Getting Started with Apache Cassandra and Apache Zeppelin (DuyHai DOAN, DataStax) | C* Summit 2016
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If you are interested in Big Data, you surely already know Apache Spark or Apache Cassandra, but do you know Apache Zeppelin ? Do you know that it is possible to draw out beautiful graph using an user-friendly interface out of your Spark RDD and Cassandra queries ? In this session, I will introduce Zeppelin by live coding example and highlight its modular architecture which allows you to plug-in any interpreter for the back-end of your choice. Then we'll dig into the Apache Cassandra interpreter and show how to use it as a default front-end to display your Cassandra data About the Speaker DuyHai DOAN Apache Cassandra Evangelist, DataStax DuyHai DOAN is an Apache Cassandra Evangelist at DataStax. He spends his time between technical presentations/meetups on Cassandra, coding on open source projects like Achilles or Apache Zeppelin to support the community and helping all companies using Cassandra to make their project successful. Previously he was working as a freelance Java/Cassandra consultant.
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xPatterns is a big data analytics platform as a service that enables a rapid development of enterprise-grade analytical applications. It provides tools, api sets and a management console for building an ELT pipeline with data monitoring and quality gates, a data warehouse for ad-hoc and scheduled querying, analysis, model building and experimentation, tools for exporting data to NoSql and solrCloud cluster for real-time access through low-latency/high-throughput apis as well as dashboard and visualization api/tools leveraging the available data and models. In this presentation we will showcase one of the analytical applications build on top of xPatterns for our largest customer for that runs xPatterns in production on top a data warehouse consisting of several hundreds TB of medical, pharmacy and lab data records consisting of tens of billions of records. We will showcase the xPatterns components in the form of APIs and tools employed throughout the entire lifecycle of this application. The core of the presentation is the evolution of the infrastructure from the Hadoop/Hive stack to the new BDAS Spark, Shark, Mesos and Tachyon, with lessons learned and demos.
Lambda Architecture with Spark
The presentation covers lambda architecture and implementation with spark. In the presentation we will discuss about components of lambda architecture like batch layer, speed layer and serving layer. We will also discuss its advantages and benefits with spark.
Leverage Mesos for running Spark Streaming production jobs by Iulian Dragos a...
Mesos is an open source cluster manager that improves resource utilization. It allows Spark Streaming jobs to leverage Mesos fault tolerance features like driver supervision using Marathon. Backpressure is also supported in Spark Streaming to prevent scheduling delays from fast data arrival. Reactive Streams provide more direct backpressure control and are expected in future Spark versions.
Scylla Summit 2022: What’s New in ScyllaDB Operator for Kubernetes
This document summarizes the Scylla Operator for Kubernetes, including its developers, features, releases, and roadmap. Key points include:
- The Scylla Operator manages and automates tasks for Scylla clusters on Kubernetes.
- Features include seedless mode, security enhancements, performance tuning, and improved stability.
- It follows a rapid 6-week release cycle and supports the latest two releases.
- Future plans include additional performance optimizations, persistent storage support, TLS encryption, and multi-datacenter capabilities.
Elassandra: Elasticsearch as a Cassandra Secondary Index (Rémi Trouville, Vin...
Many companies use both elasticsearch and cassandra, typically in the form of logs or time series, but managing many softwares at a large scale can be quite challenging. Elassandra tightly integrates elasticsearch within cassandra as a secondary index, allowing near-realtime search with all existing elasticsearch APIs, plugins and tools like Kibana. We will present the core concepts of elassandra and explain how it draws benefit from internal cassandra features to make elasticsearch masterless, scalable with automatic resharding, more reliable and more efficient than deploying both softwares. We will also explore the bidirectional mapping : the way elasticsearch automatically creates the corresponding cassandra schema and the way elasticsearch indexes an existing cassandra table. Furthermore, we will share some use cases and benchmark results demonstrating practical use of elassandra to scale-out, re-index with zero-downtime, search and visualize data with various tools.
About the Speakers
Remi Trouville Consultant, Independant
Remi is an IT engineer who has worked for the last 8 years in the financial industry as a team manager responsible for all the call-center softwares managing the customer experience. At the end of this period, his team was dealing with 10,000+ agents with 100+ sites and some highly critical business processes such as storage of oral proof sales for transactions. He holds a Master's Degree in Telecommunication engineering and is now following an executive-MBA, in a French business school.
Spark Summit EU talk by Debasish Das and Pramod Narasimha
This document describes how Apache Spark and Apache Lucene can be used together for near-real-time predictive model building. It discusses representing streaming device data in Lucene documents that are indexed for fast search and retrieval. A framework called Trapezium is used to build batch, streaming, and API services on top of Spark and Lucene. It shows how to index large datasets in Lucene efficiently using Spark and analyze retrieved devices to generate statistical and predictive models.
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Spark Summit EU talk by Miklos Christine paddling up the stream
Spark Summit EU talk by Miklos Christine paddling up the stream
Cassandra Community Webinar: Apache Spark Analytics at The Weather Channel - ...
Cassandra Community Webinar: Apache Spark Analytics at The Weather Channel - ...
Introduction to Apache Cassandra™ + What’s New in 4.0
Introduction to Apache Cassandra™ + What’s New in 4.0
Cassandra @ Yahoo Japan (Satoshi Konno, Yahoo) | Cassandra Summit 2016
Cassandra @ Yahoo Japan (Satoshi Konno, Yahoo) | Cassandra Summit 2016
Recipes for Running Spark Streaming Applications in Production-(Tathagata Das...
Recipes for Running Spark Streaming Applications in Production-(Tathagata Das...
Lessons Learned on Java Tuning for Our Cassandra Clusters (Carlos Monroy, Kne...
Lessons Learned on Java Tuning for Our Cassandra Clusters (Carlos Monroy, Kne...
xPatterns ... beyond Hadoop (Spark, Shark, Mesos, Tachyon)
xPatterns ... beyond Hadoop (Spark, Shark, Mesos, Tachyon)
Leverage Mesos for running Spark Streaming production jobs by Iulian Dragos a...
Leverage Mesos for running Spark Streaming production jobs by Iulian Dragos a...
Scylla Summit 2022: What’s New in ScyllaDB Operator for Kubernetes
Scylla Summit 2022: What’s New in ScyllaDB Operator for Kubernetes
Elassandra: Elasticsearch as a Cassandra Secondary Index (Rémi Trouville, Vin...
