried to discuss about connectivity with language R interm of data analysis and visualization perspective. Hope you will be enjoying the whole!
Fundamentals of Big Data, Hadoop project design and case study or Use case
General planning consideration and most necessaries in Hadoop ecosystem and Hadoop projects
This will provide the basis for choosing the right Hadoop implementation, Hadoop technologies integration, adoption and creating an infrastructure.
Building applications using Apache Hadoop with a use-case of WI-FI log analysis has real life example.
Slides for talk presented at Boulder Java User's Group on 9/10/2013, updated and improved for presentation at DOSUG, 3/4/2014
Code is available at https://github.com/jmctee/hadoopTools
I have studied on Big Data analysis and found Hadoop is the best technology and most popular as well for it's distributed data processing approaches. I have gathered all possible information about various Hadoop distributions available in the market and tried to describe most important tools and their functionality in the Hadoop echosystems in this slide show. I have also tried to discuss about connectivity with language R interm of data analysis and visualization perspective. Hope you will be enjoying the whole!
Fundamentals of Big Data, Hadoop project design and case study or Use case
General planning consideration and most necessaries in Hadoop ecosystem and Hadoop projects
This will provide the basis for choosing the right Hadoop implementation, Hadoop technologies integration, adoption and creating an infrastructure.
Building applications using Apache Hadoop with a use-case of WI-FI log analysis has real life example.
Slides for talk presented at Boulder Java User's Group on 9/10/2013, updated and improved for presentation at DOSUG, 3/4/2014
Code is available at https://github.com/jmctee/hadoopTools
Presentation regarding big data. The presentation also contains basics regarding Hadoop and Hadoop components along with their architecture. Contents of the PPT are
1. Understanding Big Data
2. Understanding Hadoop & It’s Components
3. Components of Hadoop Ecosystem
4. Data Storage Component of Hadoop
5. Data Processing Component of Hadoop
6. Data Access Component of Hadoop
7. Data Management Component of Hadoop
8.Hadoop Security Management Tool: Knox ,Ranger
What are Hadoop Components? Hadoop Ecosystem and Architecture | EdurekaEdureka!
YouTube Link: https://youtu.be/ll_O9JsjwT4
** Big Data Hadoop Certification Training - https://www.edureka.co/big-data-hadoop-training-certification **
This Edureka PPT on "Hadoop components" will provide you with detailed knowledge about the top Hadoop Components and it will help you understand the different categories of Hadoop Components. This PPT covers the following topics:
What is Hadoop?
Core Components of Hadoop
Hadoop Architecture
Hadoop EcoSystem
Hadoop Components in Data Storage
General Purpose Execution Engines
Hadoop Components in Database Management
Hadoop Components in Data Abstraction
Hadoop Components in Real-time Data Streaming
Hadoop Components in Graph Processing
Hadoop Components in Machine Learning
Hadoop Cluster Management tools
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
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Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
Presentation from the Rittman Mead BI Forum 2013 on ODI11g's Hadoop connectivity. Provides a background to Hadoop, HDFS and Hive, and talks about how ODI11g, and OBIEE 11.1.1.7+, uses Hive to connect to "big data" sources.
Hadoop has showed itself as a great tool in resolving problems with different data aspects as Data Velocity, Variety and Volume, that are causing troubles to relational database storage. In this presentation you'll learn what problems with data are occurring nowdays and how Hadoop can solve them . You'll learn about Hadop basic components and principles that make Hadoop such great tool.
BDM9 - Comparison of Oracle RDBMS and Cloudera Impala for a hospital use caseDavid Lauzon
High-level use case description of one department of a hospital, and comparisons of two solutions : 1) Big data solution using Cloudera Impala; and 2) Traditional RDBMS solution using Oracle DB.
Coordinating the Many Tools of Big Data - Apache HCatalog, Apache Pig and Apa...Big Data Spain
Session presented at Big Data Spain 2012 Conference
16th Nov 2012
ETSI Telecomunicacion UPM Madrid
www.bigdataspain.org
More info: http://www.bigdataspain.org/es-2012/conference/coordinating-many-tools-of-big-data/alan-gates
Big Data and Hadoop training course is designed to provide knowledge and skills to become a successful Hadoop Developer. In-depth knowledge of concepts such as Hadoop Distributed File System, Setting up the Hadoop Cluster, Map-Reduce,PIG, HIVE, HBase, Zookeeper, SQOOP etc. will be covered in the course.
