This document provides a high-level overview of the Apache Hadoop ecosystem and some of its core components like HDFS, MapReduce, and Apache Hive. It also includes a brief and simplified example of how to perform basic operations with SQL, such as selecting rows and fields, filtering results, and joining tables.
Kevin Risden presented on Solr JDBC at Apache Lucene/Solr Committer. He provided an overview of Solr JDBC, including its background using JDBC to access tabular data through SQL, and why it is useful to access Solr data through SQL and existing JDBC tools. He demonstrated Solr JDBC through various programming languages and GUI tools, accessing a utility rates data set to answer analytic questions. Future improvements to Solr JDBC were discussed, such as replacing Presto with Calcite for SQL parsing and joining data through streaming expressions.
Incredible ODI tips to work with Hyperion tools that you ever wanted to knowRodrigo Radtke de Souza
ODI is an incredible and flexible development tool that goes beyond simple data integration. But most of its development power comes from outside-the-box ideas.
* Did you ever want to dynamically run any number of “OS” commands using a single ODI component?
* Did you ever want to have only one data store and loop different sources without the need of different ODI contexts?
* Did you ever want to have only one interface and loop any number of ODI objects with a lot of control?
* Did you ever need to have a “third command tab” in your procedures or KMs to improve ODI powers?
* Do you still use an old version of ODI and miss a way to know the values of the variables in a scenario execution?
* Did you know ODI has four “substitution tags”? And do you know how useful they are?
* Do you use “dynamic variables” and know how powerful they can be?
* Do you know how to have control over you ODI priority jobs automatically (stop, start, and restart scenarios)?
The Hop project entered Apache Software Foundation as an Incubator project in 2020, and Julian Hyde, one of their mentors, gave this presentation to educate the initial committers on the Apache Way and what to expect during the Incubation process.
The talk was given by Julian Hyde on October 1st, 2020, with the original title "Apache Incubation - What's it all about?"
This Edureka Apache Spark Interview Questions and Answers tutorial helps you in understanding how to tackle questions in a Spark interview and also gives you an idea of the questions that can be asked in a Spark Interview. The Spark interview questions cover a wide range of questions from various Spark components. Below are the topics covered in this tutorial:
1. Basic Questions
2. Spark Core Questions
3. Spark Streaming Questions
4. Spark GraphX Questions
5. Spark MLlib Questions
6. Spark SQL Questions
A glimpse into the life of two Cloudera employees and the many hats they wear.
As presented to Computer Science House (CSH) at Rochester Institute of Technology (RIT) on February 25th 2014.
Cloudera Impala provides a fast, ad hoc query capability to Apache Hadoop, complementing traditional MapReduce batch processing. Learn the design choices and architecture behind Impala, and how to use near-ubiquitous SQL to explore your own data at scale.
As presented to Portland Big Data User Group on July 23rd 2014.
http://www.meetup.com/Hadoop-Portland/events/194930422/
Cloudera Data Science Workbench: sparklyr, implyr, and More - dplyr Interfac...Cloudera, Inc.
You like to use R, and you need to use big data. dplyr, one of the most popular packages for R, makes it easy to query large data sets in scalable processing engines like Apache Spark and Apache Impala.
But there can be pitfalls: dplyr works differently with different data sources—and those differences can bite you if you don’t know what you’re doing.
Ian Cook is a data scientist, an R contributor, and a curriculum developer at Cloudera University. In this webinar, Ian will show you exactly what you need to know about sparklyr (from RStudio) and the package implyr (from Cloudera). He will show you how to write dplyr code that works across these different interfaces. And, he will solve mysteries:
Do I need to know SQL to use dplyr?
When is a “tbl” not a “tibble”?
Why is 1 not always equal to 1?
When should you collect(), collapse(), and compute()?
How can you use dplyr to combine data stored in different systems?
3 things to learn:
Do I need to know SQL to use dplyr?
When should you collect(), collapse(), and compute()?
How can you use dplyr to combine data stored in different systems?
"Analyzing Twitter Data with Hadoop - Live Demo", presented at Oracle Open World 2014. The repository for the slides is in https://github.com/cloudera/cdh-twitter-example
Kevin Risden presented on Solr JDBC at Apache Lucene/Solr Committer. He provided an overview of Solr JDBC, including its background using JDBC to access tabular data through SQL, and why it is useful to access Solr data through SQL and existing JDBC tools. He demonstrated Solr JDBC through various programming languages and GUI tools, accessing a utility rates data set to answer analytic questions. Future improvements to Solr JDBC were discussed, such as replacing Presto with Calcite for SQL parsing and joining data through streaming expressions.
Incredible ODI tips to work with Hyperion tools that you ever wanted to knowRodrigo Radtke de Souza
ODI is an incredible and flexible development tool that goes beyond simple data integration. But most of its development power comes from outside-the-box ideas.
* Did you ever want to dynamically run any number of “OS” commands using a single ODI component?
