Slides from Apache Spark Workshop from Big Data Trunk. It provides a fun way to introduce Apache Spark in the big data world.
www.BigDataTrunk.com
Youtube channel
https://www.youtube.com/channel/UCp7pR7BJNnRueEuLSau0TzA
This document summarizes Sarah Guido's talk on using Apache Spark for data science at Bitly. She discusses how Bitly uses Spark to extract, explore, and model subsets of their data including decoding Bitly links, performing topic modeling using LDA, and trend detection. While Spark provides performance benefits over MapReduce for these tasks, she notes issues with Hadoop servers, JVM, and lack of documentation that must be addressed for full production usage at Bitly.
Talend was founded in 2006 and has since grown to over 1000 employees across 10 countries serving over 1500 customers. The document discusses Apache Beam, an open source model for defining and executing data processing pipelines, and how Talend's data preparation and data streams products utilize Apache Beam and can run on Apache Spark. It concludes with a demonstration of Talend's data preparation and data streams capabilities.
Data Tools and the Data Scientist ShortageWes McKinney
Wes McKinney discusses the shortage of data scientists and analysts. There is a shortage of 140,000-190,000 people with analytics expertise and 1.5 million managers/analysts with skills to understand and make decisions based on big data analysis in the United States alone. This shortage can be addressed through improved education, tools, and a cultural shift. New approaches and tools are needed to make data science accessible to more people and bring analytics capabilities to various industries.
Valentyn Kropov, Big Data Solutions Architect has recently attended "Hadoop World / Strata" – biggest and coolest Big Data conference in a World, and he can't wait to share fresh trends and topics straight from New-York. Come and learn how Hadoop cluster will help NASA to explore Mars, how Netflix build 10PB platform, what are the latest trends in Spark, to learn about newest, just announced storage engine from Cloudera called Kudu and many many more interesting stuff.
Big Data Retrospective - STL Big Data IDEA Jan 2019Adam Doyle
Slides from the STL Big Data IDEA meeting from January 2019. The presenters discussed technologies to continue using, stop using, and start using in 2019.
Intel: How to Use Alluxio to Accelerate BigData Analytics on the Cloud and Ne...Alluxio, Inc.
The document discusses using Alluxio as an acceleration layer for analytics workloads with disaggregated storage on cloud. Key points:
- Alluxio provides an in-memory layer that caches frequently accessed data, providing a 2-3x performance boost over using object storage directly.
- Workloads like Terasort saw up to 3.25x faster performance when using Alluxio caching compared to the baseline.
- For SQL queries, Alluxio caching improved performance for most queries, though the first few queries in a session saw slower performance as the cache was warming up.
- Compute nodes saw higher CPU utilization when using Alluxio, indicating it offloads work from storage nodes to take
Spark with Azure HDInsight - Tampa Bay Data Science - Adnan Masood, PhDAdnan Masood
Spark is a unified framework for big data analytics. Spark provides one integrated API for use by developers, data scientists, and analysts to perform diverse tasks that would have previously required separate processing engines such as batch analytics, stream processing and statistical modeling. Spark supports a wide range of popular languages including Python, R, Scala, SQL, and Java. Spark can read from diverse data sources and scale to thousands of nodes.
In this presentation we discuss Microsoft HDInsight offering of Spark. Azure HDInsight, Microsoft’s managed Hadoop and Spark cloud service that runs the Hortonworks Data Platform. Spark for Azure HDInsight offers customers an enterprise-ready Spark solution that’s fully managed, secured, and highly available and made simpler for users with compelling and interactive experiences.
Stephen Dillon - Fast Data Presentation Sept 02Stephen Dillon
Fast data is a paradigm for processing large volumes of data from IoT devices in real-time. It emerged due to the growth of IoT, which produces data from many sources at high frequencies. Fast data solutions must support low-latency ingestion, processing, and delivery of data. Apache Spark is a distributed compute engine that supports fast data through its in-memory processing capabilities and APIs. It can process data up to 100 times faster than Hadoop MapReduce.
This document summarizes Sarah Guido's talk on using Apache Spark for data science at Bitly. She discusses how Bitly uses Spark to extract, explore, and model subsets of their data including decoding Bitly links, performing topic modeling using LDA, and trend detection. While Spark provides performance benefits over MapReduce for these tasks, she notes issues with Hadoop servers, JVM, and lack of documentation that must be addressed for full production usage at Bitly.
Talend was founded in 2006 and has since grown to over 1000 employees across 10 countries serving over 1500 customers. The document discusses Apache Beam, an open source model for defining and executing data processing pipelines, and how Talend's data preparation and data streams products utilize Apache Beam and can run on Apache Spark. It concludes with a demonstration of Talend's data preparation and data streams capabilities.