Elassandra: Elasticsearch as a Cassandra Secondary Index (Rémi Trouville, Vin...
Spark Summit EU talk by Debasish Das and Pramod Narasimha
Spark Summit EU talk by Debasish Das and Pramod Narasimha
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Spark zeppelin-cassandra at synchrotron
This document discusses using Spark, Cassandra, and Zeppelin for storing and aggregating metrics data from a particle accelerator project called HDB++. It provides an overview of the HDB++ project, how it previously used MySQL but now stores data in Cassandra. It describes the Spark jobs that are run to load metrics data from Cassandra and generate statistics that are written back to Cassandra. It also demonstrates visualizing the data using Zeppelin and discusses some tricks and traps to be aware of when using this stack.
Real-time Personal Trainer on the SMACK Stack
The document describes an automated personal trainer application called muvr that runs on the SMACK stack. Muvr suggests exercise routines and provides form feedback. It uses iOS to track user data, Akka for reactive services, Spark for distributed processing, neon for machine learning, and Cassandra for data storage. Docker and Mesos are used to deploy the system for high availability and scalability. AWS spot instances are utilized to reduce costs.
Big Data visualization with Apache Spark and Zeppelin
This presentation gives an overview of Apache Spark and explains the features of Apache Zeppelin(incubator). Zeppelin is the open source tool for data discovery, exploration and visualization. It supports REPLs for shell, SparkSQL, Spark(scala), python and angular. This presentation was made on the Big Data Day, at the Great Indian Developer Summit, Bangalore, April 2015
Everyday I’m scaling... Cassandra
Co-Founder and CTO of Instaclustr, Ben Bromhead's presentation at the Cassandra Summit 2016, in San Jose.
This presentation will show how create truly elastic Cassandra deployments on AWS allowing you to scale and shrink your large Cassandra deployments multiple times a day. Leveraging a combination of EBS backed disks, JBOD, token pinning and our previous work on bootstrapping from backups you will be able to dramatically reduce costs per cluster by scaling to match your daily workloads.
Micro-batching: High-performance Writes (Adam Zegelin, Instaclustr) | Cassand...
This presentation will investigate how using micro-batching for submitting writes to Cassandra can improve throughput and reduce client application CPU load.
Micro-batching combines writes for the same partition key into a single network request and ensures they hit the "fast path" for writes on a Cassandra node.
About the Speaker
Adam Zegelin Technical Co-founder, Instaclustr
As Instaclustrs founding software engineer, Adam provides the foundation knowledge of our capability and engineering environment. He delivers business-focused value to our code-base and overall capability architecture. Adam is also focused on providing Instaclustr's contribution to the broader open source community on which our products and services rely, including Apache Cassandra, Apache Spark and other technologies such as CoreOS and Docker.
Apache Big Data EU 2015 - HBase
The document provides an overview of the state of the Apache HBase database project. It discusses the project goals of availability, stability, and scalability. It also summarizes the mature codebase, active development areas like region replicas and ProcedureV2, and the growing ecosystem of SQL interfaces and other Hadoop components integrated with HBase. Recent releases include 1.1.2 which improved scanners and introduced quotas and throttling, and the 1.0 release which adopted semantic versioning and added region replicas.
Building a Fast, Resilient Time Series Store with Cassandra (Alex Petrov, Dat...
Cassandra is awesome for many things. One of the things it's awesome for is Time Series. Combining the power of Cassandra with APIs of existing Time Series tools, such as Graphite can yield interesting results.
Cyanite is a Time Series aggregator and store built on top of Cassandra. It's fully compatible with Graphite, can serve as a plug-in replacement for Graphite and Graphite web.
Cyanite is using SASI indexes to make glob metric path queries, can query and aggregate, store, display and analyse metrics from hundreds and thousands of servers.
Which data modelling practices work best for Time Series, which new awesome Cassandra features you can use to make your Time Series analysis better.
About the Speaker
Alex Petrov Software Engineer, DataStax
Polyglot programmer. Interested in algorithms, distributed systems, algebra and high performance solutions.
Explorez vos données avec apache zeppelin
Rapide présentation du projet Apache Zeppelin, environnement web facilitant l'exploration et le partage autour de vos données : le support de multiples langages pour le traitement (Spark) et l'accès aux bases de données (PostgreSQL, Cassandra, ...) permet à Zeppelin de s'adapter aux backends les plus divers.
Real World Use Case with Cassandra (Eddie Satterly, DataNexus) | C* Summit 2016
Discuss how IAG, Australia's leading Insurance company, uses Cassandra to change the way they integrate and leverage their data. First use case is in moving from Silos of data to enabling near real time analytics on critical digital application data leveraging kafka for data flows to Cassandra. Then leveraging Solr and Spark nodes to enable real time search and analytics. Second use case is in collecting new telemetry and telematics data to enable real time feedback to customers that initially shows 7-20% fuel savings for gas vehicles. This will also help customers to avoid heavy accident risk areas of cities during peak times and eventually suggest alternate routes and commute timing to reduce risk.
About the Speaker
Eddie Satterly Co-Founder, DataNexus
Eddie has served in a variety of roles including developer, engineer, architect and CTO over his 27+ year career. At DataNexus he is building a new big data application. Previously he was CTO of the Emerging Technologies at CSC running product portfolio and R&D teams in: Cyber, Analytics, Cloud, Mobile & IoT. Prior in CTO Office at Splunk where he presented at events and set data strategy. Prior he revolutionized how Expedia delivers service to improve user experience using Cassandra. MVP
SMACK Stack - Fast Data Done Right by Stefan Siprell at Codemotion Dubai
A talk covering the best-of-breed platform consisting of Spark, Mesos, Akka, Cassandra and Kafka. SMACK is more of a toolbox of technologies to allow the building of resilient ingestion pipelines, offering a high degree of freedom in the selection of analysis and query possibilities and baked in support for flow-control. More and more customers are using this stack, which is rapidly becoming the new industry standard for Big Data solutions. Session can be seen here - in German - https://speakerdeck.com/stefan79/fast-data-smack-down
Advanced Cassandra Operations via JMX (Nate McCall, The Last Pickle) | C* Sum...