* Did you ever want to have only one data store and loop different sources without the need of different ODI contexts?
* Did you ever want to have only one interface and loop any number of ODI objects with a lot of control?
* Did you ever need to have a “third command tab” in your procedures or KMs to improve ODI powers?
* Do you still use an old version of ODI and miss a way to know the values of the variables in a scenario execution?
* Did you know ODI has four “substitution tags”? And do you know how useful they are?
* Do you use “dynamic variables” and know how powerful they can be?
* Do you know how to have control over you ODI priority jobs automatically (stop, start, and restart scenarios)?
The Hop project entered Apache Software Foundation as an Incubator project in 2020, and Julian Hyde, one of their mentors, gave this presentation to educate the initial committers on the Apache Way and what to expect during the Incubation process.
The talk was given by Julian Hyde on October 1st, 2020, with the original title "Apache Incubation - What's it all about?"
This Edureka Apache Spark Interview Questions and Answers tutorial helps you in understanding how to tackle questions in a Spark interview and also gives you an idea of the questions that can be asked in a Spark Interview. The Spark interview questions cover a wide range of questions from various Spark components. Below are the topics covered in this tutorial:
1. Basic Questions
2. Spark Core Questions
3. Spark Streaming Questions
4. Spark GraphX Questions
5. Spark MLlib Questions
6. Spark SQL Questions
A glimpse into the life of two Cloudera employees and the many hats they wear.
As presented to Computer Science House (CSH) at Rochester Institute of Technology (RIT) on February 25th 2014.
Cloudera Impala provides a fast, ad hoc query capability to Apache Hadoop, complementing traditional MapReduce batch processing. Learn the design choices and architecture behind Impala, and how to use near-ubiquitous SQL to explore your own data at scale.
As presented to Portland Big Data User Group on July 23rd 2014.
http://www.meetup.com/Hadoop-Portland/events/194930422/
Cloudera Data Science Workbench: sparklyr, implyr, and More - dplyr Interfac...Cloudera, Inc.
You like to use R, and you need to use big data. dplyr, one of the most popular packages for R, makes it easy to query large data sets in scalable processing engines like Apache Spark and Apache Impala.
But there can be pitfalls: dplyr works differently with different data sources—and those differences can bite you if you don’t know what you’re doing.
Ian Cook is a data scientist, an R contributor, and a curriculum developer at Cloudera University. In this webinar, Ian will show you exactly what you need to know about sparklyr (from RStudio) and the package implyr (from Cloudera). He will show you how to write dplyr code that works across these different interfaces. And, he will solve mysteries:
Do I need to know SQL to use dplyr?
When is a “tbl” not a “tibble”?
Why is 1 not always equal to 1?
When should you collect(), collapse(), and compute()?
How can you use dplyr to combine data stored in different systems?
3 things to learn:
Do I need to know SQL to use dplyr?
When should you collect(), collapse(), and compute()?
How can you use dplyr to combine data stored in different systems?
"Analyzing Twitter Data with Hadoop - Live Demo", presented at Oracle Open World 2014. The repository for the slides is in https://github.com/cloudera/cdh-twitter-example
The popular R package dplyr provides a consistent grammar for data manipulation that can abstract over diverse data sources. Ian Cook shows how you can use dplyr to query large-scale data using different processing engines including Spark and Impala. He demonstrates the R package sparklyr (from RStudio) and the new R package implyr (from Cloudera) and shares tips for making dplyr code portable.
This document discusses using databases and PHP with web applications. It begins by explaining how databases organize and store data that can then be retrieved and used. It then introduces AMP as a common web development architecture using Apache, MySQL, and PHP. Several examples are provided of how PHP connects to and interacts with a MySQL database, including establishing a connection, selecting a database, and using SQL queries to insert, select, update and delete data from database tables.
The document discusses analyzing Twitter data with Hadoop. It describes using Flume to pull Twitter data from the Twitter API and store it in HDFS as JSON files. Hive is then used to query the JSON data with SQL, taking advantage of the JSONSerDe to parse the JSON. Impala provides faster interactive queries of the same data compared to Hive running MapReduce jobs. The document provides examples of the Flume, Hive, and Impala configurations and queries used in this Twitter analytics workflow.
This document discusses building dynamic web sites that retrieve content from a database rather than static files. It covers the basics of relational databases, how they can be used to store and retrieve flexible, customized content for websites. Specific topics covered include connecting to a MySQL database from PHP, performing queries to select, insert and update data, and using WHERE and ORDER BY clauses to search and sort records. The document provides examples of common SQL queries and functions for working with database records in PHP scripts.
MySQL Baics - Texas Linxufest beginners tutorial May 31st, 2019Dave Stokes
MySQL is a relational database management system. The document provides an introduction to MySQL, including:
- MySQL is available in both community and enterprise editions. The community edition is free to use while the enterprise edition starts at $5K/4 core CPU before discounts.