Data Tools and the Data Scientist ShortageWes McKinney
Wes McKinney discusses the shortage of data scientists and analysts. There is a shortage of 140,000-190,000 people with analytics expertise and 1.5 million managers/analysts with skills to understand and make decisions based on big data analysis in the United States alone. This shortage can be addressed through improved education, tools, and a cultural shift. New approaches and tools are needed to make data science accessible to more people and bring analytics capabilities to various industries.
Valentyn Kropov, Big Data Solutions Architect has recently attended "Hadoop World / Strata" – biggest and coolest Big Data conference in a World, and he can't wait to share fresh trends and topics straight from New-York. Come and learn how Hadoop cluster will help NASA to explore Mars, how Netflix build 10PB platform, what are the latest trends in Spark, to learn about newest, just announced storage engine from Cloudera called Kudu and many many more interesting stuff.
Big Data Retrospective - STL Big Data IDEA Jan 2019Adam Doyle
Slides from the STL Big Data IDEA meeting from January 2019. The presenters discussed technologies to continue using, stop using, and start using in 2019.
Intel: How to Use Alluxio to Accelerate BigData Analytics on the Cloud and Ne...Alluxio, Inc.
The document discusses using Alluxio as an acceleration layer for analytics workloads with disaggregated storage on cloud. Key points:
- Alluxio provides an in-memory layer that caches frequently accessed data, providing a 2-3x performance boost over using object storage directly.
- Workloads like Terasort saw up to 3.25x faster performance when using Alluxio caching compared to the baseline.
- For SQL queries, Alluxio caching improved performance for most queries, though the first few queries in a session saw slower performance as the cache was warming up.
- Compute nodes saw higher CPU utilization when using Alluxio, indicating it offloads work from storage nodes to take
Spark with Azure HDInsight - Tampa Bay Data Science - Adnan Masood, PhDAdnan Masood
Spark is a unified framework for big data analytics. Spark provides one integrated API for use by developers, data scientists, and analysts to perform diverse tasks that would have previously required separate processing engines such as batch analytics, stream processing and statistical modeling. Spark supports a wide range of popular languages including Python, R, Scala, SQL, and Java. Spark can read from diverse data sources and scale to thousands of nodes.
In this presentation we discuss Microsoft HDInsight offering of Spark. Azure HDInsight, Microsoft’s managed Hadoop and Spark cloud service that runs the Hortonworks Data Platform. Spark for Azure HDInsight offers customers an enterprise-ready Spark solution that’s fully managed, secured, and highly available and made simpler for users with compelling and interactive experiences.
Stephen Dillon - Fast Data Presentation Sept 02Stephen Dillon
Fast data is a paradigm for processing large volumes of data from IoT devices in real-time. It emerged due to the growth of IoT, which produces data from many sources at high frequencies. Fast data solutions must support low-latency ingestion, processing, and delivery of data. Apache Spark is a distributed compute engine that supports fast data through its in-memory processing capabilities and APIs. It can process data up to 100 times faster than Hadoop MapReduce.
Spark in the Hadoop Ecosystem-(Mike Olson, Cloudera)Spark Summit
Spark fits into the Hadoop ecosystem alongside other frameworks like MapReduce, Hive, and Pig. It provides faster processing capabilities than MapReduce for interactive queries and stream processing. Spark also benefits from sharing components with other frameworks in Hadoop, including security, data governance, and operations.
Semantic Search: Fast Results from Large, Non-Native Language Corpora with Ro...Databricks
The Semantic Engine is a custom search engine deployable on top of large, non-native language corpora that goes beyond keyword search and does NOT require translation. The large, on-the-fly calculations essential to making this an effective search engine necessitated development on a distributed platform capable of processing large volumes of unstructured data.
Hear how the low barrier to entry provided by Apache Spark allowed the Novetta Solutions team to focus on the hard analytical challenges presented by their data, without having to spend much time grappling with the inherent difficulties normally associated with distributed computing.
The document discusses using spot instances with Druid for cost savings. It describes that spot instances provide lower costs but less availability than on-demand instances. The document outlines how Druid is configured to use Terraform and Helm for infrastructure setup and deployment. It also discusses how Druid's stateless architecture and redundancy across middle managers and historical nodes allows it to withstand spot instance interruptions without data loss.
From R Script to Production Using rsparkling with Navdeep GillDatabricks
The rsparkling R package is an extension package for sparklyr (an R interface for Apache Spark) that creates an R front-end for the Sparkling Water Spark package from H2O. This provides an interface to H2O’s high performance, distributed machine learning algorithms on Spark, using R. The main purpose of this package is to provide a connector between sparklyr and H2O’s machine learning algorithms.
