Hive warehouse connector

•Download as PPTX, PDF•

1 like•96 views

Spark Hive Warehouse Connector For HDP3. A library to read/write DataFrames and Streaming DataFrames to/from Apache Hive™ using LLAP. You can use the Hive Warehouse Connector (HWC) API to access any type of table in the Hive catalog from Spark.

Technology

Confidential – Restricted
Tamil Selvan Kandasamy, Senior Customer Operations Engineer
- Configuring Spark Hive Warehouse Connector
- Debugging LLAP Issues

© Cloudera, Inc. All rights reserved. 2
HiveWarehouseConnector
A library to read/write DataFrames and Streaming DataFrames to/from Apache Hive™ using LLAP
You can use the Hive Warehouse Connector (HWC) API to access any type of table in the Hive catalog from
Spark.
When you use SparkSQL, standard Spark APIs access tables in the Spark catalog

© Cloudera, Inc. All rights reserved. 3
HiveWarehouseConnector - Continued
The Hive Warehouse Connector supports the following applications:
● Spark shell
● PySpark
● The spark-submit script
Does Ranger come into picture?
Yes, it does. Both row and column access control when the data in accessed from Spark

© Cloudera, Inc. All rights reserved. 4
HiveWarehouseConnector - Setting things up
You must add several Spark properties through spark-2-defaults in Ambari to use the Hive Warehouse
Connector for accessing data in Hive. Alternatively, configuration can be provided for each job using hive --
conf
Property Description Comments
spark.sql.hive.hiveserver2.jdbc.url URL for HiveServer2 Interactive In Ambari, copy the value from Services > Hive >
Summary > HIVESERVER2 INTERACTIVE JDBC URL
spark.datasource.hive.warehouse.metastoreUri URI for metastore Copy the value from hive.metastore.uris. For
example,thrift://mycluster-1.com:9083
spark.datasource.hive.warehouse.load.staging.dir HDFS temp directory for batch writes to Hive For example, /tmp
spark.hadoop.hive.llap.daemon.service.hosts Application name for LLAP service Copy value from Advanced hive-interactive-site
>hive.llap.daemon.service.hosts
spark.hadoop.hive.zookeeper.quorum Zookeeper hosts used by LLAP Copy value from Advanced hive-
sitehive.zookeeper.quorum

© Cloudera, Inc. All rights reserved. 5
HiveWarehouseConnector - Making a connection
Run following code in scala shell to view the table data
Scala> import com.hortonworks.hwc.HiveWarehouseSession
val hive = HiveWarehouseSession.session(spark).build()
hive.execute("show tables").show
hive.executeQuery("select * from employee").show

What's hot

AWS - Beanstalk Fundamentals

Piyush Agrawal

AWS database services

Nagesh Ramamoorthy

AWS Services - Part 1

Sivakumar Ramar

The Role of Elastic Load Balancer - Apache Stratos

Imesh Gunaratne

Spring Cloud Config

Theerut Bunkhanphol

Shipping logs to splunk from a container in aws howto

Екатерина Задорожная

Amazon S3 connector

Thang Loi

8 Source Code Cloudstack Developer Day

Kimihiko Kitase

AWS Elastic Beanstalk is the fastest and simplest way to get an application up and running on Amazon Web Services. Developers can simply upload their application code and the service automatically handles all the details such as resource provisioning, load balancing, auto-scaling, and monitoring. This session shows you how to connect your Git repository with Amazon Web Services, deploy your code to AWS Elastic Beanstalk, easily enable or disable application functionality, and perform zero-downtime deployments through interactive demos and code samples. Timothee Cruse, Solutions Architect, Amazon Web Services, ASEAN

