The document discusses options for analyzing semi-structured event data at Coursera. It considers Hive, Pig, and Scalding. Scalding uses Scala and allows joining different data sources and expressing multiple map-reduce jobs in a succinct way. However, it requires learning Scala. An example shows loading event, course, and topic data and joining them to analyze relationships between the data.
Cassandra is used for real-time bidding in online advertising. It processes billions of bid requests per day with low latency requirements. Segment data, which assigns product or service affinity to user groups, is stored in Cassandra to reduce calculations and allow users to be bid on sooner. Tuning the cache size and understanding the active dataset helps optimize performance.
Instaclustr Webinar 50,000 Transactions Per Second with Apache Spark on Apach...Instaclustr
This document describes Instaclustr's implementation of using Apache Spark on Apache Cassandra to monitor over 600 servers running Cassandra and collect metrics over time for tuning, alerting, and automated response systems. Key aspects of the implementation include writing data in 5 minute buckets to Cassandra, using Spark to efficiently roll up the raw data into aggregated metrics on those time intervals, and presenting the data. Optimizations that improved performance included upgrading Cassandra version and leveraging its built-in aggregates in Spark, reducing roll-up job times by 50%.
C* for Deep Learning (Andrew Jefferson, Tracktable) | Cassandra Summit 2016DataStax
A deep learning startup has a requirement for a robust and scalable data architecture. Training a Deep Neural Network requires 10s-100s of millions of examples consisting of data and metadata. In addition to training it is necessary to support test/validation, data exploration and more traditional data science analytics workloads. As a startup we have minimal resources and an engineering team of 1.
Cassandra, Spark and Kafka running on Mesos in AWS is a scalable architecture that is fast and easy to set up and maintain to deliver a data architecture for Deep Learning.
About the Speaker
Andrew Jefferson VP Engineering, Tractable
A software engineer specialising in realtime data systems. I've worked at companies from Startups to Apple on applications ranging from Ticketing to Genetics. Currently building data systems for training and exploiting Deep Neural Networks.
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.
This are the slides from the intensive Cassandra Workshop I held in Madrid as a Meetup: http://www.meetup.com/Madrid-Cassandra-Users/events/225944063/ They cover all the Cassandra core concepts, and data modelling basic ones to get up and running with Cassandra.
The document discusses options for analyzing semi-structured event data at Coursera. It considers Hive, Pig, and Scalding. Scalding uses Scala and allows joining different data sources and expressing multiple map-reduce jobs in a succinct way. However, it requires learning Scala. An example shows loading event, course, and topic data and joining them to analyze relationships between the data.
Cassandra is used for real-time bidding in online advertising. It processes billions of bid requests per day with low latency requirements. Segment data, which assigns product or service affinity to user groups, is stored in Cassandra to reduce calculations and allow users to be bid on sooner. Tuning the cache size and understanding the active dataset helps optimize performance.
Instaclustr Webinar 50,000 Transactions Per Second with Apache Spark on Apach...Instaclustr
This document describes Instaclustr's implementation of using Apache Spark on Apache Cassandra to monitor over 600 servers running Cassandra and collect metrics over time for tuning, alerting, and automated response systems. Key aspects of the implementation include writing data in 5 minute buckets to Cassandra, using Spark to efficiently roll up the raw data into aggregated metrics on those time intervals, and presenting the data. Optimizations that improved performance included upgrading Cassandra version and leveraging its built-in aggregates in Spark, reducing roll-up job times by 50%.
C* for Deep Learning (Andrew Jefferson, Tracktable) | Cassandra Summit 2016DataStax
A deep learning startup has a requirement for a robust and scalable data architecture. Training a Deep Neural Network requires 10s-100s of millions of examples consisting of data and metadata. In addition to training it is necessary to support test/validation, data exploration and more traditional data science analytics workloads. As a startup we have minimal resources and an engineering team of 1.
Cassandra, Spark and Kafka running on Mesos in AWS is a scalable architecture that is fast and easy to set up and maintain to deliver a data architecture for Deep Learning.
About the Speaker
Andrew Jefferson VP Engineering, Tractable
A software engineer specialising in realtime data systems. I've worked at companies from Startups to Apple on applications ranging from Ticketing to Genetics. Currently building data systems for training and exploiting Deep Neural Networks.
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.
This are the slides from the intensive Cassandra Workshop I held in Madrid as a Meetup: http://www.meetup.com/Madrid-Cassandra-Users/events/225944063/ They cover all the Cassandra core concepts, and data modelling basic ones to get up and running with Cassandra.
Hello Cronies,
Here are the slides of our recent meetup. .
Title: It's about Time: Deep dive into event store using Apache Cassandra
Big data At-A-Glance
· What is Big data?
· What we have seen so far in AJM Bigdata series?
