Spark and Cassandra with the Datastax Spark Cassandra Connector
How it works and how to use it!
Missed Spark Summit but Still want to see some slides?
This slide deck is for you!
Escape From Hadoop: Spark One Liners for C* OpsRussell Spitzer
Apache Cassandra and Spark when combined can give powerful OLTP and OLAP functionality for your data. We’ll walk through the basics of both of these platforms before diving into applications combining the two. Usually joins, changing a partition key, or importing data can be difficult in Cassandra, but we’ll see how do these and other operations in a set of simple Spark Shell one-liners!
Escape From Hadoop: Spark One Liners for C* OpsRussell Spitzer
Apache Cassandra and Spark when combined can give powerful OLTP and OLAP functionality for your data. We’ll walk through the basics of both of these platforms before diving into applications combining the two. Usually joins, changing a partition key, or importing data can be difficult in Cassandra, but we’ll see how do these and other operations in a set of simple Spark Shell one-liners!
Apache cassandra and spark. you got the the lighter, let's start the firePatrick McFadin
Introduction to analyzing Apache Cassandra data using Apache Spark. This includes data models, operations topics and the internal on how Spark interfaces with Cassandra.
Owning time series with team apache Strata San Jose 2015Patrick McFadin
Break out your laptops for this hands-on tutorial is geared around understanding the basics of how Apache Cassandra stores and access time series data. We’ll start with an overview of how Cassandra works and how that can be a perfect fit for time series. Then we will add in Apache Spark as a perfect analytics companion. There will be coding as a part of the hands on tutorial. The goal will be to take a example application and code through the different aspects of working with this unique data pattern. The final section will cover the building of an end-to-end data pipeline to ingest, process and store high speed, time series data.
An Introduction to time series with Team ApachePatrick McFadin
We as an industry are collecting more data every year. IoT, web, and mobile applications send torrents of bits to our data centers that have to be processed and stored, even as users expect an always-on experience—leaving little room for error. Patrick McFadin explores how successful companies do this every day using the powerful Team Apache: Apache Kafka, Spark, and Cassandra.
Patrick walks you through organizing a stream of data into an efficient queue using Apache Kafka, processing the data in flight using Apache Spark Streaming, storing the data in a highly scaling and fault-tolerant database using Apache Cassandra, and transforming and finding insights in volumes of stored data using Apache Spark.
Topics include:
- Understanding the right use case
- Considerations when deploying Apache Kafka
- Processing streams with Apache Spark Streaming
- A deep dive into how Apache Cassandra stores data
- Integration between Cassandra and Spark
- Data models for time series
- Postprocessing without ETL using Apache Spark on Cassandra
Time series with Apache Cassandra - Long versionPatrick McFadin
Apache Cassandra has proven to be one of the best solutions for storing and retrieving time series data. This talk will give you an overview of the many ways you can be successful. We will discuss how the storage model of Cassandra is well suited for this pattern and go over examples of how best to build data models.
Maximum Overdrive: Tuning the Spark Cassandra Connector (Russell Spitzer, Dat...DataStax
Worried that you aren't taking full advantage of your Spark and Cassandra integration? Well worry no more! In this talk we'll take a deep dive into all of the available configuration options and see how they affect Cassandra and Spark performance. Concerned about throughput? Learn to adjust batching parameters and gain a boost in speed. Always running out of memory? We'll take a look at the various causes of OOM errors and how we can circumvent them. Want to take advantage of Cassandra's natural partitioning in Spark? Find out about the recent developments that let you perform shuffle-less joins on Cassandra-partitioned data! Come with your questions and problems and leave with answers and solutions!
About the Speaker
Russell Spitzer Software Engineer, DataStax
Russell Spitzer received a Ph.D in Bio-Informatics before finding his deep passion for distributed software. He found the perfect outlet for this passion at DataStax where he began on the Automation and Test Engineering team. He recently moved from finding bugs to making bugs as part of the Analytics team where he works on integration between Cassandra and Spark as well as other tools.
Storing time series data with Apache CassandraPatrick McFadin
If you are looking to collect and store time series data, it's probably not going to be small. Don't get caught without a plan! Apache Cassandra has proven itself as a solid choice now you can learn how to do it. We'll look at possible data models and the the choices you have to be successful. Then, let's open the hood and learn about how data is stored in Apache Cassandra. You don't need to be an expert in distributed systems to make this work and I'll show you how. I'll give you real-world examples and work through the steps. Give me an hour and I will upgrade your time series game.
Nike Tech Talk: Double Down on Apache Cassandra and SparkPatrick McFadin
Apache Cassandra has proven to be one of the best solutions for storing and retrieving time series data at high velocity and high volume. This talk will give you an overview of the many ways you can be successful by introducing Apache Cassandra concepts. We will discuss how the storage model of Cassandra is well suited for this pattern and go over examples of how best to build data models. There will also be examples of how you can use Apache Spark along with Apache Cassandra to create a real time data analytics platform. It’s so easy, you will be shocked and ready to try it yourself.