Agile Deployment using Git and AWS Elastic Beanstalk

Amazon Web Services

Aws landing zone. journey to the cloud

Екатерина Задорожная

Running and managing large scale applications with microservices architectures can be difficult and often requires operating complex container management infrastructure. Amazon EC2 Container Service (ECS) is a highly scalable, high performance service for running and managing Docker applications. In this session, we will walk through a number of patterns and tools used by our customers to run their applications on Amazon ECS. We will show you how to setup, manage, and scale your Amazon ECS resources, and keep them secure. We will also discuss how to monitor your containerized application running on Amazon ECS and we'll provide best practices for logging and service discovery. AWS DevDay San Francisco, June 21, 2016. Presenter: Konstantin Williams, Solutions Architect

Amazon ECS Deep Dive

Amazon Web Services

20171122 aws usergrp_coretech-spn-cicd-aws-v01

Scott Miao

Hochverfügbarkeit mit MariaDB Enterprise - MariaDB Roadshow Summer 2014 Hambu...

MariaDB Corporation

Ansible Automation for Oracle RMAN / Apex Restores

Abhilash Kumar

6 Roadmap Cloudstack Developer Day

Kimihiko Kitase

Apache Ambari BOF - Overview - Hadoop Summit 2013

Hortonworks

Elastic Load Balancing automatically distributes incoming application traffic across multiple Amazon EC2 instances for fault tolerance and load distribution. In this session, we go into detail about Elastic Load Balancing's configuration and day-to-day management, as well as its use in conjunction with Auto Scaling. We explain how to make decisions about the service's many customization choices. We also share best practices and useful tips for success.

Elastic Load Balancing Deep Dive and Best Practices - Pop-up Loft Tel Aviv

Amazon Web Services

Auto Retweets Using AWS Lambda

CodeOps Technologies LLP

Serverless design considerations for Cloud Native workloads

Tensult

Deploying, configuring, and managing large Apache Hadoop and HBase clusters can be quite complex. Once you have your clusters, keeping them up and running and making sure that the SLAs are met presents even more challenges and headaches to Hadoop operators. To make matters worse, managing upgrades can be a nightmare. Hadoop users are presented with their own fair share of difficulties such as slow running jobs and not knowing why they are slow. For third-party software vendors interested in incorporating Hadoop management and monitoring capabilities, there does not seem to be an obvious, easy solution. Apache Ambari is aimed at making lives of Hadoop operators, users, and integrators simpler by providing a management interface to do all of that and more. This session presents usages of Ambari`s Web UI for Hadoop operators (deploying, managing, and monitoring) as well as Hadoop users (job analytics). The talk will also touch upon Ambari`s REST API and how it is used in the real world. The session concludes by revealing the future roadmap of Ambari including queue management, upgrade, disaster recovery, high availability, and more.

Managing your Hadoop Clusters with Apache Ambari

DataWorks Summit

What's hot (20)

AWS - Beanstalk Fundamentals

AWS database services

AWS Services - Part 1

The Role of Elastic Load Balancer - Apache Stratos

Spring Cloud Config

Shipping logs to splunk from a container in aws howto

Amazon S3 connector

8 Source Code Cloudstack Developer Day

Agile Deployment using Git and AWS Elastic Beanstalk

Aws landing zone. journey to the cloud

Amazon ECS Deep Dive

20171122 aws usergrp_coretech-spn-cicd-aws-v01

Hochverfügbarkeit mit MariaDB Enterprise - MariaDB Roadshow Summer 2014 Hambu...