In this session, Gill will introduce the basic architectures of rsparkling, H2O Sparkling Water and sparklyr, and go over how these frameworks work together to build a cohesive machine learning framework. In addition, you’ll learn about various implementations for using rsparkling in production. The session will conclude with a live demo of rsparkling that will display an end-to-end use case of data ingestion, munging and machine learning.
Spark - The Ultimate Scala Collections by Martin OderskySpark Summit
Spark is a domain-specific language for working with collections that is implemented in Scala and runs on a cluster. While similar to Scala collections, Spark differs in that it is lazy and supports additional functionality for paired data. Scala can learn from Spark by adding views to make laziness clearer, caching for persistence, and pairwise operations. Types are important for Spark as they prevent logic errors and help with programming complex functional operations across a cluster.
(1) The document discusses challenges of managing large and complex datasets for interdisciplinary research projects. It presents Hadoop and the Etosha data catalog as solutions.
(2) Etosha aims to publish and link metadata about datasets to enable discovery and sharing across distributed research clusters. It focuses on descriptive, structural and administrative metadata rather than just technical metadata.
(3) Etosha's architecture includes a distributed metadata service and context browser that can query metadata from different Hadoop clusters to support federated querying and subquery delegation.
The Little Warehouse That Couldn't Or: How We Learned to Stop Worrying and Mo...Spark Summit
The document discusses the challenges faced by Shopify in using its existing data warehouse and ETL processes due to increasing data volume and complexity. It describes Shopify's attempts to use Pig and Luigi as well as Platfora to address these issues, but notes they did not meet Shopify's needs. Shopify then moved to using Spark due to its fast performance, nice development model using Python, and ability to better handle their data and query complexity. The summary provides an overview of why Shopify changed its data warehousing approach and the key technology it adopted.
This document discusses building a digital bank and Macquarie's digital transformation efforts. It summarizes that Macquarie wants to deliver awesome digital experiences for clients, new revenue streams, and operational efficiency through digital transformation. The main drivers of Macquarie's transformation are a new way of work focused on client needs, client experience, strategic partnerships, and service-driven IT.
This document discusses data ingestion with Spark. It provides an overview of Spark, which is a unified analytics engine that can handle batch processing, streaming, SQL queries, machine learning and graph processing. Spark improves on MapReduce by keeping data in-memory between jobs for faster processing. The document contrasts data collection, which occurs where data originates, with data ingestion, which receives and routes data, sometimes coupled with storage.
- A brief introduction to Spark Core
- Introduction to Spark Streaming
- A Demo of Streaming by evaluation top hashtags being used
- Introduction to Spark MLlib
- A Demo of MLlib by building a simple movie recommendation engine
Uber has created a Data Science Workbench to improve the productivity of its data scientists by providing scalable tools, customization, and support. The Workbench provides Jupyter notebooks for interactive coding and visualization, RStudio for rapid prototyping, and Apache Spark for distributed processing. It aims to centralize infrastructure provisioning, leverage Uber's distributed backend, enable knowledge sharing and search, and integrate with Uber's data ecosystem tools. The Workbench manages Docker containers of tools like Jupyter and RStudio running on a Mesos cluster, with files stored in a shared file system. It addresses the problems of wasted time from separate infrastructures and lack of tool standardization across Uber's data science teams.
This document introduces TitanDB, a scalable graph database, and Apache TinkerPop, an open-source graph computing framework. It defines what a graph database is, the need for graph databases and TitanDB. It describes key features of TitanDB like support for various storage backends and integration with tools like Spark and Giraph. It also summarizes the CAP theorem, TitanDB architecture, its acquisition by DataStax, and what Apache TinkerPop is and why it is needed when dealing with complex graph databases.
Spectator to Participant. Contributing to Cassandra (Patrick McFadin, DataSta...DataStax
Feeling the need to contribute something to Apache Cassandra? Maybe you want to help guide the future of your favorite database? Get off the sidelines and get in the game! It's easy to say but how do you even get started? I will outline some of the ways you can help contribute to Apache Cassandra from minor to major. If you don't have the time or ability to submit code, there are a lot of ways you can participate. What if you do want to write some code? I can walk you through the process of creating a patch and submitting for final approval. Got a great idea? I'll show you propose that to the community at large. Take it from me, participating is so much more fun than just watching the project from a distance. Time to jump in!