You’ve heard all of the hype, but how can SMACK work for you? In this all-star lineup, you will learn how to create a reactive, scaling, resilient and performant data processing powerhouse. We will go through the basics of Akka, Kafka and Mesos and then deep dive into putting them together in an end2end (and back again) distrubuted transaction. Distributed transactions mean producers waiting for one or more of consumers to respond. On the backend, you will see how Apache Cassandra and Spark can be combined to add the incredibly scaling storage and data analysis needed for fast data pipelines. With these technologies as a foundation, you have the assurance that scale is never a problem and uptime is default.
Managing large volumes of data isn’t trivial and needs a plan. Fast Data is how we describe the nature of data in a heavily consumer-driven world. Fast in. Fast out. Is your data infrastructure ready? You will learn some important reference architectures for large-scale data problems. The three main areas are covered:
Organize - Manage the incoming data stream and ensure it is processed correctly and on time. No data left behind.
Process - Analyze volumes of data you receive in near real-time or in a batch. Be ready for fast serving in your application.
Store - Reliably store data in the data models to support your application. Never accept downtime or slow response times.
Lessons from Cassandra & Spark (Matthias Niehoff & Stephan Kepser, codecentri...DataStax
We built an application based on the principles of CQRS and Event Sourcing using Cassandra and Spark. During the project we encountered a number of challenges and problems with Cassandra and the Spark Connector.
In this talk we want to outline a few of those problems and our actions to solve them. While some problems are specific to CQRS and Event Sourcing applications most of them are use case independent.
About the Speakers
Matthias Niehoff IT-Consultant, codecentric AG
works as an IT-Consultant at codecentric AG in Germany. His focus is on big data & streaming applications with Apache Cassandra & Apache Spark. Yet he does not lose track of other tools in the area of big data. Matthias shares his experiences on conferences, meetups and usergroups.
Stephan Kepser Senior IT Consultant and Data Architect, codecentric AG
Dr. Stephan Kepser is an expert on cloud computing and big data. He wrote a couple of journal articles and blog posts on subjects of both fields. His interests reach from legal questions to questions of architecture and design of cloud computing and big data systems to technical details of NoSQL databases.
At this meetup Patrick McFadin, Solutions Architect at DataStax, will be discussing the most recently added features in Apache Cassandra 2.0, including: Lightweight transactions, eager retries, improved compaction, triggers, and CQL cursors. He'll also be touching on time series data with Apache Cassandra.
Escape from Hadoop: Ultra Fast Data Analysis with Spark & CassandraPiotr Kolaczkowski
We present the basic functionality of the official DataStax spark-cassandra connector - how to load cassandra tables as Spark RDDs and how to save Spark RDDs to Cassandra.
Apache cassandra and spark. you got the the lighter, let's start the firePatrick McFadin
Introduction to analyzing Apache Cassandra data using Apache Spark. This includes data models, operations topics and the internal on how Spark interfaces with Cassandra.
Owning time series with team apache Strata San Jose 2015Patrick McFadin
Break out your laptops for this hands-on tutorial is geared around understanding the basics of how Apache Cassandra stores and access time series data. We’ll start with an overview of how Cassandra works and how that can be a perfect fit for time series. Then we will add in Apache Spark as a perfect analytics companion. There will be coding as a part of the hands on tutorial. The goal will be to take a example application and code through the different aspects of working with this unique data pattern. The final section will cover the building of an end-to-end data pipeline to ingest, process and store high speed, time series data.
An Introduction to time series with Team ApachePatrick McFadin
We as an industry are collecting more data every year. IoT, web, and mobile applications send torrents of bits to our data centers that have to be processed and stored, even as users expect an always-on experience—leaving little room for error. Patrick McFadin explores how successful companies do this every day using the powerful Team Apache: Apache Kafka, Spark, and Cassandra.
Patrick walks you through organizing a stream of data into an efficient queue using Apache Kafka, processing the data in flight using Apache Spark Streaming, storing the data in a highly scaling and fault-tolerant database using Apache Cassandra, and transforming and finding insights in volumes of stored data using Apache Spark.
Topics include:
- Understanding the right use case
- Considerations when deploying Apache Kafka
- Processing streams with Apache Spark Streaming
- A deep dive into how Apache Cassandra stores data
- Integration between Cassandra and Spark
- Data models for time series
- Postprocessing without ETL using Apache Spark on Cassandra
Time series with Apache Cassandra - Long versionPatrick McFadin
Apache Cassandra has proven to be one of the best solutions for storing and retrieving time series data. This talk will give you an overview of the many ways you can be successful. We will discuss how the storage model of Cassandra is well suited for this pattern and go over examples of how best to build data models.
Maximum Overdrive: Tuning the Spark Cassandra Connector (Russell Spitzer, Dat...DataStax
Worried that you aren't taking full advantage of your Spark and Cassandra integration? Well worry no more! In this talk we'll take a deep dive into all of the available configuration options and see how they affect Cassandra and Spark performance. Concerned about throughput? Learn to adjust batching parameters and gain a boost in speed. Always running out of memory? We'll take a look at the various causes of OOM errors and how we can circumvent them. Want to take advantage of Cassandra's natural partitioning in Spark? Find out about the recent developments that let you perform shuffle-less joins on Cassandra-partitioned data! Come with your questions and problems and leave with answers and solutions!