Ansible Automation for Oracle RMAN / Apex Restores

6 Roadmap Cloudstack Developer Day

Apache Ambari BOF - Overview - Hadoop Summit 2013

Elastic Load Balancing Deep Dive and Best Practices - Pop-up Loft Tel Aviv

Auto Retweets Using AWS Lambda

Serverless design considerations for Cloud Native workloads

Managing your Hadoop Clusters with Apache Ambari

Similar to Hive warehouse connector

Security is one of fundamental features for enterprise adoption. Specifically, for SQL users, row/column-level access control is important. However, when a cluster is used as a data warehouse accessed by various user groups via different ways, it is difficult to guarantee data governance in a consistent way. In this talk, we focus on SQL users and talk about how to provide row/column-level access controls with common access control rules throughout the whole cluster with various SQL engines, e.g., Apache Spark 2.1, Apache Spark 1.6 and Apache Hive 2.1. If some of rules are changed, all engines are controlled consistently in near real-time. Technically, we enables Spark Thrift Server to work with an identify given by JDBC connection and take advantage of Hive LLAP daemon as a shared and secured processing engine. We demonstrate row-level filtering, column-level filtering and various column maskings in Apache Spark with Apache Ranger. We use Apache Ranger as a single point of security control center.

Row/Column- Level Security in SQL for Apache Spark

DataWorks Summit/Hadoop Summit

Securing Spark Applications

DataWorks Summit/Hadoop Summit

HiveWarehouseConnector

Eric Wohlstadter

Get most out of Spark on YARN

DataWorks Summit

Watch full webinar here: https://buff.ly/43PDVsz In today's fast-paced, data-driven world, organizations need real-time data pipelines and streaming applications to make informed decisions. Apache Kafka, a distributed streaming platform, provides a powerful solution for building such applications and, at the same time, gives the ability to scale without downtime and to work with high volumes of data. At the heart of Apache Kafka lies Kafka Topics, which enable communication between clients and brokers in the Kafka cluster. Join us for this session with Pooja Dusane, Data Engineer at Denodo where we will explore the critical role that Kafka listeners play in enabling connectivity to Kafka Topics. We'll dive deep into the technical details, discussing the key concepts of Kafka listeners, including their role in enabling real-time communication between consumers and producers. We'll also explore the various configuration options available for Kafka listeners and demonstrate how they can be customized to suit specific use cases. Attend and Learn: - The critical role that Kafka listeners play in enabling connectivity in Apache Kafka. - Key concepts of Kafka listeners and how they enable real-time communication between clients and brokers. - Configuration options available for Kafka listeners and how they can be customized to suit specific use cases.

Unlocking the Power of Apache Kafka: How Kafka Listeners Facilitate Real-time...

Denodo

In Data Engineer’s Lunch #45: Apache Livy, we discussed Apache Livy, a REST API for interacting with Spark Clusters, submitting jobs and managing Spark Contexts and cached data. Accompanying Blog: https://blog.anant.us/data-engineers-lunch-45-apache-livy Accompanying YouTube: https://youtu.be/WMRN815FuQ8 Sign Up For Our Newsletter: http://eepurl.com/grdMkn Join Data Engineer’s Lunch Weekly at 12 PM EST Every Monday: https://www.meetup.com/Data-Wranglers-DC/events/ Cassandra.Link: https://cassandra.link/ Follow Us and Reach Us At: Anant: https://www.anant.us/ Awesome Cassandra: https://github.com/Anant/awesome-cassandra Email: solutions@anant.us LinkedIn: https://www.linkedin.com/company/anant/ Twitter: https://twitter.com/anantcorp Eventbrite: https://www.eventbrite.com/o/anant-1072927283 Facebook: https://www.facebook.com/AnantCorp/ Join The Anant Team: https://www.careers.anant.us

Data Engineer’s Lunch #45: Apache Livy

Anant Corporation

Query Engines for Hive: MR, Spark, Tez with LLAP – Considerations!

Mich Talebzadeh (Ph.D.)