· Refresher/Overview of basic terminology
· Where it is? Am I using it?
Introduction to Apache Cassandra
· What, When and Why of Apache Cassandra
· Protocol, Queries, Architecture and everything else
· Who is using Apache Cassandra
· Interesting use cases of Apache Cassandra ( Twitter/ Disqus/ etc.)
· Demo application walk-through
Scylla Summit 2018: From SAP to Scylla - Tracking the Fleet at GPS InsightScyllaDB
Originally using SAP Adaptive Server Enterprise (ASE), the GPS Insight team soon found that relational databases simply aren’t a match for high volume machine data. To top it off, SAP ASE’s clustering technology proved cumbersome to manage and operate. In this presentation, you’ll learn about GPS Insight’s hybrid Scylla deployment that runs on-premises and on AWS datacenter. GPS Insight relies on Scylla to capture and analyze GPS data, offloading data from RDBMS to Scylla for hybrid analytics approach.
The document discusses Apache Cassandra, a distributed database management system designed to handle large amounts of data across many commodity servers. It was developed at Facebook and modeled after Google's Bigtable. The summary discusses key concepts like its use of consistent hashing to distribute data, support for tunable consistency levels, and focus on scalability and availability over traditional SQL features. It also provides an overview of how Cassandra differs from relational databases by not supporting joins, having an optional schema, and using a prematerialized and transaction-less model.
Webinar: Getting Started with Apache CassandraDataStax
Would you like to learn how to use Cassandra but don’t know where to begin? Want to get your feet wet but you’re lost in the desert? Longing for a cluster when you don’t even know how to set up a node? Then look no further! Rebecca Mills, Junior Evangelist at Datastax, will guide you in the webinar “Getting Started with Apache Cassandra...”
You'll get an overview of Planet Cassandra’s resources to get you started quickly and easily. Rebecca will take you down the path that's right for you, whether you are a developer or administrator. Join if you are interested in getting Cassandra up and working in the way that suits you best.
These are the slides from my talk at Hulu in March 2015 discussing Apache Spark & Cassandra. I cover the evolution of data from a single machine to RDBMS (MySQL is the primary example) to big data systems.
On the Spark side, I covered batch jobs, streaming, Apache Kafka, an introduction to machine learning, clustering, logistic regression and recommendations systems (collaborative filtering).
The talk was recorded and is available on youtube: https://www.youtube.com/watch?v=_gFgU3phogQ
We run multiple DataStax Enterprise clusters in Azure each holding 300 TB+ data to deeply understand Office 365 users. In this talk, we will deep dive into some of the key challenges and takeaways faced in running these clusters reliably over a year. To name a few: process crashes, ephemeral SSDs contributing to data loss, slow streaming between nodes, mutation drops, compaction strategy choices, schema updates when nodes are down and backup/restore. We will briefly talk about our contributions back to Cassandra, and our path forward using network attached disks offered via Azure premium storage.
About the Speaker
Anubhav Kale Sr. Software Engineer, Microsoft
Anubhav is a senior software engineer at Microsoft. His team is responsible for building big data platform using Cassandra, Spark and Azure to generate per-user insights of Office 365 users.
Introduction to Real-Time Analytics with Cassandra and HadoopPatricia Gorla
This presentation examines the benefits of using Cassandra to store data, and how the Hadoop ecosystem can fit in to add aggregation functionality to your cluster.
Accompanying code can be found online at bit.ly/1aB8Jy8.
Talk delivered at StrataConf + Hadoop World 2013.
Tales From the Field: The Wrong Way of Using Cassandra (Carlos Rolo, Pythian)...DataStax
Cassandra is a distributed database with features included but not limited to Secundary Indexes, UDF, Materialized Views, etc. and not so strict hardware requirements.
It is important to use those features and select hardware correctly to make sure the use of Cassandra in your business can be as painless as possible.
I will address how these features are used in the wrong way, how hardware should be selected, and how to make Cassandra work in the best possible way.
Learning Objective #1:
Learn that Cassandra hardware requirements exist (and why) and the shortcomings in some of features(Secundary Indexes, Compaction Strategies, etc).
Learning Objective #2:
The most misused features and common hardware errors. How they might seem harmeless at first (either small cluster or even single node).
Learning Objective #3:
How to correctly use Cassandra and it's features and go for perfect operation.
About the Speaker
Carlos Rolo Cassandra Consultant, Pythian
Carlos Rolo is a Cassandra MVP, and has deep expertise with distributed architecture technologies. Carlos is driven by challenge, and enjoys the opportunities to discover new things.. He has become known and trusted by customers and colleagues for his ability to understand complex problems, and to work well under pressure. When Carlos isn't working he can be found playing water polo or enjoying the his local community.