About the Speaker
Russell Spitzer Software Engineer, DataStax
Russell Spitzer received a Ph.D in Bio-Informatics before finding his deep passion for distributed software. He found the perfect outlet for this passion at DataStax where he began on the Automation and Test Engineering team. He recently moved from finding bugs to making bugs as part of the Analytics team where he works on integration between Cassandra and Spark as well as other tools.
Storing time series data with Apache CassandraPatrick McFadin
If you are looking to collect and store time series data, it's probably not going to be small. Don't get caught without a plan! Apache Cassandra has proven itself as a solid choice now you can learn how to do it. We'll look at possible data models and the the choices you have to be successful. Then, let's open the hood and learn about how data is stored in Apache Cassandra. You don't need to be an expert in distributed systems to make this work and I'll show you how. I'll give you real-world examples and work through the steps. Give me an hour and I will upgrade your time series game.
Nike Tech Talk: Double Down on Apache Cassandra and SparkPatrick McFadin
Apache Cassandra has proven to be one of the best solutions for storing and retrieving time series data at high velocity and high volume. This talk will give you an overview of the many ways you can be successful by introducing Apache Cassandra concepts. We will discuss how the storage model of Cassandra is well suited for this pattern and go over examples of how best to build data models. There will also be examples of how you can use Apache Spark along with Apache Cassandra to create a real time data analytics platform. It’s so easy, you will be shocked and ready to try it yourself.
You’ve heard all of the hype, but how can SMACK work for you? In this all-star lineup, you will learn how to create a reactive, scaling, resilient and performant data processing powerhouse. We will go through the basics of Akka, Kafka and Mesos and then deep dive into putting them together in an end2end (and back again) distrubuted transaction. Distributed transactions mean producers waiting for one or more of consumers to respond. On the backend, you will see how Apache Cassandra and Spark can be combined to add the incredibly scaling storage and data analysis needed for fast data pipelines. With these technologies as a foundation, you have the assurance that scale is never a problem and uptime is default.
Managing large volumes of data isn’t trivial and needs a plan. Fast Data is how we describe the nature of data in a heavily consumer-driven world. Fast in. Fast out. Is your data infrastructure ready? You will learn some important reference architectures for large-scale data problems. The three main areas are covered:
Organize - Manage the incoming data stream and ensure it is processed correctly and on time. No data left behind.
Process - Analyze volumes of data you receive in near real-time or in a batch. Be ready for fast serving in your application.
Store - Reliably store data in the data models to support your application. Never accept downtime or slow response times.
Lessons from Cassandra & Spark (Matthias Niehoff & Stephan Kepser, codecentri...DataStax
We built an application based on the principles of CQRS and Event Sourcing using Cassandra and Spark. During the project we encountered a number of challenges and problems with Cassandra and the Spark Connector.
In this talk we want to outline a few of those problems and our actions to solve them. While some problems are specific to CQRS and Event Sourcing applications most of them are use case independent.
About the Speakers
Matthias Niehoff IT-Consultant, codecentric AG
works as an IT-Consultant at codecentric AG in Germany. His focus is on big data & streaming applications with Apache Cassandra & Apache Spark. Yet he does not lose track of other tools in the area of big data. Matthias shares his experiences on conferences, meetups and usergroups.
Stephan Kepser Senior IT Consultant and Data Architect, codecentric AG
Dr. Stephan Kepser is an expert on cloud computing and big data. He wrote a couple of journal articles and blog posts on subjects of both fields. His interests reach from legal questions to questions of architecture and design of cloud computing and big data systems to technical details of NoSQL databases.
At this meetup Patrick McFadin, Solutions Architect at DataStax, will be discussing the most recently added features in Apache Cassandra 2.0, including: Lightweight transactions, eager retries, improved compaction, triggers, and CQL cursors. He'll also be touching on time series data with Apache Cassandra.
Escape from Hadoop: Ultra Fast Data Analysis with Spark & CassandraPiotr Kolaczkowski
We present the basic functionality of the official DataStax spark-cassandra connector - how to load cassandra tables as Spark RDDs and how to save Spark RDDs to Cassandra.
A rundown of the key findings and overall themes of InvestmentNews' flagship adviser technology benchmarking study, addressing the driving factors behind how and why firms invest in technology, the factors they weigh the closest in making those key decisions, and the strategies they implement to better their firms.
InvestmentNews Research collected firm-level data from over 300 independent advisory firms in December 2014 - January 2014. Firms submitted truncated P&L data, including measures of operating expenses, profits, revenue, assets, client and staff. "Top Performers", or the most profitable and productive firms, and "Innovators", the firms who utilized the most technology at their firms, were identified as cohorts used to benchmark the industry.
During the period of internship the security markte was down by almost 5.9%, but the research reports generated calls with an accuracy level of 69% with an return of 4%.