- Data in MySQL is organized into tables within schemas (or databases). Tables contain rows of data organized into columns.
- Structured Query Language (SQL) is used to interact with MySQL databases. Common SQL commands include SELECT to retrieve data, INSERT to add data, UPDATE to modify data, and DELETE to remove data.
- JOIN clauses allow retrieving data from multiple tables by linking them together on common columns. This helps normalize data
The document provides an overview of Apache Atlas, a metadata management and governance solution for Hadoop data lakes. It discusses Atlas' architecture, which uses a graph database to store types and instances. Atlas also includes search capabilities and integration with Hadoop components like Hive to capture lineage metadata. The remainder of the document outlines Atlas' roadmap, with goals of adding additional component connectors, a governance certification program, and generally moving towards a production release.
This document provides a summary of updates to MySQL between October 2021 and May 2021. It discusses three releases of MySQL 8.0 (versions 8.0.23, 8.0.24, and 8.0.25) and new features including invisible columns, asynchronous replication connection failover, improved load/dump functionality in MySQL Shell, and the new MySQL Database Service on Oracle Cloud Infrastructure with HeatWave for accelerated analytics.
This document discusses API anti-patterns, which are commonly occurring solutions to problems that seem good on the surface but are not actually good solutions. It provides examples of anti-patterns related to request parameters, response codes, and organizational structure of APIs. The document advocates for RESTful design practices and using HTTP methods and status codes as intended to clearly represent operations.
The document discusses how data has changed over the past 30 years, with a shift from mostly structured data to mostly unstructured data. However, data management strategies have largely stayed the same, relying on relational databases with predefined schemas. This no longer works well given the volume, variety and velocity of modern data. The document proposes that Apache Hadoop and Cloudera Enterprise provide a new platform that can ingest, store, process and analyze all data at scale to enable businesses to ask bigger questions of their data.
Solr JDBC: Presented by Kevin Risden, Avalon ConsultingLucidworks
Solr JDBC allows users to query indexed data in Apache Solr using standard SQL. It provides a JDBC driver and integrates with existing JDBC tools, allowing SQL skills to be leveraged with Solr. The presenter demonstrated Solr JDBC with various programming languages and tools like Java, Python, R, Apache Zeppelin, RStudio, DbVisualizer and SQuirreL SQL. Future improvements may include replacing Presto with Calcite for SQL processing and enhancing compatibility. Joining data from multiple Solr collections was also discussed.
This document describes an automated zoo management system project. It includes motivations for the project such as overcoming limitations of existing file systems. It outlines objectives like researching domains, designing ER diagrams and DFDs, and implementing a MySQL database. It explains choices of MySQL for the backend and PHP/CSS for the frontend interface. It provides details on the proposed algorithm, backend database design with relations, and important constructs in MySQL and PHP. It also includes examples of insert, delete, and display operations and a datasheet view of relations.
The document discusses using Oracle Database to store and query JSON documents along with relational data. It shows how Oracle allows storing JSON in table columns, querying JSON with SQL, and configuring REST services. It also discusses using materialized views to improve query performance when joining JSON and relational data, redirecting queries to use the materialized view.
Drupal has built-in user authentication but can integrate with external authentication systems using modules. Common systems include LDAP, Kerberos, CAS for single sign-on. Federated authentication allows users from outside the Drupal site to authenticate using standards like OpenID, SAML and OAuth. Modules exist to integrate Drupal with these authentication methods and systems.
The document discusses which database to use for different situations. It begins with explaining why a relational database may not be suitable for all problems and then describes different database categories including key-value stores, column family databases, document databases, graph databases, and Hadoop. It notes the characteristics and uses of each database type. The document concludes that the choice depends on factors like data structure, scalability needs, and workload.
The document discusses designing robust data architectures for decision making. It advocates for building architectures that can easily add new data sources, improve and expand analytics, standardize metadata and storage for easy data access, discover and recover from mistakes. The key aspects discussed are using Kafka as a data bus to decouple pipelines, retaining all data for recovery and experimentation, treating the filesystem as a database by storing intermediate data, leveraging Spark and Spark Streaming for batch and stream processing, and maintaining schemas for integration and evolution of the system.
This are the slides from the intensive Cassandra Workshop I held in Madrid as a Meetup: http://www.meetup.com/Madrid-Cassandra-Users/events/225944063/ They cover all the Cassandra core concepts, and data modelling basic ones to get up and running with Cassandra.
OUG Scotland 2014 - NoSQL and MySQL - The best of both worldsAndrew Morgan
Understand how you can get the benefits you're looking for from NoSQL data stores without sacrificing the power and flexibility of the world's most popular open source database - MySQL.