About the Speaker
Patrick McFadin Chief Evangelist, DataStax
Patrick McFadin is one of the leading experts of Apache Cassandra and data modeling techniques. As the Chief Evangelist for Apache Cassandra and consultant for DataStax, he has helped build some of the largest and exciting deployments in production. Previous to DataStax, he was Chief Architect at Hobsons and an Oracle DBA/Developer for over 15 years.
From a student to an apache committer practice of apache io tdbjixuan1989
This talk is introduce by Xiangdong Huang, who is a PPMC of Apache IoTDB (incubating) project, at Apache Event at Tsinghua University in China.
About the Event:
The open source ecosystem plays more and more important role in the world. Open source software is widely used in operating systems, cloud computing, big data, artificial intelligence, and industrial Internet. Many companies have gradually increased their participation in the open source community. Developers with open source experience are increasingly valued and favored by large enterprises. The Apache Software Foundation is one of the most important open source communities, contributing a large number of valuable open source software and communities to the world.
The invited guests of this lecture are all from ASF community, including the chairman of the Apache Software Foundation, three Apache members, Top 5 Apache code committers (according to Apache annual report), the first Committer in the Hadoop project in China, several Apache project mentors or VPs, and many Apache Committers. They will tell you what the open source culture is, how to join the Apache open source community, and the Apache Way.
Apache Druid ingests and enables instant query on many billions of events in real-time. But how? In this talk, each of the components of an Apache Druid cluster is described – along with the data and query optimisations at its core – that unlock fresh, fast data for all.
Bio: Peter Marshall (https://linkedin.com/in/amillionbytes/) leads outreach and engineering across Europe for Imply (http://imply.io/), a company founded by the original developers of Apache Druid. He has 20 years architecture experience in CRM, EDRM, ERP, EIP, Digital Services, Security, BI, Analytics, and MDM. He is TOGAF certified and has a BA (hons) degree in Theology and Computer Studies from the University of Birmingham in the United Kingdom.
JEEConf 2015 - Introduction to real-time big data with Apache SparkTaras Matyashovsky
This presentation will be useful to those who would like to get acquainted with Apache Spark architecture, top features and see some of them in action, e.g. RDD transformations and actions, Spark SQL, etc. Also it covers real life use cases related to one of ours commercial projects and recall roadmap how we’ve integrated Apache Spark into it.
Was presented on JEEConf 2015 in Kyiv.
Design by Yarko Filevych: http://www.filevych.com/
Spark is going to replace Apache Hadoop! Know Why?Edureka!
The document discusses how Spark is emerging to replace Hadoop for big data processing. It notes that Hadoop MapReduce is limited to batch processing and is not fast enough for real-time processing needs. In contrast, Spark is up to 100 times faster than Hadoop MapReduce, supports both batch and real-time processing, and stores data in memory for faster analysis. A survey is cited showing increasing adoption of Spark over Hadoop in industries handling large volumes of data. The document concludes that while Hadoop will still be used, Spark will replace Hadoop MapReduce as the primary framework for big data applications due to its ability to support real-time processing demands.
Introduction to Big data with Hadoop & Spark | Big Data Hadoop Spark Tutorial...CloudxLab
(Big Data with Hadoop & Spark Training: http://bit.ly/2k2wiL9
This CloudxLab Big Data with Hadoop and Spark tutorial helps you to understand Big Data in detail. Below are the topics covered in this tutorial:
1) Data Variety
2) What is Big Data?
3) Characteristics of Big Data - Volume, Velocity, and Variety
4) Why Big Data and why it is important now?
5) Example Big Data Customers
6) Big Data Solutions
7) What is Hadoop?
8) Hadoop Components
9) Apache Spark Introduction & Architecture
Spark in the Hadoop Ecosystem-(Mike Olson, Cloudera)Spark Summit
Spark fits into the Hadoop ecosystem alongside other frameworks like MapReduce, Hive, and Pig. It provides faster processing capabilities than MapReduce for interactive queries and stream processing. Spark also benefits from sharing components with other frameworks in Hadoop, including security, data governance, and operations.
Semantic Search: Fast Results from Large, Non-Native Language Corpora with Ro...Databricks
The Semantic Engine is a custom search engine deployable on top of large, non-native language corpora that goes beyond keyword search and does NOT require translation. The large, on-the-fly calculations essential to making this an effective search engine necessitated development on a distributed platform capable of processing large volumes of unstructured data.
Hear how the low barrier to entry provided by Apache Spark allowed the Novetta Solutions team to focus on the hard analytical challenges presented by their data, without having to spend much time grappling with the inherent difficulties normally associated with distributed computing.
The document discusses using spot instances with Druid for cost savings. It describes that spot instances provide lower costs but less availability than on-demand instances. The document outlines how Druid is configured to use Terraform and Helm for infrastructure setup and deployment. It also discusses how Druid's stateless architecture and redundancy across middle managers and historical nodes allows it to withstand spot instance interruptions without data loss.