Big Data raises challenges about how to process such vast pool of raw data and how to aggregate value to our lives. For addressing these demands an ecosystem of tools named Hadoop was conceived.
A Maturing Role of Workflows in the Presence of Heterogenous Computing Archit...Ilkay Altintas, Ph.D.
cientific workflows are used by many scientific communities to capture, automate and standardize computational and data practices in science. Workflow-based automation is often achieved through a craft that combines people, process, computational and Big Data platforms, application-specific purpose and programmability, leading to provenance-aware archival and publications of the results. This talk summarizes varying and changing requirements for distributed workflows influenced by Big Data and heterogeneous computing architectures and present a methodology for workflow-driven science based on these maturing requirements.
Presentation regarding big data. The presentation also contains basics regarding Hadoop and Hadoop components along with their architecture. Contents of the PPT are
1. Understanding Big Data
2. Understanding Hadoop & It’s Components
3. Components of Hadoop Ecosystem
4. Data Storage Component of Hadoop
5. Data Processing Component of Hadoop
6. Data Access Component of Hadoop
7. Data Management Component of Hadoop
8.Hadoop Security Management Tool: Knox ,Ranger
What are Hadoop Components? Hadoop Ecosystem and Architecture | EdurekaEdureka!
YouTube Link: https://youtu.be/ll_O9JsjwT4
** Big Data Hadoop Certification Training - https://www.edureka.co/big-data-hadoop-training-certification **
This Edureka PPT on "Hadoop components" will provide you with detailed knowledge about the top Hadoop Components and it will help you understand the different categories of Hadoop Components. This PPT covers the following topics:
What is Hadoop?
Core Components of Hadoop
Hadoop Architecture
Hadoop EcoSystem
Hadoop Components in Data Storage
General Purpose Execution Engines
Hadoop Components in Database Management
Hadoop Components in Data Abstraction
Hadoop Components in Real-time Data Streaming
Hadoop Components in Graph Processing
Hadoop Components in Machine Learning
Hadoop Cluster Management tools
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
Presentation from the Rittman Mead BI Forum 2013 on ODI11g's Hadoop connectivity. Provides a background to Hadoop, HDFS and Hive, and talks about how ODI11g, and OBIEE 11.1.1.7+, uses Hive to connect to "big data" sources.
Hadoop has showed itself as a great tool in resolving problems with different data aspects as Data Velocity, Variety and Volume, that are causing troubles to relational database storage. In this presentation you'll learn what problems with data are occurring nowdays and how Hadoop can solve them . You'll learn about Hadop basic components and principles that make Hadoop such great tool.
BDM9 - Comparison of Oracle RDBMS and Cloudera Impala for a hospital use caseDavid Lauzon
High-level use case description of one department of a hospital, and comparisons of two solutions : 1) Big data solution using Cloudera Impala; and 2) Traditional RDBMS solution using Oracle DB.
Coordinating the Many Tools of Big Data - Apache HCatalog, Apache Pig and Apa...Big Data Spain
Session presented at Big Data Spain 2012 Conference
16th Nov 2012
ETSI Telecomunicacion UPM Madrid
www.bigdataspain.org
More info: http://www.bigdataspain.org/es-2012/conference/coordinating-many-tools-of-big-data/alan-gates
Big Data and Hadoop training course is designed to provide knowledge and skills to become a successful Hadoop Developer. In-depth knowledge of concepts such as Hadoop Distributed File System, Setting up the Hadoop Cluster, Map-Reduce,PIG, HIVE, HBase, Zookeeper, SQOOP etc. will be covered in the course.
Big Data raises challenges about how to process such vast pool of raw data and how to aggregate value to our lives. For addressing these demands an ecosystem of tools named Hadoop was conceived.
A Maturing Role of Workflows in the Presence of Heterogenous Computing Archit...Ilkay Altintas, Ph.D.
cientific workflows are used by many scientific communities to capture, automate and standardize computational and data practices in science. Workflow-based automation is often achieved through a craft that combines people, process, computational and Big Data platforms, application-specific purpose and programmability, leading to provenance-aware archival and publications of the results. This talk summarizes varying and changing requirements for distributed workflows influenced by Big Data and heterogeneous computing architectures and present a methodology for workflow-driven science based on these maturing requirements.
Rainer Schmidt, AIT Austrian Institute of Technology, presented Scalable Preservation Workflows from SCAPE at the 5-days ‘Digital Preservation Advanced Practitioner Training’ event (http://bit.ly/1fYCvMO), hosted by DPC, in Glasgow on 15-19 July 2013.