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IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Analyzing Time Series Data with Apache Spark and CassandraPatrick McFadin
You have collected a lot of time series data so now what? It's not going to be useful unless you can analyze what you have. Apache Spark has become the heir apparent to Map Reduce but did you know you don't need Hadoop? Apache Cassandra is a great data source for Spark jobs! Let me show you how it works, how to get useful information and the best part, storing analyzed data back into Cassandra. That's right. Kiss your ETL jobs goodbye and let's get to analyzing. This is going to be an action packed hour of theory, code and examples so caffeine up and let's go.
A Cassandra + Solr + Spark Love Triangle Using DataStax EnterprisePatrick McFadin
Wait! Back away from the Cassandra 2ndary index. It’s ok for some use cases, but it’s not an easy button. "But I need to search through a bunch of columns to look for the data and I want to do some regression analysis… and I can’t model that in C*, even after watching all of Patrick McFadins videos. What do I do?” The answer, dear developer, is in DSE Search and Analytics. With it’s easy Solr API and Spark integration so you can search and analyze data stored in your Cassandra database until your heart’s content. Take our hand. WE will show you how.
Analyzing Time-Series Data with Apache Spark and Cassandra - StampedeCon 2016StampedeCon
Have you ever wanted to analyze sensor data that arrives every second from across the world? Or maybe your want to analyze intra-day trading prices of millions of financial instruments? Or take all the page views from Wikipedia and compare the hourly statistics? To do this or any other similar analysis, you will need to analyze large sequences of measurements over time. And what better way to do this then with Apache Spark? In this session we will dig into how to consume data, and analyze it with Spark, and then store the results in Apache Cassandra.
Beyond the Query: A Cassandra + Solr + Spark Love Triangle Using Datastax Ent...DataStax Academy
Wait! Back away from the Cassandra 2ndary index. It’s ok for some use cases, but it’s not an easy button. "But I need to search through a bunch of columns to look for the data and I want to do some regression analysis… and I can’t model that in C*, even after watching all of Patrick McFadins videos. What do I do?” The answer, dear developer, is in DSE Search and Analytics. With it’s easy Solr API and Spark integration so you can search and analyze data stored in your Cassandra database until your heart’s content. Take our hand. WE will show you how.
Beyond the Query – Bringing Complex Access Patterns to NoSQL with DataStax - ...StampedeCon
Learn how to model beyond traditional direct access in Apache Cassandra. Utilizing the DataStax platform to harness the power of Spark and Solr to perform search, analytics, and complex operations in place on your Cassandra data!
A Tale of Two APIs: Using Spark Streaming In ProductionLightbend
Fast Data architectures are the answer to the increasing need for the enterprise to process and analyze continuous streams of data to accelerate decision making and become reactive to the particular characteristics of their market.
Apache Spark is a popular framework for data analytics. Its capabilities include SQL-based analytics, dataflow processing, graph analytics and a rich library of built-in machine learning algorithms. These libraries can be combined to address a wide range of requirements for large-scale data analytics.
To address Fast Data flows, Spark offers two API's: The mature Spark Streaming and its younger sibling, Structured Streaming. In this talk, we are going to introduce both APIs. Using practical examples, you will get a taste of each one and obtain guidance on how to choose the right one for your application.
Cassandra ne permet ni jointure, ni agrégats et limite drastiquement vos capacités à requêter vos données pour permettre une scalabilité linéaire dans une architecture masterless. L'outil de choix pour effectuer des traitements analytiques sur vos tables Cassandra est Spark mais ce dernier complexifie des opérations pourtant simples en SQL. SparkSQL permet de retrouver une syntaxe SQL dans Spark et nous allons voir comment l'utiliser en Scala, Java et en Python pour travailler sur des tables Cassandra, et retrouver jointures et agrégats (entre autres).
Apache Cassandra and Spark when combined can give powerful OLTP and OLAP functionality for your data. We’ll walk through the basics of both of these platforms before diving into applications combining the two. Usually joins, changing a partition key, or importing data can be difficult in Cassandra, but we’ll see how do these and other operations in a set of simple Spark Shell one-liners!
Lambda Architecture with Spark, Spark Streaming, Kafka, Cassandra, Akka and S...Helena Edelson
Regardless of the meaning we are searching for over our vast amounts of data, whether we are in science, finance, technology, energy, health care…, we all share the same problems that must be solved: How do we achieve that? What technologies best support the requirements? This talk is about how to leverage fast access to historical data with real time streaming data for predictive modeling for lambda architecture with Spark Streaming, Kafka, Cassandra, Akka and Scala. Efficient Stream Computation, Composable Data Pipelines, Data Locality, Cassandra data model and low latency, Kafka producers and HTTP endpoints as akka actors...
Building a High-Performance Database with Scala, Akka, and SparkEvan Chan
Here is my talk at Scala by the Bay 2016, Building a High-Performance Database with Scala, Akka, and Spark. Covers integration of Akka and Spark, when to use actors and futures, back pressure, reactive monitoring with Kamon, and more.
5 Ways to Use Spark to Enrich your Cassandra EnvironmentJim Hatcher
Apache Cassandra is a powerful system for supporting large-scale, low-latency data systems, but it has some tradeoffs. Apache Spark can help fill those gaps, and this presentation will show you how.
