Jai Ranganathan, Senior Director of Product Management, discusses why Spark has experienced such wide adoption and provide a technical deep dive into the architecture. Additionally, he presents some use cases in production today. Finally, he shares our vision for the Hadoop ecosystem and why we believe Spark is the successor to MapReduce for Hadoop data processing.
http://bit.ly/1BTaXZP – Hadoop has been a huge success in the data world. It’s disrupted decades of data management practices and technologies by introducing a massively parallel processing framework. The community and the development of all the Open Source components pushed Hadoop to where it is now.
That's why the Hadoop community is excited about Apache Spark. The Spark software stack includes a core data-processing engine, an interface for interactive querying, Sparkstreaming for streaming data analysis, and growing libraries for machine-learning and graph analysis. Spark is quickly establishing itself as a leading environment for doing fast, iterative in-memory and streaming analysis.
This talk will give an introduction the Spark stack, explain how Spark has lighting fast results, and how it complements Apache Hadoop.
Keys Botzum - Senior Principal Technologist with MapR Technologies
Keys is Senior Principal Technologist with MapR Technologies, where he wears many hats. His primary responsibility is interacting with customers in the field, but he also teaches classes, contributes to documentation, and works with engineering teams. He has over 15 years of experience in large scale distributed system design. Previously, he was a Senior Technical Staff Member with IBM, and a respected author of many articles on the WebSphere Application Server as well as a book.
High concurrency, Low latency analytics using Spark/KuduChris George
With the right combination of open source projects, you can have a high concurrency and low latency spark jobs for doing data analysis. We'll show both REST and JDBC access to access data from a persistent spark context and then show how the combination of Spark Job Server, Spark Thrift Server and Apache Kudu can create a scalable backend for low latency analytics.
Presentation given for the SQLPass community at SQLBits XIV in Londen. The presentation is an overview about the performance improvements provided to Hive with the Stinger initiative.
http://bit.ly/1BTaXZP – Hadoop has been a huge success in the data world. It’s disrupted decades of data management practices and technologies by introducing a massively parallel processing framework. The community and the development of all the Open Source components pushed Hadoop to where it is now.
That's why the Hadoop community is excited about Apache Spark. The Spark software stack includes a core data-processing engine, an interface for interactive querying, Sparkstreaming for streaming data analysis, and growing libraries for machine-learning and graph analysis. Spark is quickly establishing itself as a leading environment for doing fast, iterative in-memory and streaming analysis.
This talk will give an introduction the Spark stack, explain how Spark has lighting fast results, and how it complements Apache Hadoop.
Keys Botzum - Senior Principal Technologist with MapR Technologies
Keys is Senior Principal Technologist with MapR Technologies, where he wears many hats. His primary responsibility is interacting with customers in the field, but he also teaches classes, contributes to documentation, and works with engineering teams. He has over 15 years of experience in large scale distributed system design. Previously, he was a Senior Technical Staff Member with IBM, and a respected author of many articles on the WebSphere Application Server as well as a book.
High concurrency, Low latency analytics using Spark/KuduChris George
With the right combination of open source projects, you can have a high concurrency and low latency spark jobs for doing data analysis. We'll show both REST and JDBC access to access data from a persistent spark context and then show how the combination of Spark Job Server, Spark Thrift Server and Apache Kudu can create a scalable backend for low latency analytics.
Presentation given for the SQLPass community at SQLBits XIV in Londen. The presentation is an overview about the performance improvements provided to Hive with the Stinger initiative.
Apache Drill and Zeppelin: Two Promising Tools You've Never Heard OfCharles Givre
Study after study shows that data preparation and other data janitorial work consume 50-90% of most data scientists’ time. Apache Drill is a very promising tool which can help address this. Drill works with many different forms of “self describing data” and allows analysts to run ad-hoc queries in ANSI SQL against that data. Unlike HIVE or other SQL on Hadoop tools, Drill is not a wrapper for Map-Reduce and can scale to clusters of up to 10k nodes.
Transitioning Compute Models: Hadoop MapReduce to SparkSlim Baltagi
This presentation is an analysis of the observed trends in the transition from the Hadoop ecosystem to the Spark ecosystem. The related talk took place at the Chicago Hadoop User Group (CHUG) meetup held on February 12, 2015.
This presentation discusses the follow topics
What is Hadoop?
Need for Hadoop
History of Hadoop
Hadoop Overview
Advantages and Disadvantages of Hadoop
Hadoop Distributed File System
Comparing: RDBMS vs. Hadoop
Advantages and Disadvantages of HDFS
Hadoop frameworks
Modules of Hadoop frameworks
Features of 'Hadoop‘
Hadoop Analytics Tools
Hive, Impala, and Spark, Oh My: SQL-on-Hadoop in Cloudera 5.5Cloudera, Inc.
Inefficient data workloads are all too common across enterprises - causing costly delays, breakages, hard-to-maintain complexity, and ultimately lost productivity. For a typical enterprise with multiple data warehouses, thousands of reports, and hundreds of thousands of ETL jobs being executed every day, this loss of productivity is a real problem. Add to all of this the complex handwritten SQL queries, and there can be nearly a million queries executed every month that desperately need to be optimized, especially to take advantage of the benefits of Apache Hadoop. How can enterprises dig through their workloads and inefficiencies to easily see which are the best fit for Hadoop and what’s the fastest path to get there?