Advanced Apache Cassandra operations depends on an understanding of what features are available via the JMX interface. While nodetool exposes many of these, the most useful are still waiting to be discovered. The JMX interface allows the code base to expose functions that operate directly on internal structures, making real time changes to the way the process runs. With this skill in your toolkit there is no limit to the changes you can make.
In this talk Nate McCall, CTO at The Last Pickle, will explain how to explore, secure, and invoke the JMX interface exposed by Cassandra. He'll then move on to what you can do with it such as compacting specific SSTables, changing compaction on a single node, managing repairs, diagnosing latency, viewing cross node timeouts, and others. Whether you are a developer or operator, new or experienced, you will be given a thorough understanding of what all is available via JMX without having to consult the code on your own.
About the Speaker
Nate McCall CTO, The Last Pickle
Nate McCall has 16 years of server-side systems and software development experience. He started his involvement in the Cassandra community in the late fall of 2009 when he became one of the original developers on the Hector Java client. He has contributed a number of patches over the years to the Apache Cassandra code base and continues to be actively involved on the mail lists, issue system and IRC. He has been a DataStax MVP every year since the inception of the program.
Using Spark to Load Oracle Data into Cassandra (Jim Hatcher, IHS Markit) | C*...
Spark is an execution framework designed to operate on distributed systems like Cassandra. It's a handy tool for many things, including ETL (extract, transform, and load) jobs. In this session, let me share with you some tips and tricks that I have learned through experience. I'm no oracle, but I can guarantee these tips will get you well down the path of pulling your relational data into Cassandra.
About the Speaker
Jim Hatcher Principal Architect, IHS Markit
Jim Hatcher is a software architect with a passion for data. He has spent most of his 20 year career working with relational databases, but he has been working with Big Data technologies such as Cassandra, Solr, and Spark for the last several years. He has supported systems with very large databases at companies like First Data, CyberSource, and Western Union. He is currently working at IHS, supporting an Electronic Parts Database which tracks half a billion electronic parts using Cassandra.
Apache zeppelin the missing component for the big data ecosystem
Duy Hai Doan presented Apache Zeppelin, an open-source web-based notebook that allows users to interact with data. Zeppelin provides a front-end GUI and display system for data analysis tools and uses interpreters to connect to back-end systems like Spark, Cassandra, and Flink. Doan demonstrated Zeppelin's notebook interface, display options, and how users can write their own interpreters to connect new systems to Zeppelin. Future plans for Zeppelin include improving usability, adding authentication and authorization, and developing more interpreters and visualizations.
Cassandra 3 new features 2016
New Cassandra 3 features:
- materialized views
- user defined functions and aggregates
- JSON syntax
- SASI index for full text search
Data Modeling a Scheduling App (Adam Hutson, DataScale) | Cassandra Summit 2016
The task of a data modeler is to create order out of chaos without excessively distorting the truth. The finished product should be data model that describes the structure, manipulation and integrity aspects of the data to be stored. To properly create a data model, the modeler will transform said chaos through three distinct stages. The first is a Conceptual Data Model, then a Logical Data Model, and lastly, a Physical Data Model. This presentation will cover all three stages. It will also walk through the creation of a full blown data model for a service scheduling application to be built with a Cassandra backend. This presentation is based on a 3 part blog series I wrote for DataScale.
About the Speaker
Adam Hutson Data Architect, DataScale
Adam is Data Architect for DataScale, Inc. He is a seasoned data professional with experience designing & developing large-scale, high-volume database systems. Adam previously spent four years as Senior Data Engineer for Expedia building a distributed Hotel Search using Cassandra 1.1 in AWS. Having worked with Cassandra since version 0.8, he was early to recognize the value Cassandra adds to Enterprise data storage. Adam is also a DataStax Certified Cassandra Developer.
What We Learned About Cassandra While Building go90 (Christopher Webster & Th...
Go90 is a mobile entertainment platform offering access to live and on demand videos. We built the web services platform and social features like activity feed for go90 by making heavy use of Cassandra and Scala, and would like to share what we learned during development and while operating go90. In this presentation, we cover our data model evolution from the initial prototypes to the current production version and the significant performance gain by using a better data model. We will explain how we apply time series data modeling and the benefits of using expiring columns with DateTieredCompactionStrategy. We will also talk about interesting experiences related to table modifications, tombstones and table pagination. On the operations side, we will discuss our findings on java driver usage, performance, monitoring, cluster maintenance, version upgrade, 2-way ssl and many more. We hope you can learn from our mistakes instead of making them yourself!
About the Speakers
Christopher Webster Software Engineer, AOL
Christopher Webster works on the web services platform for the go90 AOL project. Previously he was a Computer Scientist for the Mission Control Technologies project at NASA Ames Center. Chris worked as a senior staff engineer at Sun Microsystems for Project zembly, the cloud development and deployment environment as well as technical lead in many NetBeans projects. Chris is an author of the NetBeans Field Guide and Assemble the Social Web With Zembly.
Thomas Ng Software Engineer, AOL
Thomas Ng is a software engineer at AOL, building web services for the go90 mobile entertainment platform using Cassandra, Scala and Kafka.
Time Series Processing with Apache Spark
Chronix Spark - a framework for time series processing with Apache Spark. Presentation from Apache Big Data, North America, 2016, Vancouver BC.
Intro to Big Data Analytics using Apache Spark and Apache Zeppelin
This workshop will provide an introduction to Big Data Analytics using Apache Spark and Apache Zeppelin.
https://github.com/zeltovhorton/intro_spark_zeppelin_meetup
There will be a short lecture that includes an introduction to Spark, the Spark components.
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.
The lecture will be followed by demo . There will be a short lecture on Hadoop and how Spark and Hadoop interact and compliment each other. You will learn how to move data into HDFS using Spark APIs, create Hive table, explore the data with Spark and SQL, transform the data and then issue some SQL queries. We will be using Scala and/or PySpark for labs.
Frustration-Reduced Spark: DataFrames and the Spark Time-Series Library
In this talk I talk about my recent experience working with Spark Data Frames and the Spark TimeSeries library. For data frames, the focus will be on usability. Specifically, a lot of the documentation does not cover common use cases like intricacies of creating data frames, adding or manipulating individual columns, and doing quick and dirty analytics. For the time series library, I dive into the kind of use cases it supports and why it’s actually super useful.