Apache Hive - Introduction

Muralidharan Deenathayalan

1 Introduction One can argue that the most challenging task in a Big Data setting is getting the data that can then be used for data analysis and predictions. Towards this goal, in this assignment, you will be setting up a pipeline to ingest data from Twitter, clean and process it, and load it into a Hive table for analysis. You will be using Apache Kafka, Apache Flume for data ingestion into HDFS, and Spark SQL for data analysis and Spark ML for prediction. 2 Instructions 2.1 Step 1: Setup Kafka producer to ingest tweets Setup a Kafka producer in Python that gets data from Twitter for a specific set of keywords related to a topic (the choice of topic and keywords are up to you) and sends it to a topic in a Kafka broker. You will need to sign up for a developer account with Twitter, which is free. The data should be formatted in a way that can be easily ingested by the other components of the pipeline. There is a limit on the number of calls that a producer can make to Twitter at any one time. Check the limitations and adjust your code so that tweets are received continuously without going over the limit. Some sample code is provided for setting up the producer as well online videos. 2.2 Step 2: Setup Kafka Consumer Setup a Kafka consumer that reads from the Kafka topic and saves the data to HDFS. The consumer should be designed to handle large volumes of data and should be fault-tolerant. Some sample Kafka consumers are available as well. 2.3 Step 3: Setup Flume Agent Apache Flume is a streaming tool typically used for text data. Unlike Apache Kafka, it is more lightweight in installation and setup. Review the videos posted on Apache Flume and setup a Flume agent that gets data from Twitter and saves it to HDFS. 1 2.4 Step 4: Clean and Process Data The data that is saved to HDFS needs to be cleaned and put into multiple columns. It is up to you how you want to clean the data, either in the consumer, producer for Kafka, or in Flume, or at the end of the pipeline. You should ensure that the data is formatted in a way that can be easily loaded into Spark for later processing (see below). 2.5 Step 5: Load Data into Spark SQL Data then must be loaded into a Scala DataFrame for analysis. Use the Scala DataFrame to run some queries on the data that you have read. The queries will depend on the topic that you have chosen and keywords received from Twitter. 2.6 Step 6: Train a Spark ML algorithm Using the data in HDFS, train a machine learning algorithm using Spark ML to predict whether the tweets that you have have ingested have positive sentiment or negative sentiment. You can also choose other predictions depending on the topic..

1 Introduction One can argue that the most challenging task .pdf

cbholla1

Introduction One can argue that the most challenging task in a Big Data setting is getting the data that can then be used for data analysis and predictions. Towards this goal, in this assignment, you will be setting up a pipeline to ingest data from Twitter, clean and process it, and load it into a Hive table for analysis. You will be using Apache Kafka, Apache Flume for data ingestion into HDFS, and Spark SQL for data analysis and Spark ML for prediction. 2 Instructions 2.1 Step 1: Setup Kafka producer to ingest tweets Setup a Kafka producer in Python that gets data from Twitter for a specific set of keywords related to a topic (the choice of topic and keywords are up to you) and sends it to a topic in a Kafka broker. You will need to sign up for a developer account with Twitter, which is free. The data should be formatted in a way that can be easily ingested by the other components of the pipeline. There is a limit on the number of calls that a producer can make to Twitter at any one time. Check the limitations and adjust your code so that tweets are received continuously without going over the limit. Some sample code is provided for setting up the producer as well online videos. 2.2 Step 2: Setup Kafka Consumer Setup a Kafka consumer that reads from the Kafka topic and saves the data to HDFS. The consumer should be designed to handle large volumes of data and should be fault-tolerant. Some sample Kafka consumers are available as well. 2.3 Step 3: Setup Flume Agent Apache Flume is a streaming tool typically used for text data. Unlike Apache Kafka, it is more lightweight in installation and setup. Review the videos posted on Apache Flume and setup a Flume agent that gets data from Twitter and saves it to HDFS. 1 2.4 Step 4: Clean and Process Data The data that is saved to HDFS needs to be cleaned and put into multiple columns. It is up to you how you want to clean the data, either in the consumer, producer for Kafka, or in Flume, or at the end of the pipeline. You should ensure that the data is formatted in a way that can be easily loaded into Spark for later processing (see below). 2.5 Step 5: Load Data into Spark SQL Data then must be loaded into a Scala DataFrame for analysis. Use the Scala DataFrame to run some queries on the data that you have read. The queries will depend on the topic that you have chosen and keywords received from Twitter. 2.6 Step 6: Train a Spark ML algorithm Using the data in HDFS, train a machine learning algorithm using Spark ML to predict whether the tweets that you have have ingested have positive sentiment or negative sentiment. You can also choose other predictions depending on the topic..