Cloudera Navigator Optimizer is the solution - analyzing existing SQL workloads to provide instant insights into your workloads and turns that into an intelligent optimization strategy so you can unlock peak performance and efficiency with Hadoop. As the newest addition to Cloudera’s enterprise Hadoop platform, and now available in limited beta, Navigator Optimizer has helped customers profile over 1.5 million queries and ultimately save millions by optimizing for Hadoop.
Disaster Recovery and Cloud Migration for your Apache Hive WarehouseDataWorks Summit
As Apache Hadoop clusters become central to an organization’s operations, they have clusters in more than one data center. Historically, this has been largely driven by requirements of business continuity planning or geo localization. It has also recently been gaining a lot of interest from a hybrid cloud perspective, i.e. wherein people are trying to augment their traditional on-prem setup with cloud-based additions as well. A robust replication solution is a fundamental requirement in such cases.
The Apache Hive community has been working on new capabilities for efficient and fault tolerant replication of data in the Hive warehouse. In this talk, we will discuss these new capabilities, how it works, what replication at Hive-scale looks like, what challenges it poses, what we have done to solve those issues. We will also focus on what we need to be aware of in our use case that might make replication optimal.
Speaker
Sankar Hariappan, Senior Software Engineer, Hortonworks
Sherlock: an anomaly detection service on top of Druid DataWorks Summit
Sherlock is an anomaly detection service built on top of Druid. It leverages EGADS (Extensible Generic Anomaly Detection System; github.com/yahoo/egads) to detect anomalies in time-series data. Users can schedule jobs on an hourly, daily, weekly, or monthly basis, view anomaly reports from Sherlock's interface, or receive them via email.
Sherlock has four major components: timeseries generation, EGADS anomaly detection, Redis backend and Spark Java UI. Timeseries generation involves building, validating, querying, parsing the Druid query. Parsed Druid response is then fed to EGADS anomaly detection component which detects and generates the anomaly reports for each input time-series data. Sherlock uses Redis backend to store jobs metadata, generated anomaly reports and persistent job queue for scheduling jobs, etc. Users can choose to have a clustered Redis or standalone Redis. Sherlock provides user interface built with Spark Java. The UI enables users to submit instant anomaly analysis, create, and launch detection jobs, view anomalies on a heatmap and on a graph. Jigarkumar Patel, Software Development Engineer I, Oath Inc. and, David Servose, Software Systems Engineer, Oath
Hadoop World 2011: Hadoop and RDBMS with Sqoop and Other Tools - Guy Harrison...Cloudera, Inc.
As Hadoop graduates from pilot project to a mission critical component of the enterprise IT infrastructure, integrating information held in Hadoop and in Enterprise RDBMS becomes imperative. We’ll look at key scenarios driving Hadoop and RDBMS integration and review technical options. In particular, we’ll deep dive into the Apache SQOOP project, which expedites data movement between Hadoop and any JDBC database, as well as providing an framework which allows developers and vendors to create connectors optimized for specific targets such as Oracle, Netezza etc.
Securing data in hybrid environments using Apache RangerDataWorks Summit
Companies are increasingly moving to the cloud to store and process data. In this talk, we will talk through how companies can use tag-based policies in Apache Ranger to protect access to data both in on-premises environments as well in AWS-based cloud environments. We will go into details of how tag-based policies work and the integration with Apache Atlas and various services. We will also talk through how companies can leverage Ranger’s policies to anonymize or tokenize data while moving into the cloud and de-anonymize dynamically using Apache Kafka, Apache Hive, Apache Spark, or plain old ETL using MapReduce. We will also deep dive into Ranger’s proposed integration with S3 and other cloud-native systems. We will wrap it up with an end-to-end demo showing how tags and tag-based masking policies can be used to anonymize sensitive data and track how tags are propagated within the system and how sensitive data can be protected using tag-based policies
Speakers
Don Bosco Durai, Chief Security Architect, Privacera
Madhan Neethiraj, Sr. Director of Engineering, Hortonworks
This talk was held at the 11th meeting on April 7 2014 by Marcel Kornacker.
Impala (impala.io) raises the bar for SQL query performance on Apache Hadoop. With Impala, you can query Hadoop data – including SELECT, JOIN, and aggregate functions – in real time to do BI-style analysis. As a result, Impala makes a Hadoop-based enterprise data hub function like an enterprise data warehouse for native Big Data.
Let Spark Fly: Advantages and Use Cases for Spark on HadoopMapR Technologies
http://bit.ly/1BTaXZP – Apache Spark is currently one of the most active projects in the Hadoop ecosystem, and as such, there’s been plenty of hype about it in recent months, but how much of the discussion is marketing spin? And what are the facts? MapR and Databricks, the company that created and led the development of the Spark stack, will cut through the noise to uncover practical advantages for having the full set of Spark technologies at your disposal and reveal the benefits for running Spark on Hadoop
This presentation was given at a webinar hosted by Data Science Central and co-presented by MapR + Databricks.
To see the webinar, please go to: http://www.datasciencecentral.com/video/let-spark-fly-advantages-and-use-cases-for-spark-on-hadoop
Apache Drill and Zeppelin: Two Promising Tools You've Never Heard OfCharles Givre
Study after study shows that data preparation and other data janitorial work consume 50-90% of most data scientists’ time. Apache Drill is a very promising tool which can help address this. Drill works with many different forms of “self describing data” and allows analysts to run ad-hoc queries in ANSI SQL against that data. Unlike HIVE or other SQL on Hadoop tools, Drill is not a wrapper for Map-Reduce and can scale to clusters of up to 10k nodes.