From R Script to Production Using rsparkling with Navdeep GillDatabricks
The rsparkling R package is an extension package for sparklyr (an R interface for Apache Spark) that creates an R front-end for the Sparkling Water Spark package from H2O. This provides an interface to H2O’s high performance, distributed machine learning algorithms on Spark, using R. The main purpose of this package is to provide a connector between sparklyr and H2O’s machine learning algorithms.
In this session, Gill will introduce the basic architectures of rsparkling, H2O Sparkling Water and sparklyr, and go over how these frameworks work together to build a cohesive machine learning framework. In addition, you’ll learn about various implementations for using rsparkling in production. The session will conclude with a live demo of rsparkling that will display an end-to-end use case of data ingestion, munging and machine learning.
Spark - The Ultimate Scala Collections by Martin OderskySpark Summit
Spark is a domain-specific language for working with collections that is implemented in Scala and runs on a cluster. While similar to Scala collections, Spark differs in that it is lazy and supports additional functionality for paired data. Scala can learn from Spark by adding views to make laziness clearer, caching for persistence, and pairwise operations. Types are important for Spark as they prevent logic errors and help with programming complex functional operations across a cluster.
(1) The document discusses challenges of managing large and complex datasets for interdisciplinary research projects. It presents Hadoop and the Etosha data catalog as solutions.
(2) Etosha aims to publish and link metadata about datasets to enable discovery and sharing across distributed research clusters. It focuses on descriptive, structural and administrative metadata rather than just technical metadata.
(3) Etosha's architecture includes a distributed metadata service and context browser that can query metadata from different Hadoop clusters to support federated querying and subquery delegation.
The Little Warehouse That Couldn't Or: How We Learned to Stop Worrying and Mo...Spark Summit
The document discusses the challenges faced by Shopify in using its existing data warehouse and ETL processes due to increasing data volume and complexity. It describes Shopify's attempts to use Pig and Luigi as well as Platfora to address these issues, but notes they did not meet Shopify's needs. Shopify then moved to using Spark due to its fast performance, nice development model using Python, and ability to better handle their data and query complexity. The summary provides an overview of why Shopify changed its data warehousing approach and the key technology it adopted.
This document discusses building a digital bank and Macquarie's digital transformation efforts. It summarizes that Macquarie wants to deliver awesome digital experiences for clients, new revenue streams, and operational efficiency through digital transformation. The main drivers of Macquarie's transformation are a new way of work focused on client needs, client experience, strategic partnerships, and service-driven IT.
This document discusses data ingestion with Spark. It provides an overview of Spark, which is a unified analytics engine that can handle batch processing, streaming, SQL queries, machine learning and graph processing. Spark improves on MapReduce by keeping data in-memory between jobs for faster processing. The document contrasts data collection, which occurs where data originates, with data ingestion, which receives and routes data, sometimes coupled with storage.
- A brief introduction to Spark Core
- Introduction to Spark Streaming
- A Demo of Streaming by evaluation top hashtags being used
- Introduction to Spark MLlib
- A Demo of MLlib by building a simple movie recommendation engine
Uber has created a Data Science Workbench to improve the productivity of its data scientists by providing scalable tools, customization, and support. The Workbench provides Jupyter notebooks for interactive coding and visualization, RStudio for rapid prototyping, and Apache Spark for distributed processing. It aims to centralize infrastructure provisioning, leverage Uber's distributed backend, enable knowledge sharing and search, and integrate with Uber's data ecosystem tools. The Workbench manages Docker containers of tools like Jupyter and RStudio running on a Mesos cluster, with files stored in a shared file system. It addresses the problems of wasted time from separate infrastructures and lack of tool standardization across Uber's data science teams.
This document introduces TitanDB, a scalable graph database, and Apache TinkerPop, an open-source graph computing framework. It defines what a graph database is, the need for graph databases and TitanDB. It describes key features of TitanDB like support for various storage backends and integration with tools like Spark and Giraph. It also summarizes the CAP theorem, TitanDB architecture, its acquisition by DataStax, and what Apache TinkerPop is and why it is needed when dealing with complex graph databases.
Spectator to Participant. Contributing to Cassandra (Patrick McFadin, DataSta...DataStax
Feeling the need to contribute something to Apache Cassandra? Maybe you want to help guide the future of your favorite database? Get off the sidelines and get in the game! It's easy to say but how do you even get started? I will outline some of the ways you can help contribute to Apache Cassandra from minor to major. If you don't have the time or ability to submit code, there are a lot of ways you can participate. What if you do want to write some code? I can walk you through the process of creating a patch and submitting for final approval. Got a great idea? I'll show you propose that to the community at large. Take it from me, participating is so much more fun than just watching the project from a distance. Time to jump in!