Introduction One can argue that the most challenging task in.pdf

adinathknit

Kafka is a scalable, distributed publish subscribe messaging system that's used as a data transmission backbone in many data intensive digital businesses. Couchbase Server is a scalable, flexible document database that's fast, agile, and elastic. Because they both appeal to the same type of customers, Couchbase and Kafka are often used together. This presentation from a meetup in Mountain View describes Kafka's design and why people use it, Couchbase Server and its uses, and the use cases for both together. Also covered is a description and demo of Couchbase Server writing documents to a Kafka topic and consuming messages from a Kafka topic. using the Couchbase Kafka Connector.

Real time Messages at Scale with Apache Kafka and Couchbase

Will Gardella

How Apache Kafka is transforming Hadoop, Spark and Storm

Edureka!

[Big Data Spain] Apache Spark Streaming + Kafka 0.10: an Integration Story

Joan Viladrosa Riera

Kafka for DBAs

Gwen (Chen) Shapira

MySQL Fabric

Mark Swarbrick

Abstract:- Apache Kafka evolved from an enterprise messaging system to a fully distributed streaming data platform for building real-time streaming data pipelines and streaming data applications without the need for other tools/clusters for data ingestion, storage and stream processing. In this talk you will learn more about: A quick introduction to Kafka Core, Kafka Connect and Kafka Streams through code examples, key concepts and key features. A reference architecture for building such Kafka-based streaming data applications. A demo of an end-to-end Kafka-based streaming data application.

Building streaming data applications using Kafka*[Connect + Core + Streams] b...

Data Con LA

In this talk, we will take a whirlwind tour of the entire stack that Spring Framework provides for Apache Kafka support. Spring for Apache Kafka is the foundational library that provides the basic support for building Spring applications with Apache Kafka and Kafka Streams. Spring Cloud Stream, using its binder for Apache Kafka, provides an opinionated programming model and other convenient features built on top of Spring for Apache Kafka. This talk will explore all these various building blocks in Spring and show the differences between them. Along the journey, we will demonstrate how Spring makes it easier for developers to build powerful applications using Apache Kafka and Kafka Streams.

Walking through the Spring Stack for Apache Kafka with Soby Chacko | Kafka S...

HostedbyConfluent

Apache Kafka evolved from an enterprise messaging system to a fully distributed streaming data platform for building real-time streaming data pipelines and streaming data applications without the need for other tools/clusters for data ingestion, storage and stream processing. In this talk you will learn more about: 1. A quick introduction to Kafka Core, Kafka Connect and Kafka Streams: What is and why? 2. Code and step-by-step instructions to build an end-to-end streaming data application using Apache Kafka

Building Streaming Data Applications Using Apache Kafka

Slim Baltagi

SpringOne Platform 2016 Speaker: Ian Fyfe; Director, Product Marketing, Hortonworks Apache Hadoop is the most powerful and popular platform for ingesting, storing and processing enormous amounts of “big data”. However, due to its original roots as a batch processing system, doing interactive business analytics with Hadoop has historically suffered from slow response times, or forced business analysts to extract data summaries out of Hadoop into separate data marts. This talk will discuss the different options for implementing speed-of-thought business analytics and machine learning tools directly on top of Hadoop including Apache Hive on Tez, Apache Hive on LLAP, Apache HAWQ and Apache MADlib.

Achieving Mega-Scale Business Intelligence Through Speed of Thought Analytics...

VMware Tanzu

Tensorflow model using docker and AWS SageMaker

Arif Khan

Similar to Hive warehouse connector (20)

Row/Column- Level Security in SQL for Apache Spark

Securing Spark Applications

HiveWarehouseConnector

Get most out of Spark on YARN

Unlocking the Power of Apache Kafka: How Kafka Listeners Facilitate Real-time...

