This session will discuss how Cassandra/Solr can be used to create real-time analytics platform – jKool.
jKool provides an in-memory analysis of time-series data, automatically performing sequencing, correlation, grouping, enriching, synchronizing, computing, querying and displaying data streams. The session will discuss architecture, challenges and approaches taken to create a real-time analytics platform on top of open source big data analytics platforms: Cassandra, Solr, Kafka & Spark.
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.
Elassandra: Elasticsearch as a Cassandra Secondary Index (Rémi Trouville, Vin...DataStax
Many companies use both elasticsearch and cassandra, typically in the form of logs or time series, but managing many softwares at a large scale can be quite challenging. Elassandra tightly integrates elasticsearch within cassandra as a secondary index, allowing near-realtime search with all existing elasticsearch APIs, plugins and tools like Kibana. We will present the core concepts of elassandra and explain how it draws benefit from internal cassandra features to make elasticsearch masterless, scalable with automatic resharding, more reliable and more efficient than deploying both softwares. We will also explore the bidirectional mapping : the way elasticsearch automatically creates the corresponding cassandra schema and the way elasticsearch indexes an existing cassandra table. Furthermore, we will share some use cases and benchmark results demonstrating practical use of elassandra to scale-out, re-index with zero-downtime, search and visualize data with various tools.
About the Speakers
Remi Trouville Consultant, Independant
Remi is an IT engineer who has worked for the last 8 years in the financial industry as a team manager responsible for all the call-center softwares managing the customer experience. At the end of this period, his team was dealing with 10,000+ agents with 100+ sites and some highly critical business processes such as storage of oral proof sales for transactions. He holds a Master's Degree in Telecommunication engineering and is now following an executive-MBA, in a French business school.
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.
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.
Cassandra Tools and Distributed Administration (Jeffrey Berger, Knewton) | C*...DataStax
At Knewton we operate across five different VPCs a total of 29 clusters, each ranging from 3 nodes to 24 nodes. For a team of three to maintain this is not herculean, however good tools to diagnose issues and gather information in a distributed manner are vital to moving quickly and minimizing engineering time spent.
The database team at Knewton has been successfully using a combination of Ansible and custom open sourced tools to maintain and improve the Cassandra deployment at Knewton. I will be talking about several of these tools and giving examples of how we are using them. Specifically I will discuss the cassandra-tracing tool, which analyzes the contents of the system_traces keyspace, and the cassandra-stat tool, which gives real-time output of the operations of a cassandra cluster. Distributed administration with ad-hoc Ansible will also be covered and I will walk through examples of using these commands to identify and remediate clusterwide issues.
About the Speaker
Jeffrey Berger Lead Database Engineer, Knewton
Dr. Jeffrey Berger is currently the lead database engineer at Knewton, an education tech startup in NYC. He joined the tech scene in NYC in 2013 and spent two years working with MongoDB, becoming a certified MongoDB administrator and a MongoDB Master. He received his Cassandra Administrator certification at Cassandra Summit 2015. He holds a Ph.D. in Theoretical Physics from Penn State and spent several years working on high energy nuclear interactions.
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.
Introduction to data modeling with apache cassandraPatrick McFadin
Are you using relational databases and wonder how to get started with data modeling and Apache Cassandra? Here is a starting tour of how to get started. Translating from the knowledge you already have to the knowledge you need to effective with Cassandra development. We cover patterns and anti-patterns. Get going today!
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.
Elassandra: Elasticsearch as a Cassandra Secondary Index (Rémi Trouville, Vin...DataStax
Many companies use both elasticsearch and cassandra, typically in the form of logs or time series, but managing many softwares at a large scale can be quite challenging. Elassandra tightly integrates elasticsearch within cassandra as a secondary index, allowing near-realtime search with all existing elasticsearch APIs, plugins and tools like Kibana. We will present the core concepts of elassandra and explain how it draws benefit from internal cassandra features to make elasticsearch masterless, scalable with automatic resharding, more reliable and more efficient than deploying both softwares. We will also explore the bidirectional mapping : the way elasticsearch automatically creates the corresponding cassandra schema and the way elasticsearch indexes an existing cassandra table. Furthermore, we will share some use cases and benchmark results demonstrating practical use of elassandra to scale-out, re-index with zero-downtime, search and visualize data with various tools.