Cassandra Summit 2014: Apache Cassandra Best Practices at EbayDataStax Academy
Presenter: Feng Qu, Principal DBA at eBay
Cassandra has been adopted widely at eBay in recent years and used by many end-user facing applications. I will introduce best practices we have built over the time around system design, capacity planning, deployment automation, monitoring integration, performance analysis and troubleshooting. I will also share our experience working with DataStax support to provide a highly available, highly scalable data store fitting into eBay infrastructure.
DynamoDB is a scalable NoSQL database service provided by Amazon that allows developers to purchase throughput rather than storage. It automatically spreads data and traffic across servers and SSDs for predictable performance. While it does not automatically scale, administrators can request more throughput. DynamoDB integrates with other AWS services like EMR for Hadoop and Redshift for data warehousing.
Best Practices for NoSQL Workloads on Amazon EC2 and Amazon EBS - February 20...Amazon Web Services
Learn how to optimize your NoSQL database on AWS for cost, efficiency, and scale. NoSQL databases are great for modern datasets that require simplicity in design, handle structured and unstructured data, scale horizontally, and offer finer control over availability. With AWS, you have options for running NoSQL on Amazon EC2 with Amazon EBS or on Amazon DynamoDB. This webinar will dive deep into best practices and architectural considerations for designing and managing NoSQL databases like Cassandra, MongoDB, CouchDB, and Aerospike on EC2 and EBS. We will share best practices around instance and volume selection, provide performance tuning hints, and describe cost optimization techniques.
Learning Objectives:
• Learn about common NoSQL database options and use cases for Cassandra, MongoDB, CouchDB, and Aerospike
• Review best practices around architecting on AWS for different NoSQL databases
• Understand the cost vs. performance of different Amazon EC2 instances and Amazon EBS volumes
Cassandra Community Webinar: Apache Spark Analytics at The Weather Channel - ...DataStax Academy
The state of analytics has changed dramatically over the last few years. Hadoop is now commonplace, and the ecosystem has evolved to include new tools such as Spark, Shark, and Drill, that live alongside the old MapReduce-based standards. It can be difficult to keep up with the pace of change, and newcomers are left with a dizzying variety of seemingly similar choices. This is compounded by the number of possible deployment permutations, which can cause all but the most determined to simply stick with the tried and true. But there are serious advantages to many of the new tools, and this presentation will give an analysis of the current state–including pros and cons as well as what’s needed to bootstrap and operate the various options.
About Robbie Strickland, Software Development Manager at The Weather Channel
Robbie works for The Weather Channel’s digital division as part of the team that builds backend services for weather.com and the TWC mobile apps. He has been involved in the Cassandra project since 2010 and has contributed in a variety of ways over the years; this includes work on drivers for Scala and C#, the Hadoop integration, heading up the Atlanta Cassandra Users Group, and answering lots of Stack Overflow questions.
What are the challenges of running Apache Cassandra on Amazon EC2? Is it a good idea?
In this presentation, we explore reasons for and against running the distributed database Cassandra on EC2. We look at the I/O performance of EC2 and
Hadoop + Cassandra: Fast queries on data lakes, and wikipedia search tutorial.Natalino Busa
Today’s services rely on massive amount of data to be processed, but require at the same time to be fast and responsive. Building fast services on big data batch- oriented frameworks is definitely a challenge. At ING, we have worked on a stack that can alleviate this problem. Namely, we materialize data model by map-reducing Hadoop queries from Hive to Cassandra. Instead of sinking the results back to hdfs, we propagate the results into Cassandra key-values tables. Those Cassandra tables are finally exposed via a http API front-end service.
Benchmarking Top NoSQL Databases: Apache Cassandra, Apache HBase and MongoDBAthiq Ahamed
This document provides a summary of a presentation that benchmarked the performance of three popular NoSQL databases: Apache Cassandra, Apache HBase, and MongoDB. It describes the architectures and data models of each database. Benchmark tests were run using the Yahoo Cloud Serving Benchmark and found that Apache Cassandra consistently outperformed the other databases across different workloads in terms of load time, read and write performance, and latency. The presentation emphasizes the importance of benchmarks for evaluating NoSQL database performance and choosing the right database based on application requirements.
Empowering the AWS DynamoDB™ application developer with AlternatorScyllaDB
Getting started with AWS DynamoDB™ is famously easy, but as an application grows and evolves it often starts to struggle with DynamoDB’s limitations. We introduce Scylla’s Alternator, which provides the same API as DynamoDB but aims to empower the application developer. In this presentation we will survey some of Alternator’s developer-centered features: Alternator lets you test and eventually deploy your application anywhere, on any public cloud or private cluster. It efficiently supports multiple tables so it does not require difficult single-table design. Finally, Alternator provides the developer with strong observability tools. The insights provided by these tools can detect bottlenecks, improve performance and even lower its cost.
a comprehensive good introduction to the the Big data world in AWS cloud, hadoop, Streaming, batch, Kinesis, DynamoDB, Hbase, EMR, Athena, Hive, Spark, Piq, Impala, Oozie, Data pipeline, Security , Cost, Best practices
This document provides an overview of Amazon DynamoDB including key concepts like tables, data types, indexes, scaling, data modeling best practices, and example scenarios. It discusses how to design DynamoDB tables for different data access patterns including 1:1, 1:N, and N:M relationships. It also provides recommendations for modeling time series data, popular fast-changing items, and messaging applications.