The presentation gives an introduction to the SCAPE Platform, it presents scenarios from SCAPE Testbeds and it finally describes how to create scalable workflows and execute them on the SCAPE Platform.
HPCC Systems Engineering Summit: Community Use Case: Because Who Has Time for...HPCC Systems
Presenter: John Andleman, Staff Database Engineer, Citrix
In this session, John will share some interesting use cases leveraging the HPCC Systems platform, including those beyond traditional big data uses. John will also share his roadmap of HPCC projects being planned for the next few months and why he feels HPCC Systems is a more suitable solution than Hadoop based on experiences and lessons learned.
NOTE: The video of this presentation is the 3rd one shown in the accompanying YouTube link.
Data Science at Scale Using Apache Spark and Apache HadoopCloudera, Inc.
Learn about the skills and tools a data scientist needs and how to start training to be one.
There's so much noise about what a data scientist is or isn't that it can be challenging to identify the skills needed to start training a team or becoming one yourself. What exactly is a data scientist and where do you start?
Cloudera's Director of Data Science, Sean Owen, will start by walking through the different skills data scientist should have and why businesses need them. Afterwards, Tom Wheeler, Cloudera's Principal Curriculum Developer, will introduce the latest data science course developed by Cloudera University designed to help people take their first steps to becoming a data scientist.
Clarify how System Integrator / Vendor Must know what is Big Data and How To Implement it in Developing Countries such as Indonesia.
This is very lightweight introduction, some animation don't work in this presentation, suitable viewed as pptx.
Big Data Analytics involves examining or processing large amounts of data (unstructured and structured) to create useful information which can help organizations to critically fi ne tune their business plans and increase pro fitability.
Apache HadoopTM is the most effi cient data platform that simpli fies and allows for
the distributed processing of large data sets. The latest revolution in big data
technology, Hadoop forms the core of an open source software framework, supporting the processing of large data across clustered systems. Using Hadoop, deep analytics that cannot be handled by a database engine can be run e ffectively.
Deep Learning on Apache® Spark™: Workflows and Best PracticesDatabricks
The combination of Deep Learning with Apache Spark has the potential for tremendous impact in many sectors of the industry. This webinar, based on the experience gained in assisting customers with the Databricks Virtual Analytics Platform, will present some best practices for building deep learning pipelines with Spark.
Rather than comparing deep learning systems or specific optimizations, this webinar will focus on issues that are common to deep learning frameworks when running on a Spark cluster, including:
* optimizing cluster setup;
* configuring the cluster;
* ingesting data; and
* monitoring long-running jobs.
We will demonstrate the techniques we cover using Google’s popular TensorFlow library. More specifically, we will cover typical issues users encounter when integrating deep learning libraries with Spark clusters.
Clusters can be configured to avoid task conflicts on GPUs and to allow using multiple GPUs per worker. Setting up pipelines for efficient data ingest improves job throughput, and monitoring facilitates both the work of configuration and the stability of deep learning jobs.
Deep Learning on Apache® Spark™: Workflows and Best PracticesJen Aman
The combination of Deep Learning with Apache Spark has the potential for tremendous impact in many sectors of the industry. This webinar, based on the experience gained in assisting customers with the Databricks Virtual Analytics Platform, will present some best practices for building deep learning pipelines with Spark.
Rather than comparing deep learning systems or specific optimizations, this webinar will focus on issues that are common to deep learning frameworks when running on a Spark cluster, including:
* optimizing cluster setup;
* configuring the cluster;
* ingesting data; and
* monitoring long-running jobs.
We will demonstrate the techniques we cover using Google’s popular TensorFlow library. More specifically, we will cover typical issues users encounter when integrating deep learning libraries with Spark clusters.
Clusters can be configured to avoid task conflicts on GPUs and to allow using multiple GPUs per worker. Setting up pipelines for efficient data ingest improves job throughput, and monitoring facilitates both the work of configuration and the stability of deep learning jobs.
Deep Learning on Apache® Spark™ : Workflows and Best PracticesJen Aman
The combination of Deep Learning with Apache Spark has the potential for tremendous impact in many sectors of the industry. This webinar, based on the experience gained in assisting customers with the Databricks Virtual Analytics Platform, will present some best practices for building deep learning pipelines with Spark.
Rather than comparing deep learning systems or specific optimizations, this webinar will focus on issues that are common to deep learning frameworks when running on a Spark cluster, including:
* optimizing cluster setup;
* configuring the cluster;
* ingesting data; and
* monitoring long-running jobs.