Transitioning Compute Models: Hadoop MapReduce to SparkSlim Baltagi
This presentation is an analysis of the observed trends in the transition from the Hadoop ecosystem to the Spark ecosystem. The related talk took place at the Chicago Hadoop User Group (CHUG) meetup held on February 12, 2015.
This presentation discusses the follow topics
What is Hadoop?
Need for Hadoop
History of Hadoop
Hadoop Overview
Advantages and Disadvantages of Hadoop
Hadoop Distributed File System
Comparing: RDBMS vs. Hadoop
Advantages and Disadvantages of HDFS
Hadoop frameworks
Modules of Hadoop frameworks
Features of 'Hadoop‘
Hadoop Analytics Tools
Hive, Impala, and Spark, Oh My: SQL-on-Hadoop in Cloudera 5.5Cloudera, Inc.
Inefficient data workloads are all too common across enterprises - causing costly delays, breakages, hard-to-maintain complexity, and ultimately lost productivity. For a typical enterprise with multiple data warehouses, thousands of reports, and hundreds of thousands of ETL jobs being executed every day, this loss of productivity is a real problem. Add to all of this the complex handwritten SQL queries, and there can be nearly a million queries executed every month that desperately need to be optimized, especially to take advantage of the benefits of Apache Hadoop. How can enterprises dig through their workloads and inefficiencies to easily see which are the best fit for Hadoop and what’s the fastest path to get there?
Cloudera Navigator Optimizer is the solution - analyzing existing SQL workloads to provide instant insights into your workloads and turns that into an intelligent optimization strategy so you can unlock peak performance and efficiency with Hadoop. As the newest addition to Cloudera’s enterprise Hadoop platform, and now available in limited beta, Navigator Optimizer has helped customers profile over 1.5 million queries and ultimately save millions by optimizing for Hadoop.
Disaster Recovery and Cloud Migration for your Apache Hive WarehouseDataWorks Summit
As Apache Hadoop clusters become central to an organization’s operations, they have clusters in more than one data center. Historically, this has been largely driven by requirements of business continuity planning or geo localization. It has also recently been gaining a lot of interest from a hybrid cloud perspective, i.e. wherein people are trying to augment their traditional on-prem setup with cloud-based additions as well. A robust replication solution is a fundamental requirement in such cases.
The Apache Hive community has been working on new capabilities for efficient and fault tolerant replication of data in the Hive warehouse. In this talk, we will discuss these new capabilities, how it works, what replication at Hive-scale looks like, what challenges it poses, what we have done to solve those issues. We will also focus on what we need to be aware of in our use case that might make replication optimal.
Speaker
Sankar Hariappan, Senior Software Engineer, Hortonworks
Sherlock: an anomaly detection service on top of Druid DataWorks Summit
Sherlock is an anomaly detection service built on top of Druid. It leverages EGADS (Extensible Generic Anomaly Detection System; github.com/yahoo/egads) to detect anomalies in time-series data. Users can schedule jobs on an hourly, daily, weekly, or monthly basis, view anomaly reports from Sherlock's interface, or receive them via email.
Sherlock has four major components: timeseries generation, EGADS anomaly detection, Redis backend and Spark Java UI. Timeseries generation involves building, validating, querying, parsing the Druid query. Parsed Druid response is then fed to EGADS anomaly detection component which detects and generates the anomaly reports for each input time-series data. Sherlock uses Redis backend to store jobs metadata, generated anomaly reports and persistent job queue for scheduling jobs, etc. Users can choose to have a clustered Redis or standalone Redis. Sherlock provides user interface built with Spark Java. The UI enables users to submit instant anomaly analysis, create, and launch detection jobs, view anomalies on a heatmap and on a graph. Jigarkumar Patel, Software Development Engineer I, Oath Inc. and, David Servose, Software Systems Engineer, Oath
Hadoop World 2011: Hadoop and RDBMS with Sqoop and Other Tools - Guy Harrison...Cloudera, Inc.
As Hadoop graduates from pilot project to a mission critical component of the enterprise IT infrastructure, integrating information held in Hadoop and in Enterprise RDBMS becomes imperative. We’ll look at key scenarios driving Hadoop and RDBMS integration and review technical options. In particular, we’ll deep dive into the Apache SQOOP project, which expedites data movement between Hadoop and any JDBC database, as well as providing an framework which allows developers and vendors to create connectors optimized for specific targets such as Oracle, Netezza etc.
Securing data in hybrid environments using Apache RangerDataWorks Summit
Companies are increasingly moving to the cloud to store and process data. In this talk, we will talk through how companies can use tag-based policies in Apache Ranger to protect access to data both in on-premises environments as well in AWS-based cloud environments. We will go into details of how tag-based policies work and the integration with Apache Atlas and various services. We will also talk through how companies can leverage Ranger’s policies to anonymize or tokenize data while moving into the cloud and de-anonymize dynamically using Apache Kafka, Apache Hive, Apache Spark, or plain old ETL using MapReduce. We will also deep dive into Ranger’s proposed integration with S3 and other cloud-native systems. We will wrap it up with an end-to-end demo showing how tags and tag-based masking policies can be used to anonymize sensitive data and track how tags are propagated within the system and how sensitive data can be protected using tag-based policies
Speakers
Don Bosco Durai, Chief Security Architect, Privacera
Madhan Neethiraj, Sr. Director of Engineering, Hortonworks
This talk was held at the 11th meeting on April 7 2014 by Marcel Kornacker.