Kubernetes Architecture v1.x
Google kubernetes architecture v1.x
https://cdn.yongbok.net/ruo91/architecture/k8s/v1.1/kubernetes_architecture.png
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Big Data visualization with Apache Spark and Zeppelin
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Micro-batching: High-performance Writes (Adam Zegelin, Instaclustr) | Cassand...
Micro-batching: High-performance Writes (Adam Zegelin, Instaclustr) | Cassand...
Building a Fast, Resilient Time Series Store with Cassandra (Alex Petrov, Dat...
Building a Fast, Resilient Time Series Store with Cassandra (Alex Petrov, Dat...
Real World Use Case with Cassandra (Eddie Satterly, DataNexus) | C* Summit 2016
Real World Use Case with Cassandra (Eddie Satterly, DataNexus) | C* Summit 2016
SMACK Stack - Fast Data Done Right by Stefan Siprell at Codemotion Dubai
SMACK Stack - Fast Data Done Right by Stefan Siprell at Codemotion Dubai
Advanced Cassandra Operations via JMX (Nate McCall, The Last Pickle) | C* Sum...
Advanced Cassandra Operations via JMX (Nate McCall, The Last Pickle) | C* Sum...
Using Spark to Load Oracle Data into Cassandra (Jim Hatcher, IHS Markit) | C*...
Using Spark to Load Oracle Data into Cassandra (Jim Hatcher, IHS Markit) | C*...
Apache zeppelin the missing component for the big data ecosystem
Apache zeppelin the missing component for the big data ecosystem
Data Modeling a Scheduling App (Adam Hutson, DataScale) | Cassandra Summit 2016
Data Modeling a Scheduling App (Adam Hutson, DataScale) | Cassandra Summit 2016
What We Learned About Cassandra While Building go90 (Christopher Webster & Th...
What We Learned About Cassandra While Building go90 (Christopher Webster & Th...
Intro to Big Data Analytics using Apache Spark and Apache Zeppelin
Intro to Big Data Analytics using Apache Spark and Apache Zeppelin
Frustration-Reduced Spark: DataFrames and the Spark Time-Series Library
Frustration-Reduced Spark: DataFrames and the Spark Time-Series Library
Similar to Getting Started with Apache Cassandra and Apache Zeppelin (DuyHai DOAN, DataStax) | C* Summit 2016
Project Avatar (Lyon JUG & Alpes JUG - March 2014)
The document discusses Project Avatar, which allows running server-side JavaScript applications on the Java Virtual Machine (JVM). Project Avatar uses Nashorn to run JavaScript on the JVM and provides an environment similar to Node.js. It allows leveraging Java libraries and frameworks from JavaScript code. Project Avatar also includes features like shared state, messaging, and an object-relational mapping framework to integrate JavaScript applications with the JVM and Java EE.
Java EE 7 et ensuite pourquoi pas JavaScript sur le serveur!
The document discusses Project Avatar, which allows server-side JavaScript applications to run on the Java Virtual Machine (JVM). It enables Node.js-style programming with advanced features like multi-threading and integration with Java EE. Project Avatar includes a runtime based on Nashorn and Node.js modules, and provides services for HTTP, WebSockets, messaging and data storage that leverage the JVM and Java EE.
Low Latency SQL on Hadoop - What's best for your cluster
This document compares different SQL engines for Hadoop including Impala, Hive, Shark, and Presto. It summarizes performance benchmarks showing Impala and Shark to be the fastest. It also describes the architectures of each engine and how they integrate with Hadoop components like YARN. Impala runs queries directly on the cluster while others like Hive rely on Tez to optimize query plans. The document concludes that while Shark can outperform Hive, it lacks vendor support, and Presto is still immature though easy to deploy.
Dr. Ike Nassi, Founder, TidalScale at MLconf NYC - 4/15/16
Scaling Spark – Vertically: The mantra of Spark technology is divide and conquer, especially for problems too big for a single computer. The more you divide a problem across worker nodes, the more total memory and processing parallelism you can exploit. This comes with a trade-off. Splitting applications and data across multiple nodes is nontrivial, and more distribution results in more network traffic which becomes a bottleneck. Can you achieve scale and parallelism without those costs?
We’ll show results of a variety of Spark application domains including structured data, graph processing and common machine learning in a single, high-capacity scaled-up system versus a more distributed approach and discuss how virtualization can be used to define node size flexibly, achieving the best balance for Spark performance.
Lift and shift to sparc cloud
Riccardo Romani from Oracle Italy discusses migrating workloads to Oracle's SPARC Cloud Infrastructure as a Service (IaaS). Key points include:
1) Oracle offers an easy "lift and shift" migration of workloads to its SPARC Cloud IaaS using standard virtualization.
2) Customers gain access to additional Oracle Cloud services like databases, storage, analytics while Oracle handles operations and support.
3) Performance tests show SPARC Cloud delivers up to 1.6-6.1x better performance than AWS for various workloads due to hardware acceleration features.
OpenStack and NetApp - Chen Reuven - OpenStack Day Israel 2017
This document discusses NetApp's integration with OpenStack. It begins with an introduction to NetApp, describing it as a global Fortune 500 company and leader in data management solutions. It then covers basic OpenStack and NetApp terminology. The remainder summarizes NetApp's storage portfolio for OpenStack and how its different solutions provide capabilities like snapshots, cloning, and quality of service controls when used with OpenStack interfaces like Cinder and Manila. It concludes with a demonstration of provisioning OpenStack volumes using NetApp storage.
Sparc solaris servers
The document discusses Oracle's SPARC servers and Solaris operating system. It highlights the new SPARC T4 servers as providing up to 5x faster performance than previous T3 servers. It also promotes the SPARC SuperCluster as the fastest general purpose platform, capable of outperforming IBM and HP systems. Oracle positions its SPARC/Solaris products as the best foundation for enterprise cloud computing and engineered to work optimally with Oracle software.
Jaroslav Tulach: GraalVM - z vývoje nejrychlejšího virtuálního stroje na světě
The document discusses GraalVM, a virtual machine developed by Oracle Labs that allows programs written in different programming languages to run together and interoperate with each other efficiently. GraalVM uses just-in-time compilation techniques as well as ahead-of-time compilation via native image generation to provide high performance for all languages while enabling zero-overhead interoperability between them. It aims to be a single virtual machine that can support and optimize the execution of applications built with various languages.