This document provides an agenda and overview of Big Data Analytics using Spark and Cassandra. It discusses Cassandra as a distributed database and Spark as a data processing framework. It covers connecting Spark and Cassandra, reading and writing Cassandra tables as Spark RDDs, and using Spark SQL, Spark Streaming, and Spark MLLib with Cassandra data. Key capabilities of each technology are highlighted such as Cassandra's tunable consistency and Spark's fault tolerance through RDD lineage. Examples demonstrate basic operations like filtering, aggregating, and joining Cassandra data with Spark.
Hello Cronies,
Here are the slides of our recent meetup. .
Title: It's about Time: Deep dive into event store using Apache Cassandra
Big data At-A-Glance
· What is Big data?
· What we have seen so far in AJM Bigdata series?
· Refresher/Overview of basic terminology
· Where it is? Am I using it?
Introduction to Apache Cassandra
· What, When and Why of Apache Cassandra
· Protocol, Queries, Architecture and everything else
· Who is using Apache Cassandra
· Interesting use cases of Apache Cassandra ( Twitter/ Disqus/ etc.)
· Demo application walk-through
Scylla Summit 2018: From SAP to Scylla - Tracking the Fleet at GPS InsightScyllaDB
Originally using SAP Adaptive Server Enterprise (ASE), the GPS Insight team soon found that relational databases simply aren’t a match for high volume machine data. To top it off, SAP ASE’s clustering technology proved cumbersome to manage and operate. In this presentation, you’ll learn about GPS Insight’s hybrid Scylla deployment that runs on-premises and on AWS datacenter. GPS Insight relies on Scylla to capture and analyze GPS data, offloading data from RDBMS to Scylla for hybrid analytics approach.
The document discusses Apache Cassandra, a distributed database management system designed to handle large amounts of data across many commodity servers. It was developed at Facebook and modeled after Google's Bigtable. The summary discusses key concepts like its use of consistent hashing to distribute data, support for tunable consistency levels, and focus on scalability and availability over traditional SQL features. It also provides an overview of how Cassandra differs from relational databases by not supporting joins, having an optional schema, and using a prematerialized and transaction-less model.
Webinar: Getting Started with Apache CassandraDataStax
Would you like to learn how to use Cassandra but don’t know where to begin? Want to get your feet wet but you’re lost in the desert? Longing for a cluster when you don’t even know how to set up a node? Then look no further! Rebecca Mills, Junior Evangelist at Datastax, will guide you in the webinar “Getting Started with Apache Cassandra...”
You'll get an overview of Planet Cassandra’s resources to get you started quickly and easily. Rebecca will take you down the path that's right for you, whether you are a developer or administrator. Join if you are interested in getting Cassandra up and working in the way that suits you best.
These are the slides from my talk at Hulu in March 2015 discussing Apache Spark & Cassandra. I cover the evolution of data from a single machine to RDBMS (MySQL is the primary example) to big data systems.
On the Spark side, I covered batch jobs, streaming, Apache Kafka, an introduction to machine learning, clustering, logistic regression and recommendations systems (collaborative filtering).
The talk was recorded and is available on youtube: https://www.youtube.com/watch?v=_gFgU3phogQ
We run multiple DataStax Enterprise clusters in Azure each holding 300 TB+ data to deeply understand Office 365 users. In this talk, we will deep dive into some of the key challenges and takeaways faced in running these clusters reliably over a year. To name a few: process crashes, ephemeral SSDs contributing to data loss, slow streaming between nodes, mutation drops, compaction strategy choices, schema updates when nodes are down and backup/restore. We will briefly talk about our contributions back to Cassandra, and our path forward using network attached disks offered via Azure premium storage.
About the Speaker
Anubhav Kale Sr. Software Engineer, Microsoft
Anubhav is a senior software engineer at Microsoft. His team is responsible for building big data platform using Cassandra, Spark and Azure to generate per-user insights of Office 365 users.
Introduction to Real-Time Analytics with Cassandra and HadoopPatricia Gorla
This presentation examines the benefits of using Cassandra to store data, and how the Hadoop ecosystem can fit in to add aggregation functionality to your cluster.
Accompanying code can be found online at bit.ly/1aB8Jy8.