Data Engineer’s Lunch #45: Apache Livy

Query Engines for Hive: MR, Spark, Tez with LLAP – Considerations!

Apache Hive - Introduction

1 Introduction One can argue that the most challenging task .pdf

Introduction One can argue that the most challenging task in.pdf

Real time Messages at Scale with Apache Kafka and Couchbase

How Apache Kafka is transforming Hadoop, Spark and Storm

[Big Data Spain] Apache Spark Streaming + Kafka 0.10: an Integration Story

Kafka for DBAs

MySQL Fabric

Building streaming data applications using Kafka*[Connect + Core + Streams] b...

Walking through the Spring Stack for Apache Kafka with Soby Chacko | Kafka S...

Building Streaming Data Applications Using Apache Kafka

Achieving Mega-Scale Business Intelligence Through Speed of Thought Analytics...

Tensorflow model using docker and AWS SageMaker

Recently uploaded

In this session, we will delve into strategic approaches for optimizing knowledge management within Microsoft 365, amidst the evolving landscape of Copilot. From leveraging automatic metadata classification and permission governance with SharePoint Premium, to unlocking Viva Engage for the cultivation of knowledge and communities, you will gain actionable insights to bolster your organization's knowledge-sharing initiatives. In this session, we will also explore how to facilitate solutions to enable your employees to find answers and expertise within Microsoft 365. You will leave equipped with practical techniques and a deeper understanding of how there is more to effective knowledge management than just enabling Copilot, but building actual solutions to prepare the knowledge that Copilot and your employees can use.

Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...

Drew Madelung

Strategies for Landing an Oracle DBA Job as a Fresher

Remote DBA Services

Manulife - Insurer Transformation Award 2024

The Digital Insurer

Created by Mozilla Research in 2012 and now part of Linux Foundation Europe, the Servo project is an experimental rendering engine written in Rust. It combines memory safety and concurrency to create an independent, modular, and embeddable rendering engine that adheres to web standards. Stewardship of Servo moved from Mozilla Research to the Linux Foundation in 2020, where its mission remains unchanged. After some slow years, in 2023 there has been renewed activity on the project, with a roadmap now focused on improving the engine’s CSS 2 conformance, exploring Android support, and making Servo a practical embeddable rendering engine. In this presentation, Rakhi Sharma reviews the status of the project, our recent developments in 2023, our collaboration with Tauri to make Servo an easy-to-use embeddable rendering engine, and our plans for the future to make Servo an alternative web rendering engine for the embedded devices industry. (c) Embedded Open Source Summit 2024 April 16-18, 2024 Seattle, Washington (US) https://events.linuxfoundation.org/embedded-open-source-summit/ https://ossna2024.sched.com/event/1aBNF/a-year-of-servo-reboot-where-are-we-now-rakhi-sharma-igalia

A Year of the Servo Reboot: Where Are We Now?

Igalia

Scaling API-first – The story of a global engineering organization Ian Reasor, Senior Computer Scientist - Adobe Radu Cotescu, Senior Computer Scientist - Adobe Apidays New York 2024: The API Economy in the AI Era (April 30 & May 1, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/

Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe

apidays

presentation ICT roal in 21st century education

jfdjdjcjdnsjd

The value of a flexible API Management solution for Open Banking Steve Melan, Manager for IT Innovation and Architecture - State's and Saving's Bank of Luxembourg Apidays New York 2024: The API Economy in the AI Era (April 30 & May 1, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/

Apidays New York 2024 - The value of a flexible API Management solution for O...