Impala (impala.io) raises the bar for SQL query performance on Apache Hadoop. With Impala, you can query Hadoop data – including SELECT, JOIN, and aggregate functions – in real time to do BI-style analysis. As a result, Impala makes a Hadoop-based enterprise data hub function like an enterprise data warehouse for native Big Data.
Let Spark Fly: Advantages and Use Cases for Spark on HadoopMapR Technologies
http://bit.ly/1BTaXZP – Apache Spark is currently one of the most active projects in the Hadoop ecosystem, and as such, there’s been plenty of hype about it in recent months, but how much of the discussion is marketing spin? And what are the facts? MapR and Databricks, the company that created and led the development of the Spark stack, will cut through the noise to uncover practical advantages for having the full set of Spark technologies at your disposal and reveal the benefits for running Spark on Hadoop
This presentation was given at a webinar hosted by Data Science Central and co-presented by MapR + Databricks.
To see the webinar, please go to: http://www.datasciencecentral.com/video/let-spark-fly-advantages-and-use-cases-for-spark-on-hadoop
With Search, developers and data engineers can run more relevant and responsive queries on the data in Hadoop and integrate with external tools to build custom real-time applications.
Big Data Governance in Hadoop Environments with Cloudera Navigatorfeb2017meetuEmre Sevinç
Big Data Governance in Hadoop Environments with Cloudera Navigator | Cloudera Belgium User Group Meet-up, February 2017
https://www.meetup.com/Belgium-Cloudera-User-Group/events/235325905/
Gremlin is a Turing-complete, graph-based programming language developed for key/value-pair multi-relational graphs called property graphs. Gremlin makes extensive use of XPath 1.0 to support complex graph traversals. Connectors exist to various graph databases and frameworks. This language has application in the areas of graph query, analysis, and manipulation.
This deck covers key considerations and provides advice for enterprises looking to run production-scale Cloudera on AWS. We touch on everything from security to governance to selecting the right instance type for your Hadoop workload (Spark, Impala, Search, etc).
The Cloudera Impala project is pioneering the next generation of Hadoop capabilities: the convergence of interactive SQL queries with the capacity, scalability, and flexibility of a Hadoop cluster. In this webinar, join Cloudera and MicroStrategy to learn how Impala works, how it is uniquely architected to provide an interactive SQL experience native to Hadoop, and how you can leverage the power of MicroStrategy 9.3.1 to easily tap into more data and make new discoveries.
Apache Spark presentation at HasGeek FifthElelephant
https://fifthelephant.talkfunnel.com/2015/15-processing-large-data-with-apache-spark
Covering Big Data Overview, Spark Overview, Spark Internals and its supported libraries
Doug Cutting discusses:
- A brief history of Spark and its rise in popularity across developers and enterprises
- Spark's advantages over MapReduce
- The One Platform Initiative and the roadmap for Spark
- The future of data processing in Hadoop
Designing & Optimizing Micro Batching Systems Using 100+ Nodes (Ananth Ram, R...DataStax
Designing & Optimizing micro batch processing system to handle multi-billion events using 100+ nodes of Cassandra , spark and Kafka - Lessons learned from the trenches
Designing and Optimizing 20+ billion operations a day presents a set of complex challenges especially when the SLA is near real-time. In this presentation we will walk through our experience in building large scale event processing pipeline using Cassandra , spark streaming and kafka using 100+ nodes. We will present the Design patterns, development steps and diagnostics setups at the technology level and application level that are needed to manage the application of this scale. We also aim to present some unique problems we encountered in optimizing and operationalizing these environments.
About the Speakers
Ananth Ram Senior Principal / Senior Manager, Accenture
Ananth Ram is a Solution Architect with over 17 years of experience in Oracle database Architecture and designing large scale applications. He was with Oracle Corp for nine years before joining Accenture as Senior Principal . As a part of Accenture, Ananth has been working on many large scale Oracle and big data initiatives in the last four years.
Rich Rein Solution Architect, DataStax
Rich Rein is a Solutions Architect from DataStax on Accenture team with over 30+ years as an architect, manager, and consultant in Silicon Valley's computing industry.
Rumeel Kazi, Accenture Federal
Rumeel Kazi is a Senior Manager in the Accenture Health & Public Service (H&PS) practice. He has over 17 years of Systems Integration implementation experience involving Oracle, J2EE platforms, Enterprise Application Integration, Supply Chain, ETL and Business Rules Management Systems. Rumeel has been working on large scale Oracle and big data application solutions since the last 5 years.
This is the talk I gave at the Seattle Spark Meetup in March, 2015. I discussed some Spark Streaming fundamentals, integration points with Kafka, Flume etc.
Apache Spark has emerged over the past year as the imminent successor to Hadoop MapReduce. Spark can process data in memory at very high speed, while still be able to spill to disk if required. Spark’s powerful, yet flexible API allows users to write complex applications very easily without worrying about the internal workings and how the data gets processed on the cluster.
Spark comes with an extremely powerful Streaming API to process data as it is ingested. Spark Streaming integrates with popular data ingest systems like Apache Flume, Apache Kafka, Amazon Kinesis etc. allowing users to process data as it comes in.
In this talk, Hari will discuss the basics of Spark Streaming, its API and its integration with Flume, Kafka and Kinesis. Hari will also discuss a real-world example of a Spark Streaming application, and how code can be shared between a Spark application and a Spark Streaming application. Each stage of the application execution will be presented, which can help understand practices while writing such an application. Hari will finally discuss how to write a custom application and a custom receiver to receive data from other systems.
Real Time Data Processing using Spark Streaming | Data Day Texas 2015Cloudera, Inc.