Talk delivered at StrataConf + Hadoop World 2013.
Tales From the Field: The Wrong Way of Using Cassandra (Carlos Rolo, Pythian)...DataStax
Cassandra is a distributed database with features included but not limited to Secundary Indexes, UDF, Materialized Views, etc. and not so strict hardware requirements.
It is important to use those features and select hardware correctly to make sure the use of Cassandra in your business can be as painless as possible.
I will address how these features are used in the wrong way, how hardware should be selected, and how to make Cassandra work in the best possible way.
Learning Objective #1:
Learn that Cassandra hardware requirements exist (and why) and the shortcomings in some of features(Secundary Indexes, Compaction Strategies, etc).
Learning Objective #2:
The most misused features and common hardware errors. How they might seem harmeless at first (either small cluster or even single node).
Learning Objective #3:
How to correctly use Cassandra and it's features and go for perfect operation.
About the Speaker
Carlos Rolo Cassandra Consultant, Pythian
Carlos Rolo is a Cassandra MVP, and has deep expertise with distributed architecture technologies. Carlos is driven by challenge, and enjoys the opportunities to discover new things.. He has become known and trusted by customers and colleagues for his ability to understand complex problems, and to work well under pressure. When Carlos isn't working he can be found playing water polo or enjoying the his local community.
Cassandra Summit 2014: Apache Cassandra Best Practices at EbayDataStax Academy
Presenter: Feng Qu, Principal DBA at eBay
Cassandra has been adopted widely at eBay in recent years and used by many end-user facing applications. I will introduce best practices we have built over the time around system design, capacity planning, deployment automation, monitoring integration, performance analysis and troubleshooting. I will also share our experience working with DataStax support to provide a highly available, highly scalable data store fitting into eBay infrastructure.
DynamoDB is a scalable NoSQL database service provided by Amazon that allows developers to purchase throughput rather than storage. It automatically spreads data and traffic across servers and SSDs for predictable performance. While it does not automatically scale, administrators can request more throughput. DynamoDB integrates with other AWS services like EMR for Hadoop and Redshift for data warehousing.
Best Practices for NoSQL Workloads on Amazon EC2 and Amazon EBS - February 20...Amazon Web Services
Learn how to optimize your NoSQL database on AWS for cost, efficiency, and scale. NoSQL databases are great for modern datasets that require simplicity in design, handle structured and unstructured data, scale horizontally, and offer finer control over availability. With AWS, you have options for running NoSQL on Amazon EC2 with Amazon EBS or on Amazon DynamoDB. This webinar will dive deep into best practices and architectural considerations for designing and managing NoSQL databases like Cassandra, MongoDB, CouchDB, and Aerospike on EC2 and EBS. We will share best practices around instance and volume selection, provide performance tuning hints, and describe cost optimization techniques.
Learning Objectives:
• Learn about common NoSQL database options and use cases for Cassandra, MongoDB, CouchDB, and Aerospike
• Review best practices around architecting on AWS for different NoSQL databases
• Understand the cost vs. performance of different Amazon EC2 instances and Amazon EBS volumes
Cassandra Community Webinar: Apache Spark Analytics at The Weather Channel - ...DataStax Academy
The state of analytics has changed dramatically over the last few years. Hadoop is now commonplace, and the ecosystem has evolved to include new tools such as Spark, Shark, and Drill, that live alongside the old MapReduce-based standards. It can be difficult to keep up with the pace of change, and newcomers are left with a dizzying variety of seemingly similar choices. This is compounded by the number of possible deployment permutations, which can cause all but the most determined to simply stick with the tried and true. But there are serious advantages to many of the new tools, and this presentation will give an analysis of the current state–including pros and cons as well as what’s needed to bootstrap and operate the various options.
About Robbie Strickland, Software Development Manager at The Weather Channel
Robbie works for The Weather Channel’s digital division as part of the team that builds backend services for weather.com and the TWC mobile apps. He has been involved in the Cassandra project since 2010 and has contributed in a variety of ways over the years; this includes work on drivers for Scala and C#, the Hadoop integration, heading up the Atlanta Cassandra Users Group, and answering lots of Stack Overflow questions.
What are the challenges of running Apache Cassandra on Amazon EC2? Is it a good idea?
In this presentation, we explore reasons for and against running the distributed database Cassandra on EC2. We look at the I/O performance of EC2 and
Hadoop + Cassandra: Fast queries on data lakes, and wikipedia search tutorial.Natalino Busa
Today’s services rely on massive amount of data to be processed, but require at the same time to be fast and responsive. Building fast services on big data batch- oriented frameworks is definitely a challenge. At ING, we have worked on a stack that can alleviate this problem. Namely, we materialize data model by map-reducing Hadoop queries from Hive to Cassandra. Instead of sinking the results back to hdfs, we propagate the results into Cassandra key-values tables. Those Cassandra tables are finally exposed via a http API front-end service.