Similar to Spark and Cassandra 2 Fast 2 Furious (20)
Cyaniclab : Software Development Agency Portfolio.pdfCyanic lab
CyanicLab, an offshore custom software development company based in Sweden,India, Finland, is your go-to partner for startup development and innovative web design solutions. Our expert team specializes in crafting cutting-edge software tailored to meet the unique needs of startups and established enterprises alike. From conceptualization to execution, we offer comprehensive services including web and mobile app development, UI/UX design, and ongoing software maintenance. Ready to elevate your business? Contact CyanicLab today and let us propel your vision to success with our top-notch IT solutions.
SOCRadar Research Team: Latest Activities of IntelBrokerSOCRadar
The European Union Agency for Law Enforcement Cooperation (Europol) has suffered an alleged data breach after a notorious threat actor claimed to have exfiltrated data from its systems. Infamous data leaker IntelBroker posted on the even more infamous BreachForums hacking forum, saying that Europol suffered a data breach this month.
The alleged breach affected Europol agencies CCSE, EC3, Europol Platform for Experts, Law Enforcement Forum, and SIRIUS. Infiltration of these entities can disrupt ongoing investigations and compromise sensitive intelligence shared among international law enforcement agencies.
However, this is neither the first nor the last activity of IntekBroker. We have compiled for you what happened in the last few days. To track such hacker activities on dark web sources like hacker forums, private Telegram channels, and other hidden platforms where cyber threats often originate, you can check SOCRadar’s Dark Web News.
Stay Informed on Threat Actors’ Activity on the Dark Web with SOCRadar!
A Comprehensive Look at Generative AI in Retail App Testing.pdfkalichargn70th171
Traditional software testing methods are being challenged in retail, where customer expectations and technological advancements continually shape the landscape. Enter generative AI—a transformative subset of artificial intelligence technologies poised to revolutionize software testing.
Providing Globus Services to Users of JASMIN for Environmental Data AnalysisGlobus
JASMIN is the UK’s high-performance data analysis platform for environmental science, operated by STFC on behalf of the UK Natural Environment Research Council (NERC). In addition to its role in hosting the CEDA Archive (NERC’s long-term repository for climate, atmospheric science & Earth observation data in the UK), JASMIN provides a collaborative platform to a community of around 2,000 scientists in the UK and beyond, providing nearly 400 environmental science projects with working space, compute resources and tools to facilitate their work. High-performance data transfer into and out of JASMIN has always been a key feature, with many scientists bringing model outputs from supercomputers elsewhere in the UK, to analyse against observational or other model data in the CEDA Archive. A growing number of JASMIN users are now realising the benefits of using the Globus service to provide reliable and efficient data movement and other tasks in this and other contexts. Further use cases involve long-distance (intercontinental) transfers to and from JASMIN, and collecting results from a mobile atmospheric radar system, pushing data to JASMIN via a lightweight Globus deployment. We provide details of how Globus fits into our current infrastructure, our experience of the recent migration to GCSv5.4, and of our interest in developing use of the wider ecosystem of Globus services for the benefit of our user community.
top nidhi software solution freedownloadvrstrong314
This presentation emphasizes the importance of data security and legal compliance for Nidhi companies in India. It highlights how online Nidhi software solutions, like Vector Nidhi Software, offer advanced features tailored to these needs. Key aspects include encryption, access controls, and audit trails to ensure data security. The software complies with regulatory guidelines from the MCA and RBI and adheres to Nidhi Rules, 2014. With customizable, user-friendly interfaces and real-time features, these Nidhi software solutions enhance efficiency, support growth, and provide exceptional member services. The presentation concludes with contact information for further inquiries.
AI Pilot Review: The World’s First Virtual Assistant Marketing SuiteGoogle
AI Pilot Review: The World’s First Virtual Assistant Marketing Suite
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https://sumonreview.com/ai-pilot-review/
AI Pilot Review: Key Features
✅Deploy AI expert bots in Any Niche With Just A Click
✅With one keyword, generate complete funnels, websites, landing pages, and more.
✅More than 85 AI features are included in the AI pilot.
✅No setup or configuration; use your voice (like Siri) to do whatever you want.
✅You Can Use AI Pilot To Create your version of AI Pilot And Charge People For It…
✅ZERO Manual Work With AI Pilot. Never write, Design, Or Code Again.
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See My Other Reviews Article:
(1) TubeTrivia AI Review: https://sumonreview.com/tubetrivia-ai-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
Accelerate Enterprise Software Engineering with PlatformlessWSO2
Key takeaways:
Challenges of building platforms and the benefits of platformless.
Key principles of platformless, including API-first, cloud-native middleware, platform engineering, and developer experience.
How Choreo enables the platformless experience.
How key concepts like application architecture, domain-driven design, zero trust, and cell-based architecture are inherently a part of Choreo.
Demo of an end-to-end app built and deployed on Choreo.
Unleash Unlimited Potential with One-Time Purchase
BoxLang is more than just a language; it's a community. By choosing a Visionary License, you're not just investing in your success, you're actively contributing to the ongoing development and support of BoxLang.