We will demonstrate the techniques we cover using Google’s popular TensorFlow library. More specifically, we will cover typical issues users encounter when integrating deep learning libraries with Spark clusters.
Clusters can be configured to avoid task conflicts on GPUs and to allow using multiple GPUs per worker. Setting up pipelines for efficient data ingest improves job throughput, and monitoring facilitates both the work of configuration and the stability of deep learning jobs.
PyData: The Next Generation | Data Day Texas 2015Cloudera, Inc.
Speaker: Wes McKinney
Data Day Texas 2015
It's 2015 and the data system landscape is continuing to evolve at a rapid pace. This talk will give an overview of where Python and the "PyData" stack of software stands right now, where it's headed, and where more industry and community energy is needed.
Using Oracle Big Data Discovey as a Data Scientist's ToolkitMark Rittman
As delivered at Trivadis Tech Event 2016 - how Big Data Discovery along with Python and pySpark was used to build predictive analytics models against wearables and smart home data
Strata 2015 presentation from Oracle for Big Data - we are announcing several new big data products including GoldenGate for Big Data, Big Data Discovery, Oracle Big Data SQL and Oracle NoSQL
Ross King, Project Director of SCAPE, gave a short presentation of the EU funded project SCAPE, including descriptions of tools for planning and monitoring digital preservation, scalable computation and repositories, SCAPE Testbeds and where to learn more.
The presentation was given at the workshop ‘Preservation at Scale’ http://bit.ly/17ppAln in connection with the iPres2013 conference in Lissabon, Portugal, in September 2013.
Similar to Bootcamp Data Science using Cloudera (20)
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
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UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
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In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
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Essentials of Automations: Optimizing FME Workflows with Parameters
Bootcamp Data Science using Cloudera
1. Av João Paulo II, lote 5, loja 3, 1950-152 Lisboa
www.openup.pt 1
Boot Camp - Data Science using Cloudera
Revisto em Janeiro, 2018
Bootcamp duration
● 10 days
Description
Data scientists build information platforms to ask and answer previously
unimaginable questions. Learn how data science helps organizations reduce costs,
increase efficiency, improve product delivery, improve customers and users
experience, and identify new opportunities. Our bootcamp helps participants
understand what data scientists do and the problems they solve, and to become a
data scientist. Through in-class simulations, participants apply data science methods
to real-world challenges in different scenarios and, ultimately, prepare for data
scientist roles in the field.
This bootcamp is oriented to the different roles on the data science landscape,
Administrators, Developers and Data Analysts.
This bootcamp delivers the key concepts and expertise participants need to ingest
and process data on a Hadoop cluster using the most up-to-date tools and
techniques. Employing Hadoop ecosystem projects such as Spark, Hive, Flume,
Sqoop, and Impala, this training course is the best preparation for the real-world
challenges faced by Hadoop developers. Participants learn to identify which tool is
the right one to use in a given situation, and will gain hands-on experience in
developing using those tools.
Participants will also learn Apache Pig and Hive and Cloudera Impala will teach you
to apply traditional data analytics and business intelligence skills to big data. Cloudera
presents the tools data professionals need to access, manipulate, transform, and
analyze complex data sets using SQL and familiar scripting languages.
Data visualisation is vital in bridging the gap between data and decisions. Discover
the methods, tools and processes involved. Data visualisation is an important visual
method for effective communication and analysing large datasets. Through data
visualisations we are able to draw conclusions from data that are sometimes not
immediately obvious and interact with the data in an entirely different way.
This course will provide you with an informative introduction to the methods, tools
and processes involved in visualising big data
2. Av João Paulo II, lote 5, loja 3, 1950-152 Lisboa
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Audience
• This course is suitable for system administrators, developers, data analysts,
and statisticians;
• In general to all interested big data and data science;
Prerequisites
● Knowledge on operating systems like Unix/Linux are preferable but non
essential;
● Knowledge in a programming language is preferable but non essential.