apidays

Webinar Recording: https://www.panagenda.com/webinars/why-teams-call-analytics-is-critical-to-your-entire-business Nothing is as frustrating and noticeable as being in an important call and being unable to see or hear the other person. Not surprising then, that issues with Teams calls are among the most common problems users call their helpdesk for. Having in depth insight into everything relevant going on at the user’s device, local network, ISP and Microsoft itself during the call is crucial for good Microsoft Teams Call quality support. To ensure a quick and adequate solution and to ensure your users get the most out of their Microsoft 365. But did you know that ‘bad calls’ are also an excellent indicator of other problems arising? Precisely because it is so noticeable!? Like the canary in the mine, bad calls can be early indicators of problems. Problems that might otherwise not have been noticed for a while but can have a big impact on productivity and satisfaction. Join this session by Christoph Adler to learn how true Microsoft Teams call quality analytics helped other organizations troubleshoot bad calls and identify and fix problems that impacted Teams calls or the use of Microsoft365 in general. See what it can do to keep your users happy and productive! In this session we will cover - Why CQD data alone is not enough to troubleshoot call problems - The importance of attributing call problems to the right call participant - What call quality analytics can do to help you quickly find, fix-, and prevent problems - Why having retrospective detailed insights matters - Real life examples of how others have used Microsoft Teams call quality monitoring to problem shoot problems with their ISP, network, device health and more.

Why Teams call analytics are critical to your entire business

panagenda

MS Copilot expands with MS Graph connectors

Nanddeep Nachan

"I see eyes in my soup": How Delivery Hero implemented the safety system for ...

Zilliz

ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke

Product Anonymous

Data Cloud, More than a CDP by Matt Robison

Anna Loughnan Colquhoun

AXA XL - Insurer Innovation Award Americas 2024

The Digital Insurer

Following the popularity of "Cloud Revolution: Exploring the New Wave of Serverless Spatial Data," we're thrilled to announce this much-anticipated encore webinar. In this sequel, we'll dive deeper into the Cloud-Native realm by uncovering practical applications and FME support for these new formats, including COGs, COPC, FlatGeoBuf, GeoParquet, STAC, and ZARR. Building on the foundation laid by industry leaders Michelle Roby of Radiant Earth and Chris Holmes of Planet in the first webinar, this second part offers an in-depth look at the real-world application and behind-the-scenes dynamics of these cutting-edge formats. We will spotlight specific use-cases and workflows, showcasing their efficiency and relevance in practical scenarios. Discover the vast possibilities each format holds, highlighted through detailed discussions and demonstrations. Our expert speakers will dissect the key aspects and provide critical takeaways for effective use, ensuring attendees leave with a thorough understanding of how to apply these formats in their own projects. Elevate your understanding of how FME supports these cutting-edge technologies, enhancing your ability to manage, share, and analyze spatial data. Whether you're building on knowledge from our initial session or are new to the serverless spatial data landscape, this webinar is your gateway to mastering cloud-native formats in your workflows.

Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME

Safe Software

Building Digital Trust in a Digital Economy Veronica Tan, Director - Cyber Security Agency of Singapore Apidays Singapore 2024: Connecting Customers, Business and Technology (April 17 & 18, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/

Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...

apidays

DBX First Quarter 2024 Investor Presentation

Dropbox

Real Time Object Detection Using Open CV

Khem

In the thrilling conclusion to 2023, ransomware groups had a banner year, really outdoing themselves in the "make everyone's life miserable" department. LockBit 3.0 took gold in the hacking olympics, followed by the plucky upstarts Clop and ALPHV/BlackCat. Apparently, 48% of organizations were feeling left out and decided to get in on the cyber attack action. Business services won the "most likely to get digitally mugged" award, with education and retail nipping at their heels. Hackers expanded their repertoire beyond boring old encryption to the much more exciting world of extortion. The US, UK and Canada took top honors in the "countries most likely to pay up" category. Bitcoins were the currency of choice for discerning hackers, because who doesn't love untraceable money?