About the Speakers
Remi Trouville Consultant, Independant
Remi is an IT engineer who has worked for the last 8 years in the financial industry as a team manager responsible for all the call-center softwares managing the customer experience. At the end of this period, his team was dealing with 10,000+ agents with 100+ sites and some highly critical business processes such as storage of oral proof sales for transactions. He holds a Master's Degree in Telecommunication engineering and is now following an executive-MBA, in a French business school.
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.
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.
Cassandra Tools and Distributed Administration (Jeffrey Berger, Knewton) | C*...DataStax
At Knewton we operate across five different VPCs a total of 29 clusters, each ranging from 3 nodes to 24 nodes. For a team of three to maintain this is not herculean, however good tools to diagnose issues and gather information in a distributed manner are vital to moving quickly and minimizing engineering time spent.
The database team at Knewton has been successfully using a combination of Ansible and custom open sourced tools to maintain and improve the Cassandra deployment at Knewton. I will be talking about several of these tools and giving examples of how we are using them. Specifically I will discuss the cassandra-tracing tool, which analyzes the contents of the system_traces keyspace, and the cassandra-stat tool, which gives real-time output of the operations of a cassandra cluster. Distributed administration with ad-hoc Ansible will also be covered and I will walk through examples of using these commands to identify and remediate clusterwide issues.
About the Speaker
Jeffrey Berger Lead Database Engineer, Knewton
Dr. Jeffrey Berger is currently the lead database engineer at Knewton, an education tech startup in NYC. He joined the tech scene in NYC in 2013 and spent two years working with MongoDB, becoming a certified MongoDB administrator and a MongoDB Master. He received his Cassandra Administrator certification at Cassandra Summit 2015. He holds a Ph.D. in Theoretical Physics from Penn State and spent several years working on high energy nuclear interactions.
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.
Introduction to data modeling with apache cassandraPatrick McFadin
Are you using relational databases and wonder how to get started with data modeling and Apache Cassandra? Here is a starting tour of how to get started. Translating from the knowledge you already have to the knowledge you need to effective with Cassandra development. We cover patterns and anti-patterns. Get going today!
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.
Optimizing Your Cluster with Coordinator Nodes (Eric Lubow, SimpleReach) | Ca...DataStax
With the addition of vnodes (Virtual Nodes), Cassandra users were able to gain a few benefits as a result of streaming when it came to bootstrapping and decommissioning nodes. On the flip side, having to route requests on larger clusters became a lot more intensive of a workload for all nodes that were then forced to act coordinator nodes. By setting up a tier of proxy nodes, we were able to have our cluster of 50 nodes perform with a 300% improvement on average in a mixed workload environment. This is an explanation of what we did, how we did it, and why it works.
About the Speaker
Eric Lubow CTO, SimpleReach
Eric Lubow is CTO of SimpleReach, where he builds highly-scalable distributed systems for processing analytics data. Eric is also a DataStax MVP for Cassandra, and co-author of Practical Cassandra. In his spare time, Eric is a skydiver, motorcycle rider, mixed martial artist, and dog dad.
Using Spark to Load Oracle Data into CassandraJim Hatcher
This presentation describes how you can use Spark as an ETL tool to get data from a relational database into Cassandra. I go through the concept in general and then talk about some specific issues you might run into and how to fix them.
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.
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.
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.
Cassandra is the dominant data store used at Netflix and it's health is critical to many of its services. In this talk we will share details of the recent redesign of our health monitoring system and how we leveraged a reactive stream processing system to give us a real-time view our entire fleet while dramatically improving accuracy and reducing false alarms in our alerting.
About the Speaker
Jason Cacciatore Senior Software Engineer, Netflix
Jason Cacciatore is a Senior Software Engineer at Netflix, where he's been working for the past several years. He's interested in stateful distributed systems and has a diverse background in technology. In his spare time he enjoys spending time with his wife and two sons, reading non-fiction, and watching Netflix documentaries.
A brief, but action-packed introduction to DataStax Enterprise Search. In this deck, we'll get an overview of DSE Search's value proposition, see some example CQL search queries, and dive into the details of the indexing and query paths.