RESTful Web APIs – Mike Amundsen, Principal API Architect, Layer 7CA API Management
Based on the upcoming O'Reilly book "RESTful Web APIs" by Leonard Richardson and Mike Amundsen, this 1/2 day workshop covers the basics of Fielding's REST style, HTTP standards, and common practices for APIs for Web. Key topics such as how how use hypermedia to increase API flexibility and how application profiles can improve API interoperability are also covered. In addition, a wide range of existing message formats and semantic vocabularies are reviewed along with a procedure for selecting and applying these existing standards to your own implementations. Other subjects will be covered such as caching, versioning, and supporting RESTful APIs on protocols other an HTTP.Throughout the workshop, attendees will be able to apply step-by-step guidance on how to create their own RESTful Web API and share these designs with the group at the end of the session.
The days of a "simple" LAMP stack are behind us. We now rely on different types of technologies, applications and services to run our web based applications. With "the cloud" we have learned how to distribute our operations, but are we resilient when these cloud services are not available?
We have all heard about the major outages of Amazon and Azure in the past and many online services were impacted by those outages. So how can you protect yourself against being "offline" for hours or days and what are the tools you can use to protect yourself against it?
Learn how we protect our customers with distributed systems (cloud and on-prem) to mitigate outages and stay online even when the lights go out.
The document provides an overview of MySQL including:
- MySQL is an open-source relational database management system that runs as a server providing multi-user access to databases.
- MySQL 5.6 introduced improvements to performance, scalability, InnoDB, optimization, replication, and added new capabilities like NoSQL access to InnoDB and a MySQL Hadoop applier.
- MySQL Enterprise Edition offers additional high availability, security, scalability, and monitoring features over the community version through components like the MySQL Thread Pool which improves scalability as user connections grow.
There seems to be one constant when it comes to solar panels: people have a lot of questions about them.
About a year ago, Alex Moundalexis decided to install solar photovoltaic panels on his roof. When he started researching solar panels, he too had lots of questions, so he started taking notes; those notes have become a reference for ongoing reflection and conversation with friends and family. From making the initial decision to generating electricity for the first time took about three months, but since then, his small array has provided more than 90% of his home’s electrical need. Alex shares his experiences evaluating solar PV systems for his home, the resulting financial and energy impacts, and a few surprising things that popped up in the process.
As presented at OSCON 2016 in Austin, Texas. https://youtu.be/FCeNer9F2wU
Improving Hadoop Cluster Performance via Linux ConfigurationAlex Moundalexis
Administering a Hadoop cluster isn't easy. Many Hadoop clusters suffer from Linux configuration problems that can negatively impact performance. With vast and sometimes confusing config/tuning options, it can can tempting (and scary) for a cluster administrator to make changes to Hadoop when cluster performance isn't as expected. Learn how to improve Hadoop cluster performance and eliminate common problem areas, applicable across use cases, using a handful of simple Linux configuration changes.
The popular R package dplyr provides a consistent grammar for data manipulation that can abstract over diverse data sources. Ian Cook shows how you can use dplyr to query large-scale data using different processing engines including Spark and Impala. He demonstrates the R package sparklyr (from RStudio) and the new R package implyr (from Cloudera) and shares tips for making dplyr code portable.
This document discusses using databases and PHP with web applications. It begins by explaining how databases organize and store data that can then be retrieved and used. It then introduces AMP as a common web development architecture using Apache, MySQL, and PHP. Several examples are provided of how PHP connects to and interacts with a MySQL database, including establishing a connection, selecting a database, and using SQL queries to insert, select, update and delete data from database tables.
The document discusses analyzing Twitter data with Hadoop. It describes using Flume to pull Twitter data from the Twitter API and store it in HDFS as JSON files. Hive is then used to query the JSON data with SQL, taking advantage of the JSONSerDe to parse the JSON. Impala provides faster interactive queries of the same data compared to Hive running MapReduce jobs. The document provides examples of the Flume, Hive, and Impala configurations and queries used in this Twitter analytics workflow.
This document discusses building dynamic web sites that retrieve content from a database rather than static files. It covers the basics of relational databases, how they can be used to store and retrieve flexible, customized content for websites. Specific topics covered include connecting to a MySQL database from PHP, performing queries to select, insert and update data, and using WHERE and ORDER BY clauses to search and sort records. The document provides examples of common SQL queries and functions for working with database records in PHP scripts.
MySQL Baics - Texas Linxufest beginners tutorial May 31st, 2019Dave Stokes
MySQL is a relational database management system. The document provides an introduction to MySQL, including:
- MySQL is available in both community and enterprise editions. The community edition is free to use while the enterprise edition starts at $5K/4 core CPU before discounts.
- Data in MySQL is organized into tables within schemas (or databases). Tables contain rows of data organized into columns.
- Structured Query Language (SQL) is used to interact with MySQL databases. Common SQL commands include SELECT to retrieve data, INSERT to add data, UPDATE to modify data, and DELETE to remove data.