Oracle RAC on Extended Distance Clusters - Presentation
NOTE that a newer version of this presentation (covering Oracle RAC 12c Release) has been uploaded to my SlideShare: https://www.slideshare.net/MarkusMichalewicz/oracle-extended-clusters-for-oracle-rac
This presentation can be used as an illustration for some of the ideas and best practices discussed in the paper "Oracle RAC and Oracle RAC One Node on Extended Distance (Stretched) Clusters"
Sparc SuperCluster
Discusses some of the developments concerning Solaris SPARC and a description of Oracle engineering system Supercluster
configuring+oracle+rds+with+glasfish+server
This document provides instructions for installing Oracle XE with ORDS and configuring it to run on Apache Tomcat or Oracle Glassfish Server. It discusses the benefits of using ORDS over mod_plsql, including increased functionality and flexibility. It then provides step-by-step guides for installing Glassfish Server, Apache Tomcat Server, setting up ORDS, and configuring each web server with ORDS. Key steps include deploying the ords.war and i.war files, and accessing APEX over ORDS at specific URLs for each server.
Oracle RAC and Your Way to the Cloud by Angelo Pruscino
Angelo Pruscino, SVP Oracle RAC Development, presents the future of Oracle RAC, including some upcoming technologies and their relevance for the (private) database cloud as part of his Keynote during the DOAG 2014 conference.
Provisioning with Oracle Cloud Stack Manager
It’s easy to provision individual Oracle Cloud services, such as databases or Java application servers, from the instance creation pages in the Oracle cloud consoles. This presentation describes Oracle's Cloud Stack Manager tool which can be used with Oracle Cloud Infrastructure Classic to provision full sets of cloud services in a fully automated and repeatable manner.
Presentation was first delivered at Oracle's PaaS Forum in Budapest in March 18.
Unlock cassandra data for application developers using graphQL
This document discusses how Stargate provides a data gateway for unlocking Cassandra data for application developers through REST and GraphQL APIs. It introduces Stargate as an open source API framework that adds plugin support for new APIs, data types, and access methods to make it easy to use Cassandra for any application workload. It then covers the REST and GraphQL APIs provided by Stargate, how Stargate supports document and schemaless data through a document API, and how GraphQL queries can be mapped to CQL through both schema-first and CQL-first approaches.
Data processing at the speed of 100 Gbps@Apache Crail (Incubating)
Apache Crail (Incubating) is a distributed data store platform designed to share data at hardware speeds of 100+ Gbps with microsecond access latencies. It uses high-performance user-level I/O, careful software design, and data orchestration techniques to achieve these speeds. Evaluation shows it can deliver full hardware bandwidth and ultra-low access latencies. When used with Apache Spark, it provides significant performance gains for workloads like broadcast, shuffle, TeraSort, and joins. The project is seeking more language and framework integrations and aims to optimize for cloud deployments.
Spark Workshop
A gentle introduction to Apache Spark from the theorem of Resilient Distributed Datasets to deploying software to the core platform, Spark Streaming, and Spark SQL
New Not Your Father's Enterprise Manager
This document provides an overview of new features in Oracle Enterprise Manager 12c release 4. It begins with a history of Enterprise Manager requirements over time, highlighting how much more is now expected of the product. The rest of the document outlines the program agenda and then dives into some of the highlighted topics, including the new self-service portal, database as a service enhancements, cloning capabilities, and JVMD monitoring. Administration groups and dynamic groups are discussed as ways to better manage large numbers of targets. Monitoring templates, incident rule sets, and charts/dashboards are also covered as aspects of comprehensive database monitoring.
Graal VM: Multi-Language Execution Platform
Updates on the current status of Graal VM, a platform dedicated to run multiple programming languages at excellent performance. Experimental binaries are available from http://www.oracle.com/technetwork/oracle-labs/program-languages/overview/index.html.
Orcl siebel-sun-s282213-oow2006
The document discusses optimizing Oracle and Siebel applications on Sun Microsystems' UltraSPARC T1 (Niagara) platform. It provides an overview of Siebel architecture and its suitability for the T1 processor. Performance benchmarks show Siebel scaling well by taking advantage of the T1 processor's multithreading capabilities. The document also discusses various optimizations that can be done at the application, database, storage, and operating system levels to further improve performance.
Cleveland Hadoop Users Group - Spark
Spark is a unified analytics engine for large-scale data processing. It provides APIs for SQL queries, streaming data, and machine learning. Spark uses RDDs (Resilient Distributed Datasets) as its fundamental data abstraction, which allows data to be operated on in parallel. RDDs track lineage information to efficiently recover lost data. Spark offers advantages over MapReduce like being faster, using less code, and supporting iterative algorithms. It can also be used for both batch and streaming workloads using the same APIs. While still maturing, Spark is gaining popularity for its ease of use and performance.
Similar to Getting Started with Apache Cassandra and Apache Zeppelin (DuyHai DOAN, DataStax) | C* Summit 2016 (20)
Project Avatar (Lyon JUG & Alpes JUG - March 2014)
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Java EE 7 et ensuite pourquoi pas JavaScript sur le serveur!
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Low Latency SQL on Hadoop - What's best for your cluster
Low Latency SQL on Hadoop - What's best for your cluster
Dr. Ike Nassi, Founder, TidalScale at MLconf NYC - 4/15/16
Dr. Ike Nassi, Founder, TidalScale at MLconf NYC - 4/15/16
OpenStack and NetApp - Chen Reuven - OpenStack Day Israel 2017
OpenStack and NetApp - Chen Reuven - OpenStack Day Israel 2017
Jaroslav Tulach: GraalVM - z vývoje nejrychlejšího virtuálního stroje na světě
Jaroslav Tulach: GraalVM - z vývoje nejrychlejšího virtuálního stroje na světě
Oracle RAC on Extended Distance Clusters - Presentation
Oracle RAC on Extended Distance Clusters - Presentation
Oracle RAC and Your Way to the Cloud by Angelo Pruscino
Oracle RAC and Your Way to the Cloud by Angelo Pruscino
Unlock cassandra data for application developers using graphQL
Unlock cassandra data for application developers using graphQL
Data processing at the speed of 100 Gbps@Apache Crail (Incubating)
Data processing at the speed of 100 Gbps@Apache Crail (Incubating)
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Be a holiday hero—not a sorry statistic. View this on-demand webinar to learn how to drive revenue, business growth, customer satisfaction, and loyalty during the holiday season, and achieve operational excellence (and sanity!) at the same time. You’ll also hear real-world stories of companies that have experienced Black Friday nightmares—and learn how they turned things back around.