What We Learned About Cassandra While Building go90 (Christopher Webster & Th...DataStax
Go90 is a mobile entertainment platform offering access to live and on demand videos. We built the web services platform and social features like activity feed for go90 by making heavy use of Cassandra and Scala, and would like to share what we learned during development and while operating go90. In this presentation, we cover our data model evolution from the initial prototypes to the current production version and the significant performance gain by using a better data model. We will explain how we apply time series data modeling and the benefits of using expiring columns with DateTieredCompactionStrategy. We will also talk about interesting experiences related to table modifications, tombstones and table pagination. On the operations side, we will discuss our findings on java driver usage, performance, monitoring, cluster maintenance, version upgrade, 2-way ssl and many more. We hope you can learn from our mistakes instead of making them yourself!
About the Speakers
Christopher Webster Software Engineer, AOL
Christopher Webster works on the web services platform for the go90 AOL project. Previously he was a Computer Scientist for the Mission Control Technologies project at NASA Ames Center. Chris worked as a senior staff engineer at Sun Microsystems for Project zembly, the cloud development and deployment environment as well as technical lead in many NetBeans projects. Chris is an author of the NetBeans Field Guide and Assemble the Social Web With Zembly.
Thomas Ng Software Engineer, AOL
Thomas Ng is a software engineer at AOL, building web services for the go90 mobile entertainment platform using Cassandra, Scala and Kafka.
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.
DataStax: An Introduction to DataStax Enterprise SearchDataStax Academy
1) Why We Built DSE Search
2) Basics of the Read and Write Paths
3) Fault-tolerance and Adaptive Routing
4) Analytics with Search and Spark
5) Live Indexing
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
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.
Many architects in companies ranging from small startups to publicly traded companies are turning to event-driven architectures to solve mission-critical scalability problems, often ones that carry real-time processing requirements. In this talk we'll demonstrate how you can use Apache Cassandra to build powerful event-driven systems in combination with technologies like Akka, RabbitMQ, and others. These concepts will help you radically simplify the design of complex systems and give you the ability to remain available and responsive even in the face of bursty workloads. If your organization does any sort of stream processing or real-time aggregation work with C*, then this talk is for you.
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.
Optimizing Your Cluster with Coordinator Nodes (Eric Lubow, SimpleReach) | Ca...DataStax
With the addition of vnodes (Virtual Nodes), Cassandra users were able to gain a few benefits as a result of streaming when it came to bootstrapping and decommissioning nodes. On the flip side, having to route requests on larger clusters became a lot more intensive of a workload for all nodes that were then forced to act coordinator nodes. By setting up a tier of proxy nodes, we were able to have our cluster of 50 nodes perform with a 300% improvement on average in a mixed workload environment. This is an explanation of what we did, how we did it, and why it works.
About the Speaker
Eric Lubow CTO, SimpleReach
Eric Lubow is CTO of SimpleReach, where he builds highly-scalable distributed systems for processing analytics data. Eric is also a DataStax MVP for Cassandra, and co-author of Practical Cassandra. In his spare time, Eric is a skydiver, motorcycle rider, mixed martial artist, and dog dad.
Using Spark to Load Oracle Data into CassandraJim Hatcher
This presentation describes how you can use Spark as an ETL tool to get data from a relational database into Cassandra. I go through the concept in general and then talk about some specific issues you might run into and how to fix them.
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.
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.
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.
Cassandra is the dominant data store used at Netflix and it's health is critical to many of its services. In this talk we will share details of the recent redesign of our health monitoring system and how we leveraged a reactive stream processing system to give us a real-time view our entire fleet while dramatically improving accuracy and reducing false alarms in our alerting.
About the Speaker
Jason Cacciatore Senior Software Engineer, Netflix
Jason Cacciatore is a Senior Software Engineer at Netflix, where he's been working for the past several years. He's interested in stateful distributed systems and has a diverse background in technology. In his spare time he enjoys spending time with his wife and two sons, reading non-fiction, and watching Netflix documentaries.
A brief, but action-packed introduction to DataStax Enterprise Search. In this deck, we'll get an overview of DSE Search's value proposition, see some example CQL search queries, and dive into the details of the indexing and query paths.