- JOIN clauses allow retrieving data from multiple tables by linking them together on common columns. This helps normalize data
The document provides an overview of Apache Atlas, a metadata management and governance solution for Hadoop data lakes. It discusses Atlas' architecture, which uses a graph database to store types and instances. Atlas also includes search capabilities and integration with Hadoop components like Hive to capture lineage metadata. The remainder of the document outlines Atlas' roadmap, with goals of adding additional component connectors, a governance certification program, and generally moving towards a production release.
This document provides a summary of updates to MySQL between October 2021 and May 2021. It discusses three releases of MySQL 8.0 (versions 8.0.23, 8.0.24, and 8.0.25) and new features including invisible columns, asynchronous replication connection failover, improved load/dump functionality in MySQL Shell, and the new MySQL Database Service on Oracle Cloud Infrastructure with HeatWave for accelerated analytics.
This document discusses API anti-patterns, which are commonly occurring solutions to problems that seem good on the surface but are not actually good solutions. It provides examples of anti-patterns related to request parameters, response codes, and organizational structure of APIs. The document advocates for RESTful design practices and using HTTP methods and status codes as intended to clearly represent operations.
The document discusses how data has changed over the past 30 years, with a shift from mostly structured data to mostly unstructured data. However, data management strategies have largely stayed the same, relying on relational databases with predefined schemas. This no longer works well given the volume, variety and velocity of modern data. The document proposes that Apache Hadoop and Cloudera Enterprise provide a new platform that can ingest, store, process and analyze all data at scale to enable businesses to ask bigger questions of their data.
Solr JDBC: Presented by Kevin Risden, Avalon ConsultingLucidworks
Solr JDBC allows users to query indexed data in Apache Solr using standard SQL. It provides a JDBC driver and integrates with existing JDBC tools, allowing SQL skills to be leveraged with Solr. The presenter demonstrated Solr JDBC with various programming languages and tools like Java, Python, R, Apache Zeppelin, RStudio, DbVisualizer and SQuirreL SQL. Future improvements may include replacing Presto with Calcite for SQL processing and enhancing compatibility. Joining data from multiple Solr collections was also discussed.
This document describes an automated zoo management system project. It includes motivations for the project such as overcoming limitations of existing file systems. It outlines objectives like researching domains, designing ER diagrams and DFDs, and implementing a MySQL database. It explains choices of MySQL for the backend and PHP/CSS for the frontend interface. It provides details on the proposed algorithm, backend database design with relations, and important constructs in MySQL and PHP. It also includes examples of insert, delete, and display operations and a datasheet view of relations.
The document discusses using Oracle Database to store and query JSON documents along with relational data. It shows how Oracle allows storing JSON in table columns, querying JSON with SQL, and configuring REST services. It also discusses using materialized views to improve query performance when joining JSON and relational data, redirecting queries to use the materialized view.
Drupal has built-in user authentication but can integrate with external authentication systems using modules. Common systems include LDAP, Kerberos, CAS for single sign-on. Federated authentication allows users from outside the Drupal site to authenticate using standards like OpenID, SAML and OAuth. Modules exist to integrate Drupal with these authentication methods and systems.
The document discusses which database to use for different situations. It begins with explaining why a relational database may not be suitable for all problems and then describes different database categories including key-value stores, column family databases, document databases, graph databases, and Hadoop. It notes the characteristics and uses of each database type. The document concludes that the choice depends on factors like data structure, scalability needs, and workload.
The document discusses designing robust data architectures for decision making. It advocates for building architectures that can easily add new data sources, improve and expand analytics, standardize metadata and storage for easy data access, discover and recover from mistakes. The key aspects discussed are using Kafka as a data bus to decouple pipelines, retaining all data for recovery and experimentation, treating the filesystem as a database by storing intermediate data, leveraging Spark and Spark Streaming for batch and stream processing, and maintaining schemas for integration and evolution of the system.
This are the slides from the intensive Cassandra Workshop I held in Madrid as a Meetup: http://www.meetup.com/Madrid-Cassandra-Users/events/225944063/ They cover all the Cassandra core concepts, and data modelling basic ones to get up and running with Cassandra.
OUG Scotland 2014 - NoSQL and MySQL - The best of both worldsAndrew Morgan
Understand how you can get the benefits you're looking for from NoSQL data stores without sacrificing the power and flexibility of the world's most popular open source database - MySQL.
RESTful Web APIs – Mike Amundsen, Principal API Architect, Layer 7CA API Management
Based on the upcoming O'Reilly book "RESTful Web APIs" by Leonard Richardson and Mike Amundsen, this 1/2 day workshop covers the basics of Fielding's REST style, HTTP standards, and common practices for APIs for Web. Key topics such as how how use hypermedia to increase API flexibility and how application profiles can improve API interoperability are also covered. In addition, a wide range of existing message formats and semantic vocabularies are reviewed along with a procedure for selecting and applying these existing standards to your own implementations. Other subjects will be covered such as caching, versioning, and supporting RESTful APIs on protocols other an HTTP.Throughout the workshop, attendees will be able to apply step-by-step guidance on how to create their own RESTful Web API and share these designs with the group at the end of the session.