Speaker: Hari Shreedharan
Data Day Texas 2015
Apache Spark has emerged over the past year as the imminent successor to Hadoop MapReduce. Spark can process data in memory at very high speed, while still be able to spill to disk if required. Spark’s powerful, yet flexible API allows users to write complex applications very easily without worrying about the internal workings and how the data gets processed on the cluster.
Spark comes with an extremely powerful Streaming API to process data as it is ingested. Spark Streaming integrates with popular data ingest systems like Apache Flume, Apache Kafka, Amazon Kinesis etc. allowing users to process data as it comes in.
In this talk, Hari will discuss the basics of Spark Streaming, its API and its integration with Flume, Kafka and Kinesis. Hari will also discuss a real-world example of a Spark Streaming application, and how code can be shared between a Spark application and a Spark Streaming application. Each stage of the application execution will be presented, which can help understand practices while writing such an application. Hari will finally discuss how to write a custom application and a custom receiver to receive data from other systems.
Large Scale Data Analytics with Spark and Cassandra on the DSE PlatformDataStax Academy
In this talk will show how Large Scale Data Analytics can be done with Spark and Cassandra on the DataStax Enterprise Platform. First we will give an overview of what is the Spark Cassandra Connector and how it enables working with large data sets. Then we will use the Spark Notebook to show live examples in the browser of interacting with the data. The example will load a large Movies Database from Cassandra into Spark and then show how that data can be transformed and analyzed using Spark.
Jump Start with Apache Spark 2.0 on DatabricksAnyscale
Apache Spark 2.x has laid the foundation for many new features and functionality. Its main three themes—easier, faster, and smarter—are pervasive in its unified and simplified high-level APIs for Structured data.
In this introductory part lecture and part hands-on workshop you’ll learn how to apply some of these new APIs using Databricks Community Edition. In particular, we will cover the following areas:
Apache Spark Fundamentals & Concepts
What’s new in Spark 2.x
SparkSessions vs SparkContexts
Datasets/Dataframes and Spark SQL
Introduction to Structured Streaming concepts and APIs
Spark Streaming& Kafka-The Future of Stream Processing by Hari Shreedharan of...Data Con LA
Abstract:-
With its easy to use interfaces and native integration with some of the most popular ingest tools, such as Kafka, Flume, Kinesis etc, Spark Streaming has become go-to tool for stream processing. Code sharing with Spark also makes it attractive. In this talk, we will discuss the latest features in Spark Streaming and how it integrates with Kafka natively with no data loss, and even do exactly once processing!
Bio:-
Hari Shreedharan is a PMC member and committer on the Apache Flume Project. As a PMC member, he is involved in making decisions on the direction of the project. Author of the O’Reilly book Using Flume, Hari is also a software engineer at Cloudera, where he works on Apache Flume, Apache Spark, and Apache Sqoop. He also ensures that customers can successfully deploy and manage Flume, Spark, and Sqoop on their clusters, by helping them resolve any issues they are facing.
Spark Streaming & Kafka-The Future of Stream ProcessingJack Gudenkauf
Hari Shreedharan/Cloudera @Playtika. With its easy to use interfaces and native integration with some of the most popular ingest tools, such as Kafka, Flume, Kinesis etc, Spark Streaming has become go-to tool for stream processing. Code sharing with Spark also makes it attractive. In this talk, we will discuss the latest features in Spark Streaming and how it integrates with Kafka natively with no data loss, and even do exactly once processing!
Similar to The Future of Hadoop: A deeper look at Apache Spark (20)
Cloudera Data Impact Awards 2021 - Finalists Cloudera, Inc.
This annual program recognizes organizations who are moving swiftly towards the future and building innovative solutions by making what was impossible yesterday, possible today.
The winning organizations' implementations demonstrate outstanding achievements in fulfilling their mission, technical advancement, and overall impact.
The 2021 Data Impact Awards recognize organizations' achievements with the Cloudera Data Platform in seven categories:
Data Lifecycle Connection
Data for Enterprise AI
Cloud Innovation
Security & Governance Leadership
People First
Data for Good
Industry Transformation
2020 Cloudera Data Impact Awards FinalistsCloudera, Inc.
Cloudera is proud to present the 2020 Data Impact Awards Finalists. This annual program recognizes organizations running the Cloudera platform for the applications they've built and the impact their data projects have on their organizations, their industries, and the world. Nominations were evaluated by a panel of independent thought-leaders and expert industry analysts, who then selected the finalists and winners. Winners exemplify the most-cutting edge data projects and represent innovation and leadership in their respective industries.
Machine Learning with Limited Labeled Data 4/3/19Cloudera, Inc.
Cloudera Fast Forward Labs’ latest research report and prototype explore learning with limited labeled data. This capability relaxes the stringent labeled data requirement in supervised machine learning and opens up new product possibilities. It is industry invariant, addresses the labeling pain point and enables applications to be built faster and more efficiently.
Data Driven With the Cloudera Modern Data Warehouse 3.19.19Cloudera, Inc.
In this session, we will cover how to move beyond structured, curated reports based on known questions on known data, to an ad-hoc exploration of all data to optimize business processes and into the unknown questions on unknown data, where machine learning and statistically motivated predictive analytics are shaping business strategy.
Introducing Cloudera DataFlow (CDF) 2.13.19Cloudera, Inc.