About the Speaker
Patrick McFadin Chief Evangelist, DataStax
Patrick McFadin is one of the leading experts of Apache Cassandra and data modeling techniques. As the Chief Evangelist for Apache Cassandra and consultant for DataStax, he has helped build some of the largest and exciting deployments in production. Previous to DataStax, he was Chief Architect at Hobsons and an Oracle DBA/Developer for over 15 years.
From a student to an apache committer practice of apache io tdbjixuan1989
This talk is introduce by Xiangdong Huang, who is a PPMC of Apache IoTDB (incubating) project, at Apache Event at Tsinghua University in China.
About the Event:
The open source ecosystem plays more and more important role in the world. Open source software is widely used in operating systems, cloud computing, big data, artificial intelligence, and industrial Internet. Many companies have gradually increased their participation in the open source community. Developers with open source experience are increasingly valued and favored by large enterprises. The Apache Software Foundation is one of the most important open source communities, contributing a large number of valuable open source software and communities to the world.
The invited guests of this lecture are all from ASF community, including the chairman of the Apache Software Foundation, three Apache members, Top 5 Apache code committers (according to Apache annual report), the first Committer in the Hadoop project in China, several Apache project mentors or VPs, and many Apache Committers. They will tell you what the open source culture is, how to join the Apache open source community, and the Apache Way.
Apache Druid ingests and enables instant query on many billions of events in real-time. But how? In this talk, each of the components of an Apache Druid cluster is described – along with the data and query optimisations at its core – that unlock fresh, fast data for all.
Bio: Peter Marshall (https://linkedin.com/in/amillionbytes/) leads outreach and engineering across Europe for Imply (http://imply.io/), a company founded by the original developers of Apache Druid. He has 20 years architecture experience in CRM, EDRM, ERP, EIP, Digital Services, Security, BI, Analytics, and MDM. He is TOGAF certified and has a BA (hons) degree in Theology and Computer Studies from the University of Birmingham in the United Kingdom.
JEEConf 2015 - Introduction to real-time big data with Apache SparkTaras Matyashovsky
This presentation will be useful to those who would like to get acquainted with Apache Spark architecture, top features and see some of them in action, e.g. RDD transformations and actions, Spark SQL, etc. Also it covers real life use cases related to one of ours commercial projects and recall roadmap how we’ve integrated Apache Spark into it.
Was presented on JEEConf 2015 in Kyiv.
Design by Yarko Filevych: http://www.filevych.com/
Spark is going to replace Apache Hadoop! Know Why?Edureka!
The document discusses how Spark is emerging to replace Hadoop for big data processing. It notes that Hadoop MapReduce is limited to batch processing and is not fast enough for real-time processing needs. In contrast, Spark is up to 100 times faster than Hadoop MapReduce, supports both batch and real-time processing, and stores data in memory for faster analysis. A survey is cited showing increasing adoption of Spark over Hadoop in industries handling large volumes of data. The document concludes that while Hadoop will still be used, Spark will replace Hadoop MapReduce as the primary framework for big data applications due to its ability to support real-time processing demands.
Introduction to Big data with Hadoop & Spark | Big Data Hadoop Spark Tutorial...CloudxLab
(Big Data with Hadoop & Spark Training: http://bit.ly/2k2wiL9
This CloudxLab Big Data with Hadoop and Spark tutorial helps you to understand Big Data in detail. Below are the topics covered in this tutorial:
1) Data Variety
2) What is Big Data?
3) Characteristics of Big Data - Volume, Velocity, and Variety
4) Why Big Data and why it is important now?
5) Example Big Data Customers
6) Big Data Solutions
7) What is Hadoop?
8) Hadoop Components
9) Apache Spark Introduction & Architecture
This document introduces Spark, including when it was created, what it is, and why it was developed. Spark was created in 2009 at the AMPLab at UC Berkeley. It is now a top-level Apache project that provides a fast and general engine for large-scale data processing. It has high-level APIs for Scala, Python, R and Java and can be used for SQL, streaming, machine learning and graph processing. The document discusses Spark's programming model and demos its use for applications like Monte Carlo simulation and financial analysis.
Hadoop or Spark: is it an either-or proposition? By Slim BaltagiSlim Baltagi
Hadoop or Spark: is it an either-or proposition? An exodus away from Hadoop to Spark is picking up steam in the news headlines and talks! Away from marketing fluff and politics, this talk analyzes such news and claims from a technical perspective.