The days of a "simple" LAMP stack are behind us. We now rely on different types of technologies, applications and services to run our web based applications. With "the cloud" we have learned how to distribute our operations, but are we resilient when these cloud services are not available?
We have all heard about the major outages of Amazon and Azure in the past and many online services were impacted by those outages. So how can you protect yourself against being "offline" for hours or days and what are the tools you can use to protect yourself against it?
Learn how we protect our customers with distributed systems (cloud and on-prem) to mitigate outages and stay online even when the lights go out.
The document provides an overview of MySQL including:
- MySQL is an open-source relational database management system that runs as a server providing multi-user access to databases.
- MySQL 5.6 introduced improvements to performance, scalability, InnoDB, optimization, replication, and added new capabilities like NoSQL access to InnoDB and a MySQL Hadoop applier.
- MySQL Enterprise Edition offers additional high availability, security, scalability, and monitoring features over the community version through components like the MySQL Thread Pool which improves scalability as user connections grow.
There seems to be one constant when it comes to solar panels: people have a lot of questions about them.
About a year ago, Alex Moundalexis decided to install solar photovoltaic panels on his roof. When he started researching solar panels, he too had lots of questions, so he started taking notes; those notes have become a reference for ongoing reflection and conversation with friends and family. From making the initial decision to generating electricity for the first time took about three months, but since then, his small array has provided more than 90% of his home’s electrical need. Alex shares his experiences evaluating solar PV systems for his home, the resulting financial and energy impacts, and a few surprising things that popped up in the process.
As presented at OSCON 2016 in Austin, Texas. https://youtu.be/FCeNer9F2wU
Improving Hadoop Cluster Performance via Linux ConfigurationAlex Moundalexis
Administering a Hadoop cluster isn't easy. Many Hadoop clusters suffer from Linux configuration problems that can negatively impact performance. With vast and sometimes confusing config/tuning options, it can can tempting (and scary) for a cluster administrator to make changes to Hadoop when cluster performance isn't as expected. Learn how to improve Hadoop cluster performance and eliminate common problem areas, applicable across use cases, using a handful of simple Linux configuration changes.
A brief introduction to YARN: how and why it came into existence and how it fits together with this thing called Hadoop.
Focus given to architecture, availability, resource management and scheduling, migration from MR1 to MR2, job history and logging, interfaces, and applications.
Administering a Hadoop cluster isn't easy. Many Hadoop clusters suffer from Linux configuration problems that can negatively impact performance. With vast and sometimes confusing config/tuning options, it can can tempting (and scary) for a cluster administrator to make changes to Hadoop when cluster performance isn't as expected. Learn how to improve Hadoop cluster performance and eliminate common problem areas, applicable across use cases, using a handful of simple Linux configuration changes.
The document discusses the visual tour of Hadoop User Experience (HUE) 2.2.0. It covers the basics of logging in and user settings. It also describes interacting with HDFS using the file browser and interacting with Hive using the Beeswax interface.
This document provides an overview of SolrCloud on Hadoop. It discusses how SolrCloud allows for distributed, highly scalable search capabilities on Hadoop clusters. Key components that work with SolrCloud are also summarized, including HDFS for storage, MapReduce for processing, and ZooKeeper for coordination services. The document demonstrates how SolrCloud can index and query large datasets stored in Hadoop.
Search in the Apache Hadoop Ecosystem: Thoughts from the FieldAlex Moundalexis
This presentation describes the Hadoop ecosystem and gives examples of how these open source tools are combined and used to solve specific and sometimes very complex problems. Drawing upon case studies from the field, Mr. Moundalexis demonstrates that one-size, rigid traditional systems don’t fit all, but that combinations of tools in the Apache Hadoop ecosystem provide a versatile and flexible platform for integrating, finding, and analyzing information.
Your One-Stop Shop for Python Success: Top 10 US Python Development Providersakankshawande
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Webinar: Designing a schema for a Data WarehouseFederico Razzoli
Are you new to data warehouses (DWH)? Do you need to check whether your data warehouse follows the best practices for a good design? In both cases, this webinar is for you.
A data warehouse is a central relational database that contains all measurements about a business or an organisation. This data comes from a variety of heterogeneous data sources, which includes databases of any type that back the applications used by the company, data files exported by some applications, or APIs provided by internal or external services.
But designing a data warehouse correctly is a hard task, which requires gathering information about the business processes that need to be analysed in the first place. These processes must be translated into so-called star schemas, which means, denormalised databases where each table represents a dimension or facts.