Watch this webinar to understand how Hortonworks DataFlow (HDF) has evolved into the new Cloudera DataFlow (CDF). Learn about key capabilities that CDF delivers such as -
-Powerful data ingestion powered by Apache NiFi
-Edge data collection by Apache MiNiFi
-IoT-scale streaming data processing with Apache Kafka
-Enterprise services to offer unified security and governance from edge-to-enterprise
Introducing Cloudera Data Science Workbench for HDP 2.12.19Cloudera, Inc.
Cloudera’s Data Science Workbench (CDSW) is available for Hortonworks Data Platform (HDP) clusters for secure, collaborative data science at scale. During this webinar, we provide an introductory tour of CDSW and a demonstration of a machine learning workflow using CDSW on HDP.
Shortening the Sales Cycle with a Modern Data Warehouse 1.30.19Cloudera, Inc.
Join Cloudera as we outline how we use Cloudera technology to strengthen sales engagement, minimize marketing waste, and empower line of business leaders to drive successful outcomes.
Leveraging the cloud for analytics and machine learning 1.29.19Cloudera, Inc.
Learn how organizations are deriving unique customer insights, improving product and services efficiency, and reducing business risk with a modern big data architecture powered by Cloudera on Azure. In this webinar, you see how fast and easy it is to deploy a modern data management platform—in your cloud, on your terms.
Modernizing the Legacy Data Warehouse – What, Why, and How 1.23.19Cloudera, Inc.
Join us to learn about the challenges of legacy data warehousing, the goals of modern data warehousing, and the design patterns and frameworks that help to accelerate modernization efforts.
Leveraging the Cloud for Big Data Analytics 12.11.18Cloudera, Inc.
Learn how organizations are deriving unique customer insights, improving product and services efficiency, and reducing business risk with a modern big data architecture powered by Cloudera on AWS. In this webinar, you see how fast and easy it is to deploy a modern data management platform—in your cloud, on your terms.
Explore new trends and use cases in data warehousing including exploration and discovery, self-service ad-hoc analysis, predictive analytics and more ways to get deeper business insight. Modern Data Warehousing Fundamentals will show how to modernize your data warehouse architecture and infrastructure for benefits to both traditional analytics practitioners and data scientists and engineers.
Explore new trends and use cases in data warehousing including exploration and discovery, self-service ad-hoc analysis, predictive analytics and more ways to get deeper business insight. Modern Data Warehousing Fundamentals will show how to modernize your data warehouse architecture and infrastructure for benefits to both traditional analytics practitioners and data scientists and engineers.
Explore new trends and use cases in data warehousing including exploration and discovery, self-service ad-hoc analysis, predictive analytics and more ways to get deeper business insight. Modern Data Warehousing Fundamentals will show how to modernize your data warehouse architecture and infrastructure for benefits to both traditional analytics practitioners and data scientists and engineers.
Extending Cloudera SDX beyond the PlatformCloudera, Inc.
Cloudera SDX is by no means no restricted to just the platform; it extends well beyond. In this webinar, we show you how Bardess Group’s Zero2Hero solution leverages the shared data experience to coordinate Cloudera, Trifacta, and Qlik to deliver complete customer insight.
Federated Learning: ML with Privacy on the Edge 11.15.18Cloudera, Inc.
Join Cloudera Fast Forward Labs Research Engineer, Mike Lee Williams, to hear about their latest research report and prototype on Federated Learning. Learn more about what it is, when it’s applicable, how it works, and the current landscape of tools and libraries.
Analyst Webinar: Doing a 180 on Customer 360Cloudera, Inc.
451 Research Analyst Sheryl Kingstone, and Cloudera’s Steve Totman recently discussed how a growing number of organizations are replacing legacy Customer 360 systems with Customer Insights Platforms.
Build a modern platform for anti-money laundering 9.19.18Cloudera, Inc.
In this webinar, you will learn how Cloudera and BAH riskCanvas can help you build a modern AML platform that reduces false positive rates, investigation costs, technology sprawl, and regulatory risk.
Introducing the data science sandbox as a service 8.30.18Cloudera, Inc.
How can companies integrate data science into their businesses more effectively? Watch this recorded webinar and demonstration to hear more about operationalizing data science with Cloudera Data Science Workbench on Cazena’s fully-managed cloud platform.
AI Pilot Review: The World’s First Virtual Assistant Marketing SuiteGoogle
AI Pilot Review: The World’s First Virtual Assistant Marketing Suite
👉👉 Click Here To Get More Info 👇👇
https://sumonreview.com/ai-pilot-review/
AI Pilot Review: Key Features
✅Deploy AI expert bots in Any Niche With Just A Click
✅With one keyword, generate complete funnels, websites, landing pages, and more.
✅More than 85 AI features are included in the AI pilot.
✅No setup or configuration; use your voice (like Siri) to do whatever you want.
✅You Can Use AI Pilot To Create your version of AI Pilot And Charge People For It…
✅ZERO Manual Work With AI Pilot. Never write, Design, Or Code Again.
✅ZERO Limits On Features Or Usages
✅Use Our AI-powered Traffic To Get Hundreds Of Customers
✅No Complicated Setup: Get Up And Running In 2 Minutes
✅99.99% Up-Time Guaranteed
✅30 Days Money-Back Guarantee
✅ZERO Upfront Cost
See My Other Reviews Article:
(1) TubeTrivia AI Review: https://sumonreview.com/tubetrivia-ai-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
Code reviews are vital for ensuring good code quality. They serve as one of our last lines of defense against bugs and subpar code reaching production.
Yet, they often turn into annoying tasks riddled with frustration, hostility, unclear feedback and lack of standards. How can we improve this crucial process?