In practical ways, while referring to components and tools from both Hadoop and Spark ecosystems, this talk will show that the relationship between Hadoop and Spark is not of an either-or type but can take different forms such as: evolution, transition, integration, alternation and complementarity.
Big Data 2.0 - How Spark technologies are reshaping the world of big data ana...Lillian Pierson
In this one-hour webinar, you will be introduced to Spark, the data engineering that supports it, and the data science advances that it has spurned. You’ll discover the interesting story of its academic origins and then get an overview of the organizations who are using the technology. After being briefed on some impressive Spark case studies, you’ll come to know of the next-generation Spark 2.0 (to be released in just a few months). We will also tell you about the tremendous impact that learning Spark can have upon your current salary, and the best ways to get trained in this ground-breaking new technology.
Big Data Processing with Apache Spark 2014mahchiev
This document provides an overview of Apache Spark, a framework for large-scale data processing. It discusses what big data is, the history and advantages of Spark, and Spark's execution model. Key concepts explained include Resilient Distributed Datasets (RDDs), transformations, actions, and MapReduce algorithms like word count. Examples are provided to illustrate Spark's use of RDDs and how it can improve on Hadoop MapReduce.
This document provides an introduction and overview of Apache Spark, a lightning-fast cluster computing framework. It discusses Spark's ecosystem, how it differs from Hadoop MapReduce, where it shines well, how easy it is to install and start learning, includes some small code demos, and provides additional resources for information. The presentation introduces Spark and its core concepts, compares it to Hadoop MapReduce in areas like speed, usability, tools, and deployment, demonstrates how to use Spark SQL with an example, and shows a visualization demo. It aims to provide attendees with a high-level understanding of Spark without being a training class or workshop.
This document provides an introduction and overview of Apache Spark, a lightning-fast cluster computing framework. It discusses Spark's ecosystem, how it differs from Hadoop MapReduce, where it shines well, how easy it is to install and start learning, includes some small code demos, and provides additional resources for information. The presentation introduces Spark and its core concepts, compares it to Hadoop MapReduce in areas like speed, usability, tools, and deployment, demonstrates how to use Spark SQL with an example, and shows a visualization demo. It aims to provide attendees with a high-level understanding of Spark without being a training class or workshop.
Data Engineer's Lunch 90: Migrating SQL Data with ArcionAnant Corporation
In Data Engineer's Lunch 90, Eric Ramseur teaches our audience how to use Arcion.
From best practices to real-world examples, this talk will provide you with the knowledge and insights you need to ensure a successful migration of your SQL data. So whether you're new to data migration or looking to improve your existing process, join us and discover how Arcion can help you achieve your goals.
In this era of ever growing data, the need for analyzing it for meaningful business insights becomes more and more significant. There are different Big Data processing alternatives like Hadoop, Spark, Storm etc. Spark, however is unique in providing batch as well as streaming capabilities, thus making it a preferred choice for lightening fast Big Data Analysis platforms.
CCA 175 - Hadoop & Spark Developer Certification | Cloudera CCA 175 ExamIntellipaat
The document discusses topics related to Apache Spark, Hadoop, and the CCA175 certification exam for Spark and Hadoop developers. It includes sections that define Hadoop and Spark, describe the CCA175 exam, outline the roles and responsibilities of a big data developer, discuss salaries, and provide tips for getting started in the field. The CCA175 exam tests skills in ingesting, transforming, processing data using Spark and Cloudera tools and covers content domains related to these tasks.
CCA 175 - Hadoop & Spark Developer Certification | Cloudera CCA 175 ExamIntellipaat
YouTube Link : https://www.youtube.com/watch?v=N0YGKlzl8LI
Intellipaat Big Data Hadoop Training: https://intellipaat.com/big-data-hadoop-training/
Intellipaat Post Graduate Certification in Big Data Analytics :
https://intellipaat.com/post-graduate-certification-big-data-analytics/
Read complete Big Data Hadoop tutorial here: https://intellipaat.com/blog/tutorial/hadoop-tutorial/
The document proposes an OpenPOWER AI/cloud system for an organization based on IBM Power9. It includes:
- An IBM Power9 system called Raptor with 32GB RAM, 128GB storage, and Nvidia RTX 2070 GPU for deep learning.
- An education bundle with IBM PowerAI Vision and H2O for auto machine learning.
- A data science curriculum covering topics from data analysis to deep learning using Python, Spark, and TensorFlow.
- References to case studies of IBM PowerAI for insights on using the complete AI stack.
Data Science at Scale: Using Apache Spark for Data Science at BitlySarah Guido
Given at Data Day Seattle 2015.