What We Learned About Cassandra While Building go90 (Christopher Webster & Th...DataStax
Go90 is a mobile entertainment platform offering access to live and on demand videos. We built the web services platform and social features like activity feed for go90 by making heavy use of Cassandra and Scala, and would like to share what we learned during development and while operating go90. In this presentation, we cover our data model evolution from the initial prototypes to the current production version and the significant performance gain by using a better data model. We will explain how we apply time series data modeling and the benefits of using expiring columns with DateTieredCompactionStrategy. We will also talk about interesting experiences related to table modifications, tombstones and table pagination. On the operations side, we will discuss our findings on java driver usage, performance, monitoring, cluster maintenance, version upgrade, 2-way ssl and many more. We hope you can learn from our mistakes instead of making them yourself!
About the Speakers
Christopher Webster Software Engineer, AOL
Christopher Webster works on the web services platform for the go90 AOL project. Previously he was a Computer Scientist for the Mission Control Technologies project at NASA Ames Center. Chris worked as a senior staff engineer at Sun Microsystems for Project zembly, the cloud development and deployment environment as well as technical lead in many NetBeans projects. Chris is an author of the NetBeans Field Guide and Assemble the Social Web With Zembly.
Thomas Ng Software Engineer, AOL
Thomas Ng is a software engineer at AOL, building web services for the go90 mobile entertainment platform using Cassandra, Scala and Kafka.
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.
DataStax: An Introduction to DataStax Enterprise SearchDataStax Academy
1) Why We Built DSE Search
2) Basics of the Read and Write Paths
3) Fault-tolerance and Adaptive Routing
4) Analytics with Search and Spark
5) Live Indexing
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
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.
Many architects in companies ranging from small startups to publicly traded companies are turning to event-driven architectures to solve mission-critical scalability problems, often ones that carry real-time processing requirements. In this talk we'll demonstrate how you can use Apache Cassandra to build powerful event-driven systems in combination with technologies like Akka, RabbitMQ, and others. These concepts will help you radically simplify the design of complex systems and give you the ability to remain available and responsive even in the face of bursty workloads. If your organization does any sort of stream processing or real-time aggregation work with C*, then this talk is for you.
Solr & Cassandra: Searching Cassandra with DataStax EnterpriseDataStax Academy
Wait! Back away from the Cassandra secondary 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 can’t model that in C*, even after watching all of Patrick McFadins data modeling videos. What do I do?” The answer, dear developer, is in DSE Search. With it’s easy Solr API, Lucene indexes (and fault tolerance) you can search data stored in your Cassandra database until your heart’s content. Take my hand. I will show you how.
Stratio: Geospatial and bitemporal search in Cassandra with pluggable Lucene ...DataStax Academy
Stratio presented its open source Lucene-based implementation of Cassandra's secondary indexes at Cassandra Summit London 2014, which provided several search engine features. It used to be distributed as a fork of Apache Cassandra, which was a huge problem both for users and maintainers. Nowadays, due to some changes introduced at Cassandra 2.1.6, we are proud to announce that it has become a plugin that can be attached to the official Apache Cassandra.
With the plugin we have been able to provide Cassandra with geospatial capabilities, making it possible to index geographical positions and perform bounding box and radial distance queries. This is achieved through Lucene's geospatial module.
Another feature we have provided with our plugin is the possibility of indexing bitemporal data models, which distinguish between system time and business time. This way, it is possible to make queries over Cassandra such as ̢give me what system thought in a certain instant about what happened in another instant. The implementation has been performed combining range prefix trees with the 4R-Tree approach exposed by Bliujute, et al.
Both full-text, geospatial and bitemporal queries can be combined with Apache Spark to avoid systematic full-scan, dramatically reducing the amount of data to be processed.
DataStax: How to Roll Cassandra into Production Without Losing your Health, M...DataStax Academy
You know Cassandra works and can solve a lot of problems, but then you try to design it into your application and things start falling apart. Stop! This is where we need to have some real talk. I've been helping organizations implement Cassandra for years. True story. I can help! It's easy to get lost in the details, but making the switch to Cassandra is a journey of many steps. This will be a system of the next 30 years so take your time, do it right and feel the happiness. It's all there for you. Your health and sanity will be intact and most importantly, your job will be better!