Prosigns: Transforming Business with Tailored Technology SolutionsProsigns
Unlocking Business Potential: Tailored Technology Solutions by Prosigns
Discover how Prosigns, a leading technology solutions provider, partners with businesses to drive innovation and success. Our presentation showcases our comprehensive range of services, including custom software development, web and mobile app development, AI & ML solutions, blockchain integration, DevOps services, and Microsoft Dynamics 365 support.
Custom Software Development: Prosigns specializes in creating bespoke software solutions that cater to your unique business needs. Our team of experts works closely with you to understand your requirements and deliver tailor-made software that enhances efficiency and drives growth.
Web and Mobile App Development: From responsive websites to intuitive mobile applications, Prosigns develops cutting-edge solutions that engage users and deliver seamless experiences across devices.
AI & ML Solutions: Harnessing the power of Artificial Intelligence and Machine Learning, Prosigns provides smart solutions that automate processes, provide valuable insights, and drive informed decision-making.
Blockchain Integration: Prosigns offers comprehensive blockchain solutions, including development, integration, and consulting services, enabling businesses to leverage blockchain technology for enhanced security, transparency, and efficiency.
DevOps Services: Prosigns' DevOps services streamline development and operations processes, ensuring faster and more reliable software delivery through automation and continuous integration.
Microsoft Dynamics 365 Support: Prosigns provides comprehensive support and maintenance services for Microsoft Dynamics 365, ensuring your system is always up-to-date, secure, and running smoothly.
Learn how our collaborative approach and dedication to excellence help businesses achieve their goals and stay ahead in today's digital landscape. From concept to deployment, Prosigns is your trusted partner for transforming ideas into reality and unlocking the full potential of your business.
Join us on a journey of innovation and growth. Let's partner for success with Prosigns.
Large Language Models and the End of ProgrammingMatt Welsh
Talk by Matt Welsh at Craft Conference 2024 on the impact that Large Language Models will have on the future of software development. In this talk, I discuss the ways in which LLMs will impact the software industry, from replacing human software developers with AI, to replacing conventional software with models that perform reasoning, computation, and problem-solving.
Enterprise Resource Planning System includes various modules that reduce any business's workload. Additionally, it organizes the workflows, which drives towards enhancing productivity. Here are a detailed explanation of the ERP modules. Going through the points will help you understand how the software is changing the work dynamics.
To know more details here: https://blogs.nyggs.com/nyggs/enterprise-resource-planning-erp-system-modules/
Paketo Buildpacks : la meilleure façon de construire des images OCI? DevopsDa...Anthony Dahanne
Les Buildpacks existent depuis plus de 10 ans ! D’abord, ils étaient utilisés pour détecter et construire une application avant de la déployer sur certains PaaS. Ensuite, nous avons pu créer des images Docker (OCI) avec leur dernière génération, les Cloud Native Buildpacks (CNCF en incubation). Sont-ils une bonne alternative au Dockerfile ? Que sont les buildpacks Paketo ? Quelles communautés les soutiennent et comment ?
Venez le découvrir lors de cette session ignite
Gamify Your Mind; The Secret Sauce to Delivering Success, Continuously Improv...Shahin Sheidaei
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Can't make Adobe Summit in Vegas? No sweat because the EMEA Marketo Engage Champions are coming to London to share their Summit sessions, insights and more!
This is a MUG with a twist you don't want to miss.
2. Russell, ostensibly a software engineer
• Did a Ph.D in bioinformatics at some
point
• Written a great deal of automation and
testing framework code
• Now develops for Datastax on the
Analytics Team
• Focuses a lot on the
Datastax OSS Spark Cassandra Connector
3. Datastax Spark Cassandra Connector
Let Spark Interact with your Cassandra Data!