Benchmarking Top NoSQL Databases: Apache Cassandra, Apache HBase and MongoDBAthiq Ahamed
This document provides a summary of a presentation that benchmarked the performance of three popular NoSQL databases: Apache Cassandra, Apache HBase, and MongoDB. It describes the architectures and data models of each database. Benchmark tests were run using the Yahoo Cloud Serving Benchmark and found that Apache Cassandra consistently outperformed the other databases across different workloads in terms of load time, read and write performance, and latency. The presentation emphasizes the importance of benchmarks for evaluating NoSQL database performance and choosing the right database based on application requirements.
Empowering the AWS DynamoDB™ application developer with AlternatorScyllaDB
Getting started with AWS DynamoDB™ is famously easy, but as an application grows and evolves it often starts to struggle with DynamoDB’s limitations. We introduce Scylla’s Alternator, which provides the same API as DynamoDB but aims to empower the application developer. In this presentation we will survey some of Alternator’s developer-centered features: Alternator lets you test and eventually deploy your application anywhere, on any public cloud or private cluster. It efficiently supports multiple tables so it does not require difficult single-table design. Finally, Alternator provides the developer with strong observability tools. The insights provided by these tools can detect bottlenecks, improve performance and even lower its cost.
a comprehensive good introduction to the the Big data world in AWS cloud, hadoop, Streaming, batch, Kinesis, DynamoDB, Hbase, EMR, Athena, Hive, Spark, Piq, Impala, Oozie, Data pipeline, Security , Cost, Best practices
This document provides an overview of Amazon DynamoDB including key concepts like tables, data types, indexes, scaling, data modeling best practices, and example scenarios. It discusses how to design DynamoDB tables for different data access patterns including 1:1, 1:N, and N:M relationships. It also provides recommendations for modeling time series data, popular fast-changing items, and messaging applications.
This document provides an agenda and overview of Big Data Analytics using Spark and Cassandra. It discusses Cassandra as a distributed database and Spark as a data processing framework. It covers connecting Spark and Cassandra, reading and writing Cassandra tables as Spark RDDs, and using Spark SQL, Spark Streaming, and Spark MLLib with Cassandra data. Key capabilities of each technology are highlighted such as Cassandra's tunable consistency and Spark's fault tolerance through RDD lineage. Examples demonstrate basic operations like filtering, aggregating, and joining Cassandra data with Spark.
Scaling web applications with cassandra presentationMurat Çakal
This document provides an introduction and overview of Cassandra, including:
- Cassandra is a distributed database modeled after Amazon Dynamo and Google Bigtable that is highly scalable and fault tolerant.
- It is used by many large companies for applications that require fast writes, high availability, and elastic scalability.
- Cassandra's data model uses a column-oriented design organized into keyspaces, column families, rows, and columns. It also supports super columns.
- The document discusses Cassandra's features like tunable consistency levels, replication, and its data distribution using consistent hashing.
- An overview of Cassandra's Thrift API and basic operations like get, batch mutate, and
This talk will walk through the journey of Cassandra at Netflix. It will go into 3-4 specific use cases where Cassandra stands out than the rest of the data-stores and is being used in Netflix, bringing great viewing experience to all customers globally. Roopa will go into the specifics of the data model being used and where Cassandra stands out with its strengths and which places where they learnt the hard way. Roopa will then share some of the best practices and self service platform being used for Cassandra to cater to their developer needs.
Rafael Bagmanov «Scala in a wild enterprise»e-Legion
This document discusses Scala adoption in the enterprise. It describes how Scala was used to build OpenGenesis, an open-source deployment orchestration tool that was successfully deployed in a large financial institution. While Scala works well with common J2EE patterns like Spring MVC, Spring, and JPA/Squeryl, there are challenges around hiring Scala developers and establishing coding standards. The greatest challenges are cultural and involve people.
Prácticas recomendadas en materia de arquitectura y errores que debes evitarElasticsearch
Crece con confianza. Desde la implementación de un pequeño nodo de desarrollo para la búsqueda de aplicaciones hasta la gestión de una gran implementación de cientos de nodos, nuestros expertos en Elastic te contarán todo lo que necesites saber.
Galera Cluster for MySQL vs MySQL (NDB) Cluster: A High Level Comparison Severalnines
Galera Cluster for MySQL, Percona XtraDB Cluster and MariaDB Cluster (the three “flavours” of Galera Cluster) make use of the Galera WSREP libraries to handle synchronous replication.MySQL Cluster is the official clustering solution from Oracle, while Galera Cluster for MySQL is slowly but surely establishing itself as the de-facto clustering solution in the wider MySQL eco-system.
In this webinar, we will look at all these alternatives and present an unbiased view on their strengths/weaknesses and the use cases that fit each alternative.