DataStax: Old Dogs, New Tricks. Teaching your Relational DBA to fetchDataStax Academy
Do you love some Cassandra, but that relational brain is still on? You aren't alone. Let's take that OLAP data model and get it OLTP. This will be an updated talk with some of the new features brought to you by Cassandra 3.0. Real techniques to translate application patterns into effective models. Common pitfalls that can slow you down and send you running back to RDBMS land. Don't do it! Finally, if you didn't get it right the first time, I'll show you how to fix that data model without any downtime. Turn a hot cup of fail into a tall glass of awesome!
Battery Ventures: Simulating and Visualizing Large Scale Cassandra DeploymentsDataStax Academy
The SimianViz microservices simulator contains a model of Cassandra that allows large scale global deployments to be created and exercised by simulating failure modes and connecting the simulation to real monitoring tools to visualize the effects. The simulator is open source Go code at github.com/adrianco/spigo and is developing rapidly.
Preview of Cassandra 2.2 and 3.0 features. Materialized views, user defined functions, user defined aggregations, new storage engine, rewritten hints, improved vnodes, native JSON support, updated garbage collector.
The internal battle has been fought, and Cassandra is your group's NoSQL platform of choice! Hooray! But now what? Wouldn't it be great to know what NOT to do? Come to this talk to hear about some of the common Ops mistakes that new users make and what the better decision will be.
DataStax & O'Reilly Media: Large Scale Data Analytics with Spark and Cassandr...DataStax Academy
In this in-depth workshop you will gain hands on experience with using Spark and Cassandra inside the DataStax Enterprise Platform. The focus of the workshop will be working through data analytics exercises to understand the major developer developer considerations. You will also gain an understanding of the internals behind the integration that allow for large scale data loading and analysis. It will also review some of the major machine learning libraries in Spark as an example of data analysis.
The workshop will start with a review the basics of how Spark and Cassandra are integrated. Then we will work through a series of exercises that will show how to perform large scale Data Analytics with Spark and Cassandra. A major part of the workshop will be to understand effective data modeling techniques in Cassandra that allow for fast parallel loading of the data into Spark to perform large scale analytics on that data. The exercises will also look at how to how to use the open source Spark Notebook to run interactive data analytics with the DataStax Enterprise Platform.
An introduction to core concepts in Apache Cassandra. We cover the evolution of database architecture as you try to scale a relational database to solve big data problems, and explain how Cassandra handles these problems efficiently.
Diagnosing Problems in Production - CassandraJon Haddad
This presentation covers diagnosing and solving common problems encountered in production, using performance profiling tools. We’ll also give a crash course to basic JVM garbage collection tuning. Readers will leave with a better understanding of what they should look for when they encounter problems with their in-production Cassandra cluster. This presentation is intended for people with a general understanding of Cassandra, but it not required to have experience running it in production.
How jKool Analyzes Streaming Data in Real Time with DataStaxDataStax
In this webinar, Charles Rich, VP of Product Management at jKool will share their journey with DataStax; how jKool knew from the start that traditional relational databases wouldn’t work for the scalability and availability demands of time-series data, and why they turned to DataStax Enterprise for blazing performance and powerful enterprise search and analytics capabilities.
How jKool Analyzes Streaming Data in Real Time with DataStaxjKool
jKool provides an application analytics SaaS for DevOps. These slides illustrate some of the choices we had to make and the architectural decisions to build a system for both real-time and historical application analytics.
Strata Singapore 2017 business use case section
"Big Telco Real-Time Network Analytics"
https://conferences.oreilly.com/strata/strata-sg/public/schedule/detail/62797
VMworld 2013: Virtualizing Databases: Doing IT Right VMworld
VMworld 2013
Michael Corey, Ntirety, Inc
Jeff Szastak, VMware
Learn more about VMworld and register at http://www.vmworld.com/index.jspa?src=socmed-vmworld-slideshare
A brave new world in mutable big data relational storage (Strata NYC 2017)Todd Lipcon
The ever-increasing interest in running fast analytic scans on constantly updating data is stretching the capabilities of HDFS and NoSQL storage. Users want the fast online updates and serving of real-time data that NoSQL offers, as well as the fast scans, analytics, and processing of HDFS. Additionally, users are demanding that big data storage systems integrate natively with their existing BI and analytic technology investments, which typically use SQL as the standard query language of choice. This demand has led big data back to a familiar friend: relationally structured data storage systems.