We will discuss these topics:
- How to gather information about a business;
- Understanding dictionaries and how to identify business entities;
- Dimensions and facts;
- Setting a table granularity;
- Types of facts;
- Types of dimensions;
- Snowflakes and how to avoid them;
- Expanding existing dimensions and facts.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Speck&Tech
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
Generating privacy-protected synthetic data using Secludy and MilvusZilliz
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
AI 101: An Introduction to the Basics and Impact of Artificial IntelligenceIndexBug
Imagine a world where machines not only perform tasks but also learn, adapt, and make decisions. This is the promise of Artificial Intelligence (AI), a technology that's not just enhancing our lives but revolutionizing entire industries.
Programming Foundation Models with DSPy - Meetup SlidesZilliz
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
Ocean lotus Threat actors project by John Sitima 2024 (1).pptxSitimaJohn
Ocean Lotus cyber threat actors represent a sophisticated, persistent, and politically motivated group that poses a significant risk to organizations and individuals in the Southeast Asian region. Their continuous evolution and adaptability underscore the need for robust cybersecurity measures and international cooperation to identify and mitigate the threats posed by such advanced persistent threat groups.
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
Introduction of Cybersecurity with OSS at Code Europe 2024Hiroshi SHIBATA
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
2. Thirty
Seconds
About
Alex
• Solu@ons
Architect
• aka
consultant
• government
• infrastructure
• former
coder
of
Perl
• former
administrator
• likes
shiny
objects
2
3. What
Does
Cloudera
Do?
• product
• distribu@on
of
Hadoop
components,
Apache
licensed
• enterprise
tooling
• support
• training
• services
(aka
consul@ng)
• community
3
4. Disclaimer
• Cloudera
builds
things
soMware
• most
donated
to
Apache
• some
closed-‐source
• Cloudera
“products”
I
reference
are
open
source
• Apache
Licensed
• source
code
is
on
GitHub
• hSps://github.com/cloudera
4
5. What
This
Talk
Isn’t
About
• deploying
• Puppet,
Chef,
Ansible,
homegrown
scripts,
intern
labor
• sizing
&
tuning
• depends
heavily
on
data
and
workload
• coding
• unless
you
count
XML
or
CSV
or
SQL
• algorithms
5
7. Why
“Ecosystem?”
• In
the
beginning,
just
Hadoop
• HDFS
• MapReduce
• Today,
dozens
of
interrelated
components
• I/O
• Processing
• Specialty
Applica@ons
• Configura@on
• Workflow
7
8. HDFS
• Distributed,
highly
fault-‐tolerant
filesystem
• Op@mized
for
large
streaming
access
to
data
• Based
on
Google
File
System
• hSp://research.google.com/archive/gfs.html
8
10. MapReduce
(MR)
• Programming
paradigm
• Batch
oriented,
not
real@me
• Works
well
with
distributed
compu@ng
• Lots
of
Java,
but
other
languages
supported
• Based
on
Google’s
paper
• hSp://research.google.com/archive/mapreduce.html
10
17. 17
I
am
not
a
SQL
wizard
by
any
means…
Super
Shady
SQL
Supplement
18. A
Simple
Rela@onal
Database
name
state
employer
year
Alex
Maryland
Cloudera
2013
Joey
Maryland
Cloudera
2011
Sean
Texas
Cloudera
2013
Paris
Maryland
AOL
2011
18
19. Interac@ng
with
Rela@onal
Data
name
state
employer
year
Alex
Maryland
Cloudera
2013
Joey
Maryland
Cloudera
2011
Sean
Texas
Cloudera
2013
Paris
Maryland
AOL
2011
19
SELECT
*
FROM
people;
20. Interac@ng
with
Rela@onal
Data
name
state
employer
year
Alex
Maryland
Cloudera
2013
Joey
Maryland
Cloudera
2011
Sean
Texas
Cloudera
2013
Paris
Maryland
AOL
2011
20
SELECT
*
FROM
people;
21. Reques@ng
Specific
Fields
name
state
employer
year
Alex
Maryland
Cloudera
2013
Joey
Maryland
Cloudera
2011
Sean
Texas
Cloudera
2013
Paris
Maryland
AOL
2011
21
SELECT
name,
state
FROM
people;
22. Reques@ng
Specific
Fields
name
state
employer
year
Alex
Maryland