This webinar will cover the following:
MySQL Cluster architecture: strengths and limitations
Galera Architecture: strengths and limitations
Deployment scenarios
Data migration
Read and write workloads (Optimistic/pessimistic locking)
WAN/Geographical replication
Schema changes
Management and monitoring
From Postgres to Cassandra (Rimas Silkaitis, Heroku) | C* Summit 2016DataStax
Most web applications start out with a Postgres database and it serves the application very well for an extended period of time. Based on type of application, the data model of the app will have a table that tracks some kind of state for either objects in the system or the users of the application. Names for this table include logs, messages or events. The growth in the number of rows in this table is not linear as the traffic to the app increases, it's typically exponential.
Over time, the state table will increasingly become the bulk of the data volume in Postgres, think terabytes, and become increasingly hard to query. This use case can be characterized as the one-big-table problem. In this situation, it makes sense to move that table out of Postgres and into Cassandra. This talk will walk through the conceptual differences between the two systems, a bit of data modeling, as well as advice on making the conversion.
About the Speaker
Rimas Silkaitis Product Manager, Heroku
Rimas currently runs Product for Heroku Postgres and Heroku Redis but the common thread throughout his career is data. From data analysis, building data warehouses and ultimately building data products, he's held various positions that have allowed him to see the challenges of working with data at all levels of an organization. This experience spans the smallest of startups to the biggest enterprises.
This document discusses using Scala in an enterprise setting. It describes how Scala can be used to build a typical J2EE stack with Spring for the web layer and service layer and Squeryl for the data access layer. While Scala and Spring integration works well with dependency injection, using Scala with Spring templates andAspect Oriented Programming is improved. Squeryl provides benefits as a lightweight ORM such as good support for Scala collections but has some downsides like hard to use native SQL and performance issues. Overall adopting Scala in an enterprise requires overcoming challenges like hiring Scala developers and determining code standards and conventions.
There are many common workloads in R that are "embarrassingly parallel": group-by analyses, simulations, and cross-validation of models are just a few examples. In this talk I'll describe several techniques available in R to speed up workloads like these, by running multiple iterations simultaneously, in parallel.
Many of these techniques require the use of a cluster of machines running R, and I'll provide examples of using cloud-based services to provision clusters for parallel computations. In particular, I will describe how you can use the SparklyR package to distribute data manipulations using the dplyr syntax, on a cluster of servers provisioned in the Azure cloud.
Presented by David Smith at Data Day Texas in Austin, January 27 2018.
MySQL Cluster Scaling to a Billion QueriesBernd Ocklin
MySQL Cluster is a distributed database that provides extreme scalability, high availability, and real-time performance. It uses an auto-sharding and auto-replicating architecture to distribute data across multiple low-cost servers. Key benefits include scaling reads and writes, 99.999% availability through its shared-nothing design with no single point of failure, and real-time responsiveness. It supports both SQL and NoSQL interfaces to enable complex queries as well as high-performance key-value access.
Getting started with Spark & Cassandra by Jon Haddad of DatastaxData Con LA
Massively scalable, always on, and ridiculously fast. Apache Cassandra is the database chosen by Apple, Netflix, and 30 of the Fortune 100 to power their critical infrastructure. How do we analyze petabytes of data, whether it be massive batching or as it’s ingested via streaming with Apache Kafka? Enter Apache Spark. Challenging MapReduce head on, Apache Spark offers powerful constructs that make it possible to slice and dice your data, whether it be through machine learning, graph queries, as well as transformations familiar to people with functional programming backgrounds such as map, filter, and reduce. Step away ready to rock with the most powerful distributed database, scalable messaging, and analytics platform on the planet.
Watch the video here
https://www.youtube.com/watch?v=X-FKmKc9hkI
Best practices for Data warehousing with Amazon Redshift - AWS PS Summit Canb...Amazon Web Services
Get a look under the hood: Understand how to take advantage of Amazon Redshift's columnar technology and parallel processing capabilities to improve your delivery of queries and improve overall database performance. You’ll also hear about how the University of Technology Sydney (UTS) are using Redshift. The University of Technology Sydney will describe how utilizing Amazon Redshift enabled agility in dealing with Data Quality, a capacity to scale when required, and optimizing development processes through rapid provisioning of Data Warehouse environments.
Speaker: Ganesh Raja, Solutions Architect, Amazon Web Services with Susan Gibson, Manager, Data and Business Intelligence, UTS
Level: 300
Vinay Chella presents on Cassandra architecture and scalability at Netflix. Some key points include:
1) Netflix uses Cassandra to store 98% of streaming data. Cassandra clusters are managed using Priam to handle backups, configuration, and cluster operations.