https://github.com/datastax/spark-cassandra-connector
http://spark-packages.org/package/datastax/spark-cassandra-connector
http://spark-packages.org/package/TargetHolding/pyspark-cassandra
Compatible with Spark 1.6 + Cassandra 3.0
• Bulk writing to Cassandra
• Distributed full table scans
• Optimized direct joins with Cassandra
• Secondary index pushdown
• Connection and prepared statement pools
4. Cassandra is essentially a hybrid between a key-value and a column-oriented
(or tabular) database management system. Its data model is a partitioned row
store with tunable consistency*
*https://en.wikipedia.org/wiki/Apache_Cassandra
Let's break that down
1.What is a C* Partition and Row
2.How does C* Place Partitions
5. CQL looks a lot like SQL
CREATE TABLE tracker (
vehicle_id uuid,
ts timestamp,
x double,
y double,
PRIMARY KEY (vehicle_id, ts))
6. INSERTS look almost Identical
CREATE TABLE tracker (
vehicle_id uuid,
ts timestamp,
x double,
y double,
PRIMARY KEY (vehicle_id, ts))
INSERT INTO tracker (vehicle_id, ts, x, y) Values ( 1, 0, 0, 1)
7. Cassandra Data is stored in Partitions
CREATE TABLE tracker (
vehicle_id uuid,
ts timestamp,
x double,
y double,
PRIMARY KEY (vehicle_id, ts))
vehicle_id
1
ts: 0
x: 0 y: 1
INSERT INTO tracker (vehicle_id, ts, x, y) Values ( 1, 0, 0, 1)
11. Within a partition there is ordering based on the
Clustering Keys
CREATE TABLE tracker (
vehicle_id uuid,
ts timestamp,
x double,
y double,
PRIMARY KEY (vehicle_id, ts))
vehicle_id
1
ts: 0
x: 0 y: 1
ts: 1
x: -1 y:0
ts: 2
x: 0 y: -1
ts: 3
x: 1 y: 0
Ordered by Clustering Key
12. Slices within a Partition are Very Easy
CREATE TABLE tracker (
vehicle_id uuid,
ts timestamp,
x double,
y double,
PRIMARY KEY (vehicle_id, ts))
vehicle_id
1
ts: 0
x: 0 y: 1
ts: 1
x: -1 y:0
ts: 2
x: 0 y: -1
ts: 3
x: 1 y: 0
Ordered by Clustering Key
13. Cassandra is a Distributed Fault-Tolerant Database
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14. Data is located on a Token Range
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15. Data is located on a Token Range
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600
16. The Partition Key of a Row is Hashed to Determine
it's Token
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600
17. The Partition Key of a Row is Hashed to Determine
it's Token
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600
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vehicle_id
1
INSERT INTO tracker (vehicle_id, ts, x, y) Values ( 1, 0, 0, 1)
18. The Partition Key of a Row is Hashed to Determine
it's Token
01200
600
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vehicle_id
1
INSERT INTO tracker (vehicle_id, ts, x, y) Values ( 1, 0, 0, 1)
Hash(1) = 1000
20. How The Spark Cassandra Connector Reads
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21. Spark Partitions are Built From the Token Range
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500
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600
600
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700
500 1200
22. Each Partition Can be Drawn Locally from at
Least One Node
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500
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600
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500 1200
23. Each Partition Can be Drawn Locally from at
Least One Node
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500
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600
600
-
700
500 1200
Spark Executor Spark Executor Spark Executor
25. Data is Retrieved using the Datastax Java Driver
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Spark Executor Cassandra
26. A Connection is Established
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Spark Executor Cassandra
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600
27. A Query is Prepared with Token Bounds
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Spark Executor Cassandra
SELECT * FROM TABLE WHERE
Token(PK) > StartToken AND
Token(PK) < EndToken500
-
600
28. The Spark Partitions Bounds are Placed in the Query
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Spark Executor Cassandra
SELECT * FROM TABLE WHERE
Token(PK) > 500 AND
Token(PK) < 600500
-
600
29. Paged a number of rows at a Time
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Spark Executor Cassandra
500
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600
SELECT * FROM TABLE WHERE
Token(PK) > 500 AND
Token(PK) < 600
30. Data is Paged
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Spark Executor Cassandra
500
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600
SELECT * FROM TABLE WHERE
Token(PK) > 500 AND
Token(PK) < 600
31. Data is Paged
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Spark Executor Cassandra
500
-
600
SELECT * FROM TABLE WHERE
Token(PK) > 500 AND
Token(PK) < 600
32. Data is Paged
0
Spark Executor Cassandra
500
-
600
SELECT * FROM TABLE WHERE
Token(PK) > 500 AND
Token(PK) < 600
33. Data is Paged
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Spark Executor Cassandra
500
-
600
SELECT * FROM TABLE WHERE
Token(PK) > 500 AND
Token(PK) < 600
34. How we write to Cassandra
• Data is written via the Datastax Java Driver
• Data is grouped based on Partition Key
(configurable)
• Batches are written
35. Data is Written using the Datastax Java Driver
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39. Once a Batch is Full or There are Too Many Batches
The Largest Batch is Executed
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Spark Executor Cassandra
40. Once a Batch is Full or There are Too Many Batches
The Largest Batch is Executed
0
Spark Executor Cassandra