Ransomware_Q4_2023. The report. [EN].pdf

Overkill Security

Artificial Intelligence Chap.5 : Uncertainty

Khushali Kathiriya

Abhishek Deb(1), Mr Abdul Kalam(2) M. Des (UX) , School of Design, DIT University , Dehradun. This paper explores the future potential of AI-enabled smartphone processors, aiming to investigate the advancements, capabilities, and implications of integrating artificial intelligence (AI) into smartphone technology. The research study goals consist of evaluating the development of AI in mobile phone processors, analyzing the existing state as well as abilities of AI-enabled cpus determining future patterns as well as chances together with reviewing obstacles as well as factors to consider for more growth.

Exploring the Future Potential of AI-Enabled Smartphone Processors

debabhi2

Recently uploaded (20)

Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...

Strategies for Landing an Oracle DBA Job as a Fresher

Manulife - Insurer Transformation Award 2024

A Year of the Servo Reboot: Where Are We Now?

Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe

presentation ICT roal in 21st century education

Apidays New York 2024 - The value of a flexible API Management solution for O...

Why Teams call analytics are critical to your entire business

MS Copilot expands with MS Graph connectors

"I see eyes in my soup": How Delivery Hero implemented the safety system for ...

ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke

Data Cloud, More than a CDP by Matt Robison

AXA XL - Insurer Innovation Award Americas 2024

Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME

Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...

DBX First Quarter 2024 Investor Presentation

Real Time Object Detection Using Open CV

Ransomware_Q4_2023. The report. [EN].pdf

Artificial Intelligence Chap.5 : Uncertainty

Exploring the Future Potential of AI-Enabled Smartphone Processors

Hive warehouse connector

1. Confidential – Restricted Tamil Selvan Kandasamy, Senior Customer Operations Engineer - Configuring Spark Hive Warehouse Connector - Debugging LLAP Issues

2. © Cloudera, Inc. All rights reserved. 2 HiveWarehouseConnector A library to read/write DataFrames and Streaming DataFrames to/from Apache Hive™ using LLAP You can use the Hive Warehouse Connector (HWC) API to access any type of table in the Hive catalog from Spark. When you use SparkSQL, standard Spark APIs access tables in the Spark catalog

3. © Cloudera, Inc. All rights reserved. 3 HiveWarehouseConnector - Continued The Hive Warehouse Connector supports the following applications: ● Spark shell ● PySpark ● The spark-submit script Does Ranger come into picture? Yes, it does. Both row and column access control when the data in accessed from Spark

4. © Cloudera, Inc. All rights reserved. 4 HiveWarehouseConnector - Setting things up You must add several Spark properties through spark-2-defaults in Ambari to use the Hive Warehouse Connector for accessing data in Hive. Alternatively, configuration can be provided for each job using hive -- conf Property Description Comments spark.sql.hive.hiveserver2.jdbc.url URL for HiveServer2 Interactive In Ambari, copy the value from Services > Hive > Summary > HIVESERVER2 INTERACTIVE JDBC URL spark.datasource.hive.warehouse.metastoreUri URI for metastore Copy the value from hive.metastore.uris. For example,thrift://mycluster-1.com:9083 spark.datasource.hive.warehouse.load.staging.dir HDFS temp directory for batch writes to Hive For example, /tmp spark.hadoop.hive.llap.daemon.service.hosts Application name for LLAP service Copy value from Advanced hive-interactive-site >hive.llap.daemon.service.hosts spark.hadoop.hive.zookeeper.quorum Zookeeper hosts used by LLAP Copy value from Advanced hive- sitehive.zookeeper.quorum

5. © Cloudera, Inc. All rights reserved. 5 HiveWarehouseConnector - Making a connection Run following code in scala shell to view the table data Scala> import com.hortonworks.hwc.HiveWarehouseSession val hive = HiveWarehouseSession.session(spark).build() hive.execute("show tables").show hive.executeQuery("select * from employee").show

6. Confidential – Restricted THANK YOU

Hive warehouse connector

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Similar to Hive warehouse connector

Similar to Hive warehouse connector (20)

Recently uploaded

Recently uploaded (20)

Hive warehouse connector