View webinar: https://pages.datastax.com/20191003-NAM-Webinar-IsYourEnterpriseReadytoShinethisHolidaySeason_1-Registration-LP.html
Explore all DataStax webinars: www.datastax.com/webinars
Designing Fault-Tolerant Applications with DataStax Enterprise and Apache Cas...
Data resiliency and availability are mission-critical for enterprises today—yet we live in a world where outages are an everyday occurrence. Whether the problem is a single server failure or losing connectivity to an entire data center, if your applications aren’t designed to be fault tolerant, recovery from an outage can be painful and slow. Watch this on-demand webinar to look at best practices for developing fault-tolerant applications with DataStax Drivers for Apache Cassandra and DataStax Enterprise (DSE).
View recording: https://youtu.be/NT2-i3u5wo0
Explore all DataStax webinars: https://www.datastax.com/resources/webinars
Running DataStax Enterprise in VMware Cloud and Hybrid Environments
To simplify deploying and managing modern applications, enterprises have been combining the benefits of hyperconverged infrastructure (HCI) with the performance and scale of a NoSQL database — and the results have been remarkable. With this combination, IT organizations have experienced more agility, improved reliability, and better application performance. Watch this on-demand webinar where you’ll learn specifically how VMware HCI with DataStax Enterprise (DSE) and Apache Cassandra™ are transforming the enterprise.
View recording: https://youtu.be/FCLGHMIB0L4
Explore all DataStax Webinars: https://www.datastax.com/resources/webinars
Best Practices for Getting to Production with DataStax Enterprise Graph
The document provides five tips for getting DataStax Enterprise Graph into production:
1) Know your data distributions and important relationships.
2) Understand your access patterns and model the data for common queries.
3) Optimize query performance by filtering vertices, choosing starting points to reduce edges traversed, and adding shortcuts.
4) Design a supernode strategy such as modeling supernodes as properties, adding edge indexes, or making vertices more granular.
5) Embrace a multi-model approach using the best tool like DSE Graph for complex connected data queries.
Webinar | Data Management for Hybrid and Multi-Cloud: A Four-Step Journey
Data management may be the hardest part of making the transition to the cloud, but enterprises including Intuit and Macy’s have figured out how to do it right. So what do they know that you might not? Join Robin Schumacher, Chief Product Officer at DataStax as he explores best practices for defining and implementing data management strategies for the cloud. He outlines a four-step journey that will take you from your first deployment in the cloud through to a true intercloud implementation and walk through a real-world use case where a major retailer has evolved through the four phases over a period of four years and is now benefiting from a highly resilient multi-cloud deployment.
View webinar: https://youtu.be/RrTxQ2BAxjg
Webinar | How to Understand Apache Cassandra™ Performance Through Read/Writ...
In this webinar, you will leverage free and open source tools as well as enterprise-grade utilities developed by DataStax to get a solid grasp on the performance of a masterless distributed database like Cassandra. You’ll also get the opportunity to walk through DataStax Enterprise Insights dashboards and see exactly how to identify performance bottlenecks.
View Recording: https://youtu.be/McZg_MMzVjI
Webinar | Better Together: Apache Cassandra and Apache Kafka
In this webinar, you’ll also be introduced to DataStax Apache Kafka Connector, and get a brief demonstration of this groundbreaking technology. You’ll directly experience how this tool can help you stream data from Kafka topics into DataStax Enterprise versions of Cassandra. The future of your organization won’t wait. Register now to reserve your spot in this exciting new webinar.
Youtube: https://youtu.be/HmkNb8twUNk
Top 10 Best Practices for Apache Cassandra and DataStax Enterprise
No matter how diligent your organization is at driving toward efficiency, databases are complex and it’s easy to make mistakes on your way to production. The good news is, these mistakes are completely avoidable. In this webinar, Jeff Carpenter shares with you exactly how to get started in the right direction — and stay on the path to a successful database launch.
View recording: https://youtu.be/K9Zj3bhjdQg
Explore all DataStax webinars: https://www.datastax.com/resources/webinars
Webinar: How Active Everywhere Database Architecture Accelerates Hybrid Cloud...
In this webinar, we’ll discuss how an Active Everywhere database—a masterless architecture where multiple servers (or nodes) are grouped together in a cluster—provides a consistent data fabric between on-premises data centers and public clouds, enabling enterprises to effortlessly scale their hybrid cloud deployments and easily transition to the new hybrid cloud world, without changes to existing applications.
View recording: https://youtu.be/ob6tr-9YiF4
Webinar | Aligning GDPR Requirements with Today's Hybrid Cloud Realities
This webinar discussed how DataStax and Thales eSecurity can help organizations comply with GDPR requirements in today's hybrid cloud environments. The key points are:
1) GDPR compliance and hybrid cloud are realities organizations must address
2) A single "point solution" is insufficient - partnerships between data platform and security services providers are needed
3) DataStax and Thales eSecurity can provide the necessary access controls, authentication, encryption, auditing and other capabilities across disparate environments to meet the 7 key GDPR security requirements.
Designing a Distributed Cloud Database for Dummies
Join Designing a Distributed Cloud Database for Dummies—the webinar. The webinar “stars” industry vet Patrick McFadin, best known among developers for his seven years at Apache Cassandra, where he held pivotal community roles. Register for the webinar today to learn: why you need distributed cloud databases, the technology you need to create the best used experience, the benefits of data autonomy and much more.
View the recording: https://youtu.be/azC7lB0QU7E
To explore all DataStax webinars: https://www.datastax.com/resources/webinars
How to Power Innovation with Geo-Distributed Data Management in Hybrid Cloud
Most enterprises understand the value of hybrid cloud. In fact, your enterprise is already working in a multi-cloud or hybrid cloud environment, whether you know it or not. View this SlideShare to gain a greater understanding of the requirements of a geo-distributed cloud database in hybrid and multi-cloud environments.