41. Once a Batch is Full or There are Too Many Batches
The Largest Batch is Executed
0
Spark Executor Cassandra
42. Once a Batch is Full or There are Too Many Batches
The Largest Batch is Executed
0
Spark Executor Cassandra
43. Most Common Features
• RDD APIs
• cassandraTable
• saveToCassandra
• repartitionByCassandraTable
• joinWithCassandraTable
• DF API
• Datasource
44. Full Table Scans, Making an RDD out of a Table
import com.datastax.spark.connector._
sc.cassandraTable(KeyspaceName, TableName)
import com.datastax.spark.connector._
sc.cassandraTable[MyClass](KeyspaceName, TableName)
45. Pushing Down CQL to Cassandra
import com.datastax.spark.connector._
sc.cassandraTable[MyClass](KeyspaceName,
TableName).select("vehicle_id").where("ts > 10")
SELECT "vehicle_id" FROM TABLE
WHERE
Token(PK) > 500 AND
Token(PK) < 600 AND
ts > 10
47. But our Data isn't Colocated
import com.datastax.spark.connector._
rdd.joinWithCassandraTable("keyspace", "table")
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Spark Executor Spark Executor Spark Executor
RDD
48. RBCR Moves bulk reshuffles our data so Data Will
Be Local
rdd
.repartitionByCassandraReplica("keyspace","table")
.joinWithCassandraTable("keyspace", "table")
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Spark Executor Spark Executor Spark Executor
RDD
49. The Connector Provides a Distributed Pool for
Prepared Statements and Sessions
CassandraConnector(sc.getConf)
rdd.mapPartitions{ it => {
val ps = CassandraConnector(sc.getConf)
.withSessionDo( s => s.prepare)
it.map{ ps.bind(_).executeAsync()}
}
Your Pool Ready to Be Deployed
50. The Connector Provides a Distributed Pool for
Prepared Statements and Sessions
CassandraConnector(sc.getConf)
rdd.mapPartitions{ it => {
val ps = CassandraConnector(sc.getConf)
.withSessionDo( s => s.prepare)
it.map{ ps.bind(_).executeAsync()}
}
Your Pool Ready to Be Deployed
Prepared
Statement Cache
Session Cache
51. The Connector Supports the DataSources Api
sqlContext
.read
.format("org.apache.spark.sql.cassandra")
.options(Map("keyspace" -> "read_test", "table" -> "simple_kv"))
.load
import org.apache.spark.sql.cassandra._
sqlContext
.read
.cassandraFormat("read_test","table")
.load
https://github.com/datastax/spark-cassandra-connector/blob/master/doc/14_data_frames.md
52. The Connector Works with Catalyst to
Pushdown Predicates to Cassandra
import com.datastax.spark.connector._
df.select("vehicle_id").filter("ts > 10")
SELECT "vehicle_id" FROM TABLE
WHERE
Token(PK) > 500 AND
Token(PK) < 600 AND
ts > 10
53. The Connector Works with Catalyst to
Pushdown Predicates to Cassandra
import com.datastax.spark.connector._
df.select("vehicle_id").filter("ts > 10")
QueryPlan Catalyst PrunedFilteredScan
Only Prunes (projections) and
Filters (predicates) are able to
be pushed down.
54. Recent Features
• C* 3.0 Support
• Materialized Views
• SASL Indexes (for pushdown)
• Advanced Spark Partitioner Support
• Increased Performance on JWCT
56. Use C* Partitioning in Spark
• C* Data is Partitioned
• Spark has Partitions and partitioners
• Spark can use a known partitioner to speed up
Cogroups (joins)
• How Can we Leverage this?
57. Use C* Partitioning in Spark
https://github.com/datastax/spark-cassandra-connector/blob/master/doc/16_partitioning.md
Now if keyBy is used on a CassandraTableScanRDD and the PartitionKey
is included in the key. The RDD will be given a C* Partitioner
val ks = "doc_example"
val rdd = { sc.cassandraTable[(String, Int)](ks, "users")
.select("name" as "_1", "zipcode" as "_2", "userid")
.keyBy[Tuple1[Int]]("userid")
}
rdd.partitioner
//res3: Option[org.apache.spark.Partitioner] =
Some(com.datastax.spark.connector.rdd.partitioner.CassandraPartitioner@94515d3e)
58. Use C* Partitioning in Spark
https://github.com/datastax/spark-cassandra-connector/blob/master/doc/16_partitioning.md
Share partitioners between Tables for joins on Partition Key
val ks = "doc_example"
val rdd1 = { sc.cassandraTable[(Int, Int, String)](ks, "purchases")
.select("purchaseid" as "_1", "amount" as "_2", "objectid" as "_3", "userid")
.keyBy[Tuple1[Int]]("userid")
}
val rdd2 = { sc.cassandraTable[(String, Int)](ks, "users")
.select("name" as "_1", "zipcode" as "_2", "userid")
.keyBy[Tuple1[Int]]("userid")
}.applyPartitionerFrom(rdd1) // Assigns the partitioner from the first rdd to this one
val joinRDD = rdd1.join(rdd2)
59. Use C* Partitioning in Spark
https://github.com/datastax/spark-cassandra-connector/blob/master/doc/16_partitioning.md
Many other uses for this, try it yourself!
• All self joins using the Partition key
• Groups within C* Partitions
• Anything formerly using SpanBy
• Joins with other RDDs
And much more!
60. Enhanced Parallelism with JWCT
• joinWithCassandraTable now has increased
concurrency and parallelism!
• 5X Speed increases in some cases
• https://datastax-oss.atlassian.net/browse/
SPARKC-233
• Thanks Jaroslaw!
61. The Connector wants you!
• OSS Project that loves community involvement
• Bug Reports
• Feature Requests
• Doc Improvements
• Come join us!
Vin Diesel may or may not be a contributor
62. Tons of Videos at Datastax Academy
https://academy.datastax.com/
https://academy.datastax.com/courses/getting-started-apache-spark
63. Tons of Videos at Datastax Academy
https://academy.datastax.com/
https://academy.datastax.com/courses/getting-started-apache-spark
64. See you at Cassandra Summit!
Join me and 3500 of your database peers, and take a deep dive
into Apache Cassandra™, the massively scalable NoSQL
database that powers global businesses like Apple, Spotify,
Netflix and Sony.
San Jose Convention Center
September 7-9, 2016
https://cassandrasummit.org/
Build Something Disruptive