In this session we will cover:
- The Art of Effective Code Reviews
- Streamlining the Review Process
- Elevating Reviews with Automated Tools
By the end of this presentation, you'll have the knowledge on how to organize and improve your code review proces
Custom Healthcare Software for Managing Chronic Conditions and Remote Patient...Mind IT Systems
Healthcare providers often struggle with the complexities of chronic conditions and remote patient monitoring, as each patient requires personalized care and ongoing monitoring. Off-the-shelf solutions may not meet these diverse needs, leading to inefficiencies and gaps in care. It’s here, custom healthcare software offers a tailored solution, ensuring improved care and effectiveness.
E-commerce Application Development Company.pdfHornet Dynamics
Your business can reach new heights with our assistance as we design solutions that are specifically appropriate for your goals and vision. Our eCommerce application solutions can digitally coordinate all retail operations processes to meet the demands of the marketplace while maintaining business continuity.
Software Engineering, Software Consulting, Tech Lead, Spring Boot, Spring Cloud, Spring Core, Spring JDBC, Spring Transaction, Spring MVC, OpenShift Cloud Platform, Kafka, REST, SOAP, LLD & HLD.
Navigating the Metaverse: A Journey into Virtual Evolution"Donna Lenk
Join us for an exploration of the Metaverse's evolution, where innovation meets imagination. Discover new dimensions of virtual events, engage with thought-provoking discussions, and witness the transformative power of digital realms."
May Marketo Masterclass, London MUG May 22 2024.pdfAdele Miller
Can't make Adobe Summit in Vegas? No sweat because the EMEA Marketo Engage Champions are coming to London to share their Summit sessions, insights and more!
This is a MUG with a twist you don't want to miss.
OpenMetadata Community Meeting - 5th June 2024OpenMetadata
The OpenMetadata Community Meeting was held on June 5th, 2024. In this meeting, we discussed about the data quality capabilities that are integrated with the Incident Manager, providing a complete solution to handle your data observability needs. Watch the end-to-end demo of the data quality features.
* How to run your own data quality framework
* What is the performance impact of running data quality frameworks
* How to run the test cases in your own ETL pipelines
* How the Incident Manager is integrated
* Get notified with alerts when test cases fail
Watch the meeting recording here - https://www.youtube.com/watch?v=UbNOje0kf6E
Top Features to Include in Your Winzo Clone App for Business Growth (4).pptxrickgrimesss22
Discover the essential features to incorporate in your Winzo clone app to boost business growth, enhance user engagement, and drive revenue. Learn how to create a compelling gaming experience that stands out in the competitive market.
Enterprise Resource Planning System includes various modules that reduce any business's workload. Additionally, it organizes the workflows, which drives towards enhancing productivity. Here are a detailed explanation of the ERP modules. Going through the points will help you understand how the software is changing the work dynamics.
To know more details here: https://blogs.nyggs.com/nyggs/enterprise-resource-planning-erp-system-modules/
Do you want Software for your Business? Visit Deuglo
Deuglo has top Software Developers in India. They are experts in software development and help design and create custom Software solutions.
Deuglo follows seven steps methods for delivering their services to their customers. They called it the Software development life cycle process (SDLC).
Requirement — Collecting the Requirements is the first Phase in the SSLC process.
Feasibility Study — after completing the requirement process they move to the design phase.
Design — in this phase, they start designing the software.
Coding — when designing is completed, the developers start coding for the software.
Testing — in this phase when the coding of the software is done the testing team will start testing.
Installation — after completion of testing, the application opens to the live server and launches!
Maintenance — after completing the software development, customers start using the software.
Atelier - Innover avec l’IA Générative et les graphes de connaissancesNeo4j
Atelier - Innover avec l’IA Générative et les graphes de connaissances
Allez au-delà du battage médiatique autour de l’IA et découvrez des techniques pratiques pour utiliser l’IA de manière responsable à travers les données de votre organisation. Explorez comment utiliser les graphes de connaissances pour augmenter la précision, la transparence et la capacité d’explication dans les systèmes d’IA générative. Vous partirez avec une expérience pratique combinant les relations entre les données et les LLM pour apporter du contexte spécifique à votre domaine et améliorer votre raisonnement.
Amenez votre ordinateur portable et nous vous guiderons sur la mise en place de votre propre pile d’IA générative, en vous fournissant des exemples pratiques et codés pour démarrer en quelques minutes.
Zoom is a comprehensive platform designed to connect individuals and teams efficiently. With its user-friendly interface and powerful features, Zoom has become a go-to solution for virtual communication and collaboration. It offers a range of tools, including virtual meetings, team chat, VoIP phone systems, online whiteboards, and AI companions, to streamline workflows and enhance productivity.
Utilocate offers a comprehensive solution for locate ticket management by automating and streamlining the entire process. By integrating with Geospatial Information Systems (GIS), it provides accurate mapping and visualization of utility locations, enhancing decision-making and reducing the risk of errors. The system's advanced data analytics tools help identify trends, predict potential issues, and optimize resource allocation, making the locate ticket management process smarter and more efficient. Additionally, automated ticket management ensures consistency and reduces human error, while real-time notifications keep all relevant personnel informed and ready to respond promptly.
The system's ability to streamline workflows and automate ticket routing significantly reduces the time taken to process each ticket, making the process faster and more efficient. Mobile access allows field technicians to update ticket information on the go, ensuring that the latest information is always available and accelerating the locate process. Overall, Utilocate not only enhances the efficiency and accuracy of locate ticket management but also improves safety by minimizing the risk of utility damage through precise and timely locates.