View recording: https://youtu.be/tHukS-p6lUI
Explore all DataStax webinars: https://www.datastax.com/resources/webinars
How to Evaluate Cloud Databases for eCommerce
The document discusses how ecommerce companies need to evaluate cloud databases to handle high transaction volumes, real-time processing, and personalized customer experiences. It outlines how DataStax Enterprise (DSE), which is built on Apache Cassandra, provides an always-on, distributed database designed for hybrid cloud environments. DSE allows companies to address the five key dimensions of contextual, always-on, distributed, scalable, and real-time requirements through features like mixed workloads, multi-model flexibility, advanced security, and faster performance. Case studies show how large ecommerce companies like eBay use DSE to power recommendations and handle high volumes of traffic and data.
Webinar: DataStax Enterprise 6: 10 Ways to Multiply the Power of Apache Cassa...
Today’s customers want experiences that are contextual, always on, and above all — delightful. To be able to provide this, enterprises need a distributed, hybrid cloud-ready database that can easily crunch massive volumes of data from disparate sources while offering data autonomy and operational simplicity. Don’t miss this webinar, where you’ll learn how DataStax Enterprise 6 maintains hybrid cloud flexibility with all the benefits of a distributed cloud database, delivers all the advantages of Apache Cassandra with none of the complexities, doubles performance, and provides additional capabilities around robust transactional analytics, graph, search, and more.
View recording: https://youtu.be/tuiWAt2jwBw
Explore all DataStax webinars: https://www.datastax.com/resources/webinars
Webinar: DataStax and Microsoft Azure: Empowering the Right-Now Enterprise wi...
This document discusses the partnership between DataStax and Microsoft Azure to empower enterprises with real-time applications in the cloud. It outlines how hybrid cloud is a strategic imperative, and how the DataStax Enterprise platform combined with Azure provides a hybrid cloud data platform for always-on applications. Examples are given of Microsoft Office 365, Komatsu, and IHS Markit using this solution to power use cases and gain benefits like increased performance, scalability, and cost savings.
Webinar - Real-Time Customer Experience for the Right-Now Enterprise featurin...
Welcome to the Right-Now Economy. To win in the Right-Now Economy, your enterprise needs to be able to provide delightful, always-on, instantaneously responsive applications via a data layer that can handle data rapidly, in real time, and at cloud scale. Don’t miss our upcoming webinar in which Forrester Principal Analyst Brendan Witcher will discuss why a singular, contextual, 360-degree view of the customer in real-time is critical to CX success and how companies are using data to deliver real-time personalization and recommendations.
View recording: https://youtu.be/e6prezfIGMY
Explore all DataStax webinars: https://www.datastax.com/resources/webinars
Datastax - The Architect's guide to customer experience (CX)
The document discusses how DataStax Enterprise can help companies deliver superior customer experiences in the "right-now economy" by providing a unified data layer for customer-related use cases. It describes how DSE provides contextual customer views in real-time, hybrid cloud capabilities, massive scalability and continuous availability, integrated security, and a flexible data model to support evolving customer data needs. The document also provides an example of how Macquarie Bank uses DSE to drive their customer experience initiatives and transform their digital presence.
An Operational Data Layer is Critical for Transformative Banking Applications
Customer expectations are changing fast, while customer-related data is pouring in at an unprecedented rate and volume. Join this webinar, to hear leading experts from DataStax, discuss how DataStax Enterprise, the data management platform trusted by 9 out of the top 15 global banks, enables innovation and industry transformation. They’ll cover how the right data management platform can help break down data silos and modernize old systems of record as an operational data layer that scales to meet the distributed, real-time, always available demands of the enterprise. Register now to learn how the right data management platform allows you to power innovative banking applications, gain instant insight into comprehensive customer interactions, and beat fraud before it happens.
Video: https://youtu.be/319NnKEKJzI
Explore all DataStax webinars: https://www.datastax.com/resources/webinars
Becoming a Customer-Centric Enterprise Via Real-Time Data and Design Thinking
Customer expectations are changing fast, while customer-related data is pouring in at an unprecedented rate and volume. How can you contextualize and analyze all this customer data in real time to meet increasingly demanding customer expectations? Join Mike Rowland, Director and National Practice Leader for CX Strategy at West Monroe Partners, and Kartavya Jain, Product Marketing Manager at DataStax, for an in-depth conversation about how customer experience frameworks, driven by Design Thinking, can help enterprises: understand their customers and their needs, define their strategy for real-time CX, create value from contextual and instant insights.
Innovation Around Data and AI for Fraud Detection
This document discusses data and AI innovations for fraud detection. It provides an overview of ACI Worldwide, a company that provides universal payments solutions and uses machine learning and big data to power fraud detection across payment segments. It also discusses challenges such as sophisticated threats, mobile payments, and data breaches that companies face. Finally, it discusses how ACI addresses challenges through continuous innovation, such as research partnerships and a big data engine that analyzes transactions, profiles, and other data to power fraud detection and other services.
More from DataStax (20)
Is Your Enterprise Ready to Shine This Holiday Season?
Is Your Enterprise Ready to Shine This Holiday Season?
Designing Fault-Tolerant Applications with DataStax Enterprise and Apache Cas...
Designing Fault-Tolerant Applications with DataStax Enterprise and Apache Cas...
Running DataStax Enterprise in VMware Cloud and Hybrid Environments
Running DataStax Enterprise in VMware Cloud and Hybrid Environments
Best Practices for Getting to Production with DataStax Enterprise Graph
Best Practices for Getting to Production with DataStax Enterprise Graph
Webinar | Data Management for Hybrid and Multi-Cloud: A Four-Step Journey
Webinar | Data Management for Hybrid and Multi-Cloud: A Four-Step Journey
Webinar | How to Understand Apache Cassandra™ Performance Through Read/Writ...
Webinar | How to Understand Apache Cassandra™ Performance Through Read/Writ...
Webinar | Better Together: Apache Cassandra and Apache Kafka
Webinar | Better Together: Apache Cassandra and Apache Kafka
Top 10 Best Practices for Apache Cassandra and DataStax Enterprise
Top 10 Best Practices for Apache Cassandra and DataStax Enterprise
Webinar: How Active Everywhere Database Architecture Accelerates Hybrid Cloud...
Webinar: How Active Everywhere Database Architecture Accelerates Hybrid Cloud...