Bitly generates over 9 billion clicks on shortened links a month, as well as over 100 million unique link shortens. Analyzing data of this scale is not without its challenges. At Bitly, we have started adopting Apache Spark as a way to process our data. In this talk, I’ll elaborate on how I use Spark as part of my data science workflow. I’ll cover how Spark fits into our existing architecture, the kind of problems I’m solving with Spark, and the benefits and challenges of using Spark for large-scale data science.
Big Data Management System: Smart SQL Processing Across Hadoop and your Data ...DataWorks Summit
The document discusses smart SQL processing for databases, Hadoop and beyond. It describes how Oracle teaches its database about Hadoop by publishing Hadoop metadata like SerDe, RecordReader and InputFormat information to Oracle's catalog. This allows SQL queries to be executed on Hadoop data. However, directly sending SQL queries to Hadoop data nodes presents bottlenecks, so the document discusses how Oracle makes SQL processing smarter by applying techniques like smart scan, storage indexing and caching utilized in Oracle Exadata to minimize data movement and improve performance.
In the past, emerging technologies took years to mature. In the case of big data, while effective tools are still emerging, the analytics requirements are changing rapidly resulting in businesses to either make it or be left behind
Getting started with GCP ( Google Cloud Platform)bigdata trunk
This document provides an overview and introduction to Google Cloud Platform (GCP). It begins with introductions and an agenda. It then discusses cloud computing concepts like deployment models and service models. It provides details on specific GCP computing, storage, machine learning, and other services. It describes how to set up Qwiklabs to do hands-on labs with GCP. Finally, it discusses next steps like training and certification for expanding GCP knowledge.
A session on Artificial Intelligence and Machine Learning for anyone and everyone.
Demystify the world of Artifical Intelligence and Machine Learning in a simple and fun way so that everyone can understand and use Machine learning.
Introduction of Artificial Intelligence and Machine Learning bigdata trunk
A Workshop to introduce Artificial Intelligence and Machine Learning for beginners. It starts with basics , terminologies and concepts for machine learning, compares with deep learning and artificial Intelligence. Highlights the ML and AI offerings like Jupyter Notebook, Azure ML , Amazon Sagemaker, Tensorflow etc.
A guide to understanding the coding interview process at top tech companies like Google, Facebook or a unicorn startup like Uber.
Checkout our Bootcamps to help in coding, Data structures and algorithms, behavior and situational interview
http://programminginterviewprep.com/
Big Data Ecosystem after Spark as part of session hosted by Big data Trunk (www.BigDataTrunk.com) for below Meetup group
https://www.meetup.com/Big-Data-IOT-101/
You can subscribe to our channel and see other videos at
https://www.youtube.com/channel/UCp7pR7BJNnRueEuLSau0TzA
Introduction to machine learning algorithmsbigdata trunk
Introduction to main Machine Learning Algorithms as part of session hosted by Big data Trunk (www.BigDataTrunk.com) for below Meetup group
https://www.meetup.com/Big-Data-IOT-101/
Presented by Antony Ross
You can subscribe to our channel and see other videos at
https://www.youtube.com/channel/UCp7pR7BJNnRueEuLSau0TzA
Data Science Process Walkthrough as part of session hosted by Big data Trunk (www.BigDataTrunk.com) for below Meetup group
https://www.meetup.com/Big-Data-IOT-101/
Presented by Antony Ross
Machine Learning Intro for Anyone and Everyonebigdata trunk
A fun and math free introduction to Machine Learning. It provides a step to step approach for everyone to get started with Machine Learning using Microsoft Azure ML
This was presented at
https://www.siliconvalley-codecamp.com/Session/2017/machine-learning-intro-for-anyone-and-everyone
You can subscribe to our channel and see other videos at
https://www.youtube.com/channel/UCp7pR7BJNnRueEuLSau0TzA
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!SOFTTECHHUB
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
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
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
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).
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
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.
Infrastructure Challenges in Scaling RAG with Custom AI modelsZilliz
Building Retrieval-Augmented Generation (RAG) systems with open-source and custom AI models is a complex task. This talk explores the challenges in productionizing RAG systems, including retrieval performance, response synthesis, and evaluation. We’ll discuss how to leverage open-source models like text embeddings, language models, and custom fine-tuned models to enhance RAG performance. Additionally, we’ll cover how BentoML can help orchestrate and scale these AI components efficiently, ensuring seamless deployment and management of RAG systems in the cloud.
7. www.BigDataTrunk.com
What is Hadoop?
Hadoop is an open source framework for scalable fault-tolerant distributed
system to store and process the data across cluster of commodity hardware.
Hadoop Goals
§Scalable
§Economical
§Reliable
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