Todd Lipcon explores the advantages of relational storage and reviews new developments, including Google Cloud Spanner and Apache Kudu, which provide a scalable relational solution for users who have too much data for a legacy high-performance analytic system. Todd explains how to address use cases that fall between HDFS and NoSQL with technologies like Apache Kudu or Google Cloud Spanner and how the combination of relational data models, SQL query support, and native API-based access enables the next generation of big data applications. Along the way, he also covers suggested architectures, the performance characteristics of Kudu and Spanner, and the deployment flexibility each option provides.
The Evolution of the Oracle Database - Then, Now and Later (Fontys Hogeschool...Lucas Jellema
Presentation on the role of the (relational) database in modern enterprise application architecture and on the major themes and development in the evolution of the Oracle Database through the years, up to and including 12c. This presentation was created for and delivered to students in Computer Science at Fontys Hogeschool in Eindhoven on April 25th 2014.
Forrester CXNYC 2017 - Delivering great real-time cx is a true craftDataStax Academy
Companies today are innovating with real-time data to deliver truly amazing customer experiences in the moment. Real-time data management for real-time customer experience is core to staying ahead of competition and driving revenue growth. Join Trays to learn how Comcast is differentiating itself from it's own historical reputation with Customer Experience strategies.
Introduction to DataStax Enterprise Graph DatabaseDataStax Academy
DataStax Enterprise (DSE) Graph is a built to manage, analyze, and search highly connected data. DSE Graph, built on NoSQL Apache Cassandra delivers continuous uptime along with predictable performance and scales for modern systems dealing with complex and constantly changing data.
Download DataStax Enterprise: Academy.DataStax.com/Download
Start free training for DataStax Enterprise Graph: Academy.DataStax.com/courses/ds332-datastax-enterprise-graph
Introduction to DataStax Enterprise Advanced Replication with Apache CassandraDataStax Academy
DataStax Enterprise Advanced Replication supports one-way distributed data replication from remote database clusters that might experience periods of network or internet downtime. Benefiting use cases that require a 'hub and spoke' architecture.
Learn more at http://www.datastax.com/2016/07/stay-100-connected-with-dse-advanced-replication
Advanced Replication docs – https://docs.datastax.com/en/latest-dse/datastax_enterprise/advRep/advRepTOC.html
Data Modeling is the one of the first things to sink your teeth into when trying out a new database. That's why we are going to cover this foundational topic in enough detail for you to get dangerous. Data Modeling for relational databases is more than a touch different than the way it's approached with Cassandra. We will address the quintessential query-driven methodology through a couple of different use cases, including working with time series data for IoT. We will also demo a new tool to get you bootstrapped quickly with MovieLens sample data. This talk should give you the basics you need to get serious with Apache Cassandra.
Hear about how Coursera uses Cassandra as the core of its scalable online education platform. I'll discuss the strengths of Cassandra that we leverage, as well as some limitations that you might run into as well in practice.
In the second part of this talk, we'll dive into how best to effectively use the Datastax Java drivers. We'll dig into how the driver is architected, and use this understanding to develop best practices to follow. I'll also share a couple of interesting bug we've run into at Coursera.
Cassandra @ Sony: The good, the bad, and the ugly part 1DataStax Academy
This talk covers scaling Cassandra to a fast growing user base. Alex and Isaias will cover new best practices and how to work with the strengths and weaknesses of Cassandra at large scale. They will discuss how to adapt to bottlenecks while providing a rich feature set to the playstation community.
Cassandra @ Sony: The good, the bad, and the ugly part 2DataStax Academy
This talk covers scaling Cassandra to a fast growing user base. Alex and Isaias will cover new best practices and how to work with the strengths and weaknesses of Cassandra at large scale. They will discuss how to adapt to bottlenecks while providing a rich feature set to the playstation community.
This is a two part talk in which we'll go over the architecture that enables Apache Cassandra’s linear scalability as well as how DataStax Drivers are able to take full advantage of it to provide developers with nicely designed and speedy clients extendable to the core.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
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CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
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.
2. Objectives
• Store everything, analyze everything…
• Combined