2) Challenges in maintaining Cassandra clusters at scale are addressed through tools like Eunomia for monitoring and predictive analysis. Mantis provides real-time health monitoring through streaming cluster data.
3) Cassandra clusters are deployed on AWS for resilience across instances, availability zones, regions, and cloud providers. Priam handles tasks like automated token assignment and backups to S3 for disaster recovery.
Making (Almost) Any Database Faster and Cheaper with CachingAmazon Web Services
Redis is an in-memory database that can be used for caching to improve database performance. Amazon ElastiCache provides a fully managed Redis service on AWS. Using ElastiCache for caching provides benefits like 34% greater throughput, automatic operations management, high availability, and reliability compared to self-managed Redis. ElastiCache supports Redis data types and clustering to enable horizontal scaling for large datasets and high throughput workloads.
Making (Almost) Any Database Faster and Cheaper with CachingAmazon Web Services
Learn how to make your AWS databases up to 10x faster and up to 90% less expensive with Amazon ElastiCache for Redis. We’ll look at how to determine whether caching will benefit your database environment and show how to easily test and implement a high speed solution.
1) Netflix uses Apache Cassandra as its main data store and has hundreds of Cassandra clusters across multiple regions containing terabytes of customer data for services like viewing history and payments.
2) Maintaining and monitoring Cassandra at Netflix's scale presents challenges around configuration, availability across regions and availability zones, and operating Cassandra in public clouds.
3) Netflix addresses these challenges through tools like Priam for automated bootstrapping and backup/restore, monitoring through services like Mantis and Atlas, and capacity planning with tools like NDBench and Unomia.
Performance Testing: Scylla vs. Cassandra vs. DatastaxScyllaDB
Ticketmaster is part of Live Nation Entertainment, the world's leading live entertainment company. Learn why they went with Scylla after conducting performance testing between Scylla, Apache Cassandra and DataStax Enterprise.
ScyllaDB: What could you do with Cassandra compatibility at 1.8 million reque...Data Con LA
Scylla is a new, open-source NoSQL data store with a novel design optimized for modern hardware, capable of 1.8 million requests per second per node, while providing Apache Cassandra compatibility and scaling properties. While conventional NoSQL databases suffer from latency hiccups, expensive locking, and low throughput due to low processor utilization, the Scylla design is based on a modern shared-nothing approach. Scylla runs multiple engines, one per core, each with its own memory, CPU and multi-queue NIC. The result is a NoSQL database that delivers an order of magnitude more performance, with less performance tuning needed from the administrator.
With extra performance to work with, NoSQL projects can have more flexibility to focus on other concerns, such as functionality and time to market. Come for the tech details on what Scylla does under the hood, and leave with some ideas on how to do more with NoSQL, faster.
Speaker bio
Don Marti is technical marketing manager for ScyllaDB. He has written for Linux Weekly News, Linux Journal, and other publications. He co-founded the Linux consulting firm Electric Lichen. Don is a strategic advisor for Mozilla, and has previously served as president and vice president of the Silicon Valley Linux Users Group and on the program committees for Uselinux, Codecon, and LinuxWorld Conference and Expo.
Performance Optimization Case Study: Shattering Hadoop's Sort Record with Spa...Databricks
Performance Optimization Case Study: Shattering Hadoop's Sort Record with Spark and Scala
Talk given by Reynold Xin at Scala Days SF 2015
In this talk, Reynold talks about the underlying techniques used to achieve high performance sorting using Spark and Scala, among which are sun.misc.Unsafe, exploiting cache locality, high-level resource pipelining.
Similar to Cassandra@Coursera: AWS deploy and MySQL transition (20)
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELgerogepatton
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
A review on techniques and modelling methodologies used for checking electrom...nooriasukmaningtyas
The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.
29. Picking a machine
• Memory
• Save some for page
cache!
Author: brutalSoCal
Licence: CC BY-NC-ND 2.0
30. On AWS
• Ephemeral disks.
• Please don’t use EBS. Really.
• IOPS usually the problem
• Instance sizes:
• spinning disk: m1.large, m1.xlarge, m2.4xlarge
• ssd: m3.xlarge, c3.2xlarge, i2.*
31. Set up the machine
• Lots of documentation / talks about this
• Recommended reading: Datastax guide [1]
[1] http://www.datastax.com/documentation/cassandra/2.0/cassandra/install/installRecommendSettings.html
62. Data modeling consulting
• Build core team proficient at C* data modeling
• Available to consult for trickier use cases
63. Libraries / Patterns
• Abstract away simple (but common) use-cases
• Key-value storage
• Simple time series
• Maybe every developer won’t need deep C*
knowledge?
• More radical: data as a service (e.g. STAASH)
STAASH: https://github.com/Netflix/staash
64. It’s a long road
but we’ll get there…
Author: Carissa Rogers
License: CC BY 2.0