The Future of Hadoop: A deeper look at Apache Spark
1. 1
An Introduction to Spark
Jai Ranganathan, Senior Director Product Management, Cloudera
Denny Lee, Senior Director Data Sciences Engineering, Concur
16. 16
About Concur
What do we do?
• Leading provider of spend management solutions and (Travel,
Invoice, TripIt, etc.) services in the world
• Global customer base of 20,000 clients and 25 million users
• Processing more than $50 Billion in Travel & Expense (T&E)
spend each year
17. 17
About the Speaker
Who Am I?
• Long time SQL Server BI guy
(24TB Yahoo! Cube)
• Project Isotope (Hadoop on
Windows and Azure)
• At Concur, helping with Big
Data and Data Sciences
18. 18
A long time ago…
• We started using Hadoop because
• It was free
• i.e. Didn’t want to pay for a big data warehouse
• Could slowly extract from hundreds of relational data sources, consolidate it, and query it
• We were not thinking about advanced analytics
• We were thinking …. “cheaper reporting”
• We have some hardware lying around … let’s cobble it together and now we have reports
21. Can quickly switch to map mode and determine where most itineraries are from in 2013
21
22. 22
Or even quickly map out the airport locations on a map to see that Sun Moon
Lake Airport is in the center of Taiwan
23. 23
Starbucks Store #3313
601 108th Ave NE
Bellevue, WA (425) 646-9602
-------------------------------
Chk 713452
05/14/2014 11:04 AM
1961558 Drawer: 1 Reg: 1
-------------------------------
Bacon Art Brkfst 3.45
Warmed
T1 Latte 2.70
Triple 1.50
Soy 0.60
Gr Vanilla Mac 4.15
Reload Card 50.00
AMEX $50.00
XXXXXXXXXXXXXXXXXX1004
SBUX Card $13.56
SUBTOTAL $62.40
New Caffe Espresso
Frappuccino(R) Blended beverage
Our Signature
Frappuccino(R) roast coffee and
fresh milk, blended with ice.
Topped with our new espresso
whipped cream and new
Italian roast drizzle
Expense Categorization
One of my receipts that I had OCRed
One of the issues we’re trying to solve
is to auto-categorize this, so how
can we do this?
Below is a simplistic solution using
WordCount
Note, a real solution should involve
machine learning algorithms
24. 24
Spark assembly has been built with Hive, including Datanucleus jars on classpath
Welcome to
____ __
/ __/__ ___ _____/ /__
_ / _ / _ `/ __/ '_/
/___/ .__/_,_/_/ /_/_ version 1.1.0
/_/
Using Scala version 2.10.4 (Java HotSpot(TM) 64-Bit Server VM, Java 1.7.0_45)
Type in expressions to have them evaluated.
Type :help for more information.
2014-09-07 22:31:21.064 java[1871:15527] Unable to load realm info from SCDynamicStore
14/09/07 22:31:21 WARN NativeCodeLoader: Unable to load native-hadoop library for your
platform... using builtin-java classes where applicable
Spark context available as sc.
scala> val receipt = sc.textFile("/usr/local/Cellar/workspace/data/receipt/receipt.txt")
receipt: org.apache.spark.rdd.RDD[String] =
/usr/local/Cellar/workspace/data/receipt/receipt.txt MappedRDD[1] at textFile at
<console>:12
scala> receipt.count
res0: Long = 30
25. 25
scala> val words = receipt.flatMap(_.split(" "))
words: org.apache.spark.rdd.RDD[String] = FlatMappedRDD[2] at flatMap at <console>:14
scala> words.count
res1: Long = 161
scala> words.distinct.count
res2: Long = 72
scala> val wordCounts = words.map(x => (x, 1)).reduceByKey(_ + _).map{case(x,y) =>
(y,x)}.sortByKey(false).map{case(i,j) => (j, i)}
wordCounts: org.apache.spark.rdd.RDD[(String, Int)] = MappedRDD[12] at map at <console>:16
scala> wordCounts.take(12)
res5: Array[(String, Int)] = Array(("",82), (with,2), (Card,2), (new,2), (----------------
---------------,2), (Frappuccino(R),2), (roast,2), (1,2), (and,2), (New,1), (Topped,1),
(Starbucks,1))
Cloudera’s enterprise data hub (powered by Hadoop) is a data management platform that provides a unique offering that’s unified, compliance-ready, accessible, and open.
This enterprise data hub bring everything together in one unified layer. No copying of data. Simply one single transparent view that allows you to easily meet auditing and compliance goals.
It offers a single, unified solution for:
Storage & serialization
Data ingest & egress
Security & governance
Metadata
Resource management
It’s compliance-ready for security and governance and includes:
Authentication, authorization, encryption, audit, RBAC, lineage
Single interface with integrated controls
It’s accessible through:
Multiple frameworks
Familiar tools and skills
And it’s completely open:
100% open source Apache licensed platform
Extensible to 3rd party frameworks
Zero lock-in platform
As mentioned, Cloudera’s enterprise data hub has multiple different frameworks integrated into the platform for robust querying. One of the newest and most exciting querying frameworks is Spark, an open source, flexible data processing framework for machine learning and stream processing. Before we dive into Spark, we need to understand why Spark is necessary. And that requires an understanding of MapReduce
Key idea: add “variables” to the “functions” in functional programming
This is for a 29 GB dataset on 20 EC2 m1.xlarge machines (4 cores each)