Cloudera
2013
Joey
Maryland
Cloudera
2011
Sean
Texas
Cloudera
2013
Paris
Maryland
AOL
2011
22
SELECT
name,
state
FROM
people;
23. Reques@ng
Specific
Rows
name
state
employer
year
Alex
Maryland
Cloudera
2013
Joey
Maryland
Cloudera
2011
Sean
Texas
Cloudera
2013
Paris
Maryland
AOL
2011
23
SELECT
name,
state
FROM
people
WHERE
year
2012;
24. Reques@ng
Specific
Rows
name
state
employer
year
Alex
Maryland
Cloudera
2013
Joey
Maryland
Cloudera
2011
Sean
Texas
Cloudera
2013
Paris
Maryland
AOL
2011
24
SELECT
name,
state
FROM
people
WHERE
year
2012;
25. Two
Simple
Tables
owner
species
name
Alex
Cactus
Marvin
Joey
Cat
Brain
Sean
None
Paris
Unknown
25
name
state
employer
year
Alex
Maryland
Cloudera
2013
Joey
Maryland
Cloudera
2011
Sean
Texas
Cloudera
2013
Paris
Maryland
AOL
2011
26. Joining
Two
Tables
owner
species
name
Alex
Cactus
Marvin
Joey
Cat
Brain
Sean
None
Paris
Unknown
26
SELECT
people.name
AS
owner,
people.state
AS
state,
pets.name
AS
pet
FROM
people
LEFT
JOIN
pets
ON
people.name
=
pets.owner
name
state
employer
year
Alex
Maryland
Cloudera
2013
Joey
Maryland
Cloudera
2011
Sean
Texas
Cloudera
2013
Paris
Maryland
AOL
2011
27. Joining
Two
Tables
owner
species
name
Alex
Cactus
Marvin
Joey
Cat
Brain
Sean
None
Paris
Unknown
27
SELECT
people.name
AS
owner,
people.state
AS
state,
pets.name
AS
pet
FROM
people
LEFT
JOIN
pets
ON
people.name
=
pets.owner
name
state
employer
year
Alex
Maryland
Cloudera
2013
Joey
Maryland
Cloudera
2011
Sean
Texas
Cloudera
2013
Paris
Maryland
AOL
2011
28. Joining
Two
Tables
owner
species
name
Alex
Cactus
Marvin
Joey
Cat
Brain
Sean
None
Paris
Unknown
28
SELECT
people.name
AS
owner,
people.state
AS
state,
pets.name
AS
pet
FROM
people
LEFT
JOIN
pets
ON
people.name
=
pets.owner
name
state
employer
year
Alex
Maryland
Cloudera
2013
Joey
Maryland
Cloudera
2011
Sean
Texas
Cloudera
2013
Paris
Maryland
AOL
2011
29. Joining
Two
Tables
29
SELECT
people.name
AS
owner,
people.state
AS
state,
pets.name
AS
pet
FROM
people
LEFT
JOIN
pets
ON
people.name
=
pets.owner
owner
state
pet
Alex
Maryland
Marvin
Joey
Maryland
Brain
Sean
Texas
Paris
Maryland
30. Varying
Implementa@on
of
JOIN
30
SELECT
people.name
AS
owner,
people.state
AS
state,
pets.name
AS
pet
FROM
people
LEFT
JOIN
pets
ON
people.name
=
pets.owner
owner
state
pet
Alex
Maryland
Marvin
Joey
Maryland
Brain
Sean
Texas
?
Paris
Maryland
?
32. Cloudera
Impala
• Interac@ve
query
on
Hadoop
• think
seconds,
not
minutes
• Nearly
ANSI-‐92
standard
SQL
• compa@ble
with
HiveQL
• Na@ve
MPP
query
engine
• built
for
low-‐latency
queries
32
33. Cloudera
Impala
–
Design
Choices
• Na@ve
daemons,
wriSen
in
C/C++
• No
JVM,
no
MapReduce
• Saturate
disks
on
reads
• Uses
in-‐memory
HDFS
caching
• Re-‐uses
Hive
metastore
• Not
as
fault-‐tolerant
as
MapReduce
33
34. Cloudera
Impala
–
Architecture
• Impala
Daemon
• runs
on
every
node
• handles
client
requests
• handles
query
planning
execu@on
• State
Store
Daemon
• provides
name
service
• metadata
distribu@on
• used
for
finding
data
34
36. Impala
Query
Execu@on
36
Query
Planner
Query
Coordinator
Query
Executor
HDFS
DN
HBase
SQL
App
ODBC
Hive
Metastore
HDFS
NN
Statestore
Query
Planner
Query
Coordinator
Query
Executor
HDFS
DN
HBase
Query
Planner
Query
Coordinator
Query
Executor
HDFS
DN
HBase
2)
Planner
turns
request
into
collecRons
of
plan
fragments
3)
Coordinator
iniRates
execuRon
on
impalad(s)
local
to
data
37. Impala
Query
Execu@on
37
Query
Planner
Query
Coordinator
Query
Executor
HDFS
DN
HBase
SQL
App
ODBC
Hive
Metastore
HDFS
NN
Statestore
Query
Planner
Query
Coordinator
Query
Executor
HDFS
DN
HBase
Query
Planner
Query
Coordinator
Query
Executor
HDFS
DN
HBase
4)
Intermediate
results
are
streamed
between
impalad(s)
5)
Query
results
are
streamed
back
to
client
Query
results
38. Cloudera
Impala
–
Results
• Allows
for
fast
itera@on/discovery
• How
much
faster?
• 3-‐4x
faster
on
I/O
bound
workloads
• up
to
45x
faster
on
mul@-‐MR
queries
• up
to
90x
faster
on
in-‐memory
cache
38