Objectives
After conclusions participants will learn:
● How to identify potential business use cases where data science can provide
impactful results;
● How to obtain, clean and combine disparate data sources to create a
coherent picture for analysis;
● What statistical methods to leverage for data exploration that will provide
critical insight into your data;
● Where and when to leverage Hadoop streaming and Apache Spark for data
science pipelines;
● What machine learning technique to use for a particular data science
project;
● How to implement and manage recommenders using Spark’s MLlib, and how
to set up and evaluate data experiments;
● What are the pitfalls of deploying new analytics projects to production, at
scale;
3. Av João Paulo II, lote 5, loja 3, 1950-152 Lisboa
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● How data is distributed, stored, and processed in a Hadoop cluster;
● How to use Sqoop and Flume to ingest data;
● How to process distributed data with Apache Spark;
● How to model structured data as tables in Impala and Hive;
● How to choose the best data storage format for different data usage
patterns;
● Best practices for data storage;
● The features that Pig, Hive, and Impala offer for data acquisition, storage,
and analysis;
● The fundamentals of Apache Hadoop and data ETL (extract, transform,
load), ingestion, and processing with Hadoop tools
● How Pig, Hive, and Impala improve productivity for typical analysis tasks
● Joining diverse datasets to gain valuable business insight
● Performing real-time, complex queries on datasets
● Use big data and data science visualization tools
4. Av João Paulo II, lote 5, loja 3, 1950-152 Lisboa
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Course Outline:
Introduction
• About This Course
• About Cloudera
• Course Logistics
• Introductions
Data Science Overview
• What Is Data Science?
• The Growing Need for Data Science
• The Role of a Data Scientist
Introduction to Hadoopand the Hadoop Ecosystem
• Problems with TraditionalLarge-scale Systems
• Hadoop!
• The Hadoop EcoSystem
Hadoop Architecture and HDFS
• Distributed Processing on a Cluster
• Storage: HDFS Architecture
• Storage: Using HDFS
• Resource Management: YARN Architecture
• Resource Management: Working with YARN
Importing Relational Data with Apache Sqoop
• Sqoop Overview
• Basic Imports and Exports
• Limiting Results
• Improving Sqoop’s Performance
• Sqoop 2
Introduction to Impala and Hive
• Introduction to Impala and Hive
• Why Use Impala and Hive?
• Comparing Hive to Traditional Databases
• Hive Use Cases
Modeling and Managing Data with Impala and Hive
• Data Storage Overview
• Creating Databases and Tables
• Loading Data into Tables
• HCatalog
5. Av João Paulo II, lote 5, loja 3, 1950-152 Lisboa
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• Impala Metadata Caching
Data Formats
• Selecting a File Format
• Hadoop Tool Support for File Formats
• Avro Schemas
• Using Avro with Hive and Sqoop
• Avro Schema Evolution
• Compression
Data Partitioning
• Partitioning Overview
• Partitioning in Impala and Hive
Capturing Data with Apache Flume
• What is Apache Flume?
• Basic Flume Architecture
• Flume Sources
• Flume Sinks
• Flume Channels
• Flume Configuration
Spark Basics
• What is Apache Spark?
• Using the Spark Shell
• RDDs (Resilient Distributed Datasets)
• Functional Programming in Spark
Working with RDDs in Spark
• A Closer Look at RDDs
• Key-Value Pair RDDs
• MapReduce
• Other Pair RDD Operations
Writing and Deploying Spark Applications
• Spark Applications vs. Spark Shell
• Creating the SparkContext
• Building a Spark Application (Scala and Java)
• Running a Spark Application
• The Spark Application Web UI
• Configuring Spark Properties
• Logging
Parallel Programming with Spark
• Review: Spark on a Cluster
• RDD Partitions
• Partitioning of File-based RDDs
• HDFS and Data Locality
• Executing Parallel Operations
• Stages and Tasks
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Spark Caching and Persistence
• RDD Lineage
• Caching Overview
• Distributed Persistence
Common Patterns in Spark Data Processing
• Common Spark Use Cases
• Iterative Algorithms in Spark
• Graph Processing and Analysis
• Machine Learning
• Example: k-means
Preview: Spark SQL
• Spark SQL and the SQL Context
• Creating DataFrames
•Transforming and Querying DataFrames
• Saving DataFrames
• Comparing Spark SQL with Impala
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 Troubleshooting and Optimization
• Troubleshooting Pig
7. Av João Paulo II, lote 5, loja 3, 1950-152 Lisboa
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• 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 and Impala
• What Is Hive?
• What Is Impala?
• Schema and Data Storage
• Comparing Hive to Traditional Databases
• Hive Use Cases
Querying with Hive and 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 and Performance
• Partitioning Tables
• Choosing a File Format
• Managing Metadata
• Controlling Access to Data
Relational Data Analysis with Hive and 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
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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?
Visualizations Tools
• Try different data visualization tools
• Discover the methods, tools and processes involved.
• Choosing the Best Visualization Tool for the Job
Conclusion