Webinar | Aligning GDPR Requirements with Today's Hybrid Cloud Realities
Webinar | Aligning GDPR Requirements with Today's Hybrid Cloud Realities
Designing a Distributed Cloud Database for Dummies
Designing a Distributed Cloud Database for Dummies
How to Power Innovation with Geo-Distributed Data Management in Hybrid Cloud
How to Power Innovation with Geo-Distributed Data Management in Hybrid Cloud
Webinar: DataStax Enterprise 6: 10 Ways to Multiply the Power of Apache Cassa...
Webinar: DataStax Enterprise 6: 10 Ways to Multiply the Power of Apache Cassa...
Webinar: DataStax and Microsoft Azure: Empowering the Right-Now Enterprise wi...
Webinar: DataStax and Microsoft Azure: Empowering the Right-Now Enterprise wi...
Webinar - Real-Time Customer Experience for the Right-Now Enterprise featurin...
Webinar - Real-Time Customer Experience for the Right-Now Enterprise featurin...
Datastax - The Architect's guide to customer experience (CX)
Datastax - The Architect's guide to customer experience (CX)
An Operational Data Layer is Critical for Transformative Banking Applications
An Operational Data Layer is Critical for Transformative Banking Applications
Becoming a Customer-Centric Enterprise Via Real-Time Data and Design Thinking
Becoming a Customer-Centric Enterprise Via Real-Time Data and Design Thinking
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如何办理(hull学位证书)英国赫尔大学毕业证硕士文凭原版一模一样
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留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
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【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
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国外毕业证学位证成绩单办理方法：
1客户提供办理旧金山大学旧金山大学本科毕业证成绩单信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：我们有专业老师帮你查询）；
2开始安排制作毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
5成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）。
教育部文凭学历认证认证的用途：
如果您计划在国内发展那么办理国内教育部认证是必不可少的。事业性用人单位如银行国企公务员在您应聘时都会需要您提供这个认证。其他私营 #外企企业无需提供！办理教育部认证所需资料众多且烦琐所有材料您都必须提供原件我们凭借丰富的经验帮您快速整合材料让您少走弯路。
实体公司专业为您服务如有需要请联系我: 微信95270640奈一次次令他失望山娃今年岁上五年级识得很多字从走出小屋开始山娃就知道父亲的家和工地共有一个很动听的名字——天河工地的底层空空荡荡很宽阔很凉爽在地上铺上报纸和水泥袋父亲和工人们中午全睡在地上地面坑坑洼洼山娃曾多次绊倒过也曾有长铁钉穿透凉鞋刺在脚板上但山娃不怕工地上也常有五六个从乡下来的小学生他们的父母亲也是高楼上的建筑工人小伙伴来自不同省份都操着带有浓重口音的普通话可不知为啥山娃不仅很快与他们熟识了
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SQL has attained widespread adoption, but Business Intelligence tools still use their own higher level languages based upon a multidimensional paradigm. Composable calculations are what is missing from SQL, and we propose a new kind of column, called a measure, that attaches a calculation to a table. Like regular tables, tables with measures are composable and closed when used in queries.
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Authors: Julian Hyde (Google) and John Fremlin (Google)
https://doi.org/10.1145/3626246.3653374
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Getting Started with Apache Cassandra and Apache Zeppelin (DuyHai DOAN, DataStax) | C* Summit 2016
- 1. Getting started with Apache Cassandra and Apache Zeppelin DuyHai DOAN – Apache Cassandra evangelist
- 2. 1 Apache Zeppelin (quick) introduction 2 Cassandra interpreter 3 Advanced usage 4 Dynamic forms 5 Zeppelin with Spark/Cassandra connector 2© DataStax, All Rights Reserved.
- 3. Apache Zeppelin (quick) intro
- 4. What is Apache Zeppelin ? © DataStax, All Rights Reserved. 4
- 5. © DataStax, All Rights Reserved. 5 Demo
- 6. Zeppelin architecture © DataStax, All Rights Reserved. 6 Zeppelin Server Zeppelin Engine R E S TWebSocket Spark Interpreter Group Spark SparkSQL Zeppelin Interpreter Factory Tajo Interpreter Flink Interpreter Cassandra Interpreter JVM JVM JVM JVM JVM
- 8. Cassandra interpreter architecture © DataStax, All Rights Reserved. 8 Cassandra Interpreter JVM Display Results as HTML ① ② ⑤ Zeppelin Server JVM Raw Text Block Raw Text Block Cassandra Cassandra Java Driver ③ Async CQL statements ④Render HTML ⑥
- 9. © DataStax, All Rights Reserved. 9 Demo
- 10. Advanced Usage
- 11. © DataStax, All Rights Reserved. 11 Demo
- 12. Dynamic Forms
- 13. © DataStax, All Rights Reserved. 13 Demo
- 14. Zeppelin with Spark/Cassandra connector
- 15. Configuration matrix • Vanilla Zeppelin + OSS Spark • Vanilla Zeppelin + Datastax Enterprise (DSE) • Pre-built Zeppelin + OSS Spark • Pre-built Zeppelin + Datastax Enterprise (DSE) © DataStax, All Rights Reserved. 15
- 16. Vanilla Zeppelin + OSS Spark First add Spark/Cassandra connector as dependency for Spark interpreter in Zeppelin © DataStax, All Rights Reserved. 16
- 17. Vanilla Zeppelin + OSS Spark Then configure Spark to include the connector dependencies: $SPARK_HOME/ conf/spark-env.sh Either Or © DataStax, All Rights Reserved. 17
- 18. Vanilla Zeppelin + DSE Only need to add Spark/Cassandra connector as dependency for Spark interpreter in Zeppelin © DataStax, All Rights Reserved. 18
- 19. Pre-built Zeppelin + OSS Spark Only need to configure Spark to include the connector dependencies: $SPARK_HOME/conf/spark-env.sh Either Or © DataStax, All Rights Reserved. 19
- 20. Pre-built Zeppelin + DSE Nothing to do … © DataStax, All Rights Reserved. 20
- 21. Where to get pre-built Zeppelin ? http://goo.gl/8S4Vv6 © DataStax, All Rights Reserved. 21
- 22. © DataStax, All Rights Reserved. 22 Q & A ! "
- 23. © DataStax, All Rights Reserved. 23 Thank You @doanduyhai duy_hai.doan@datastax.com https://academy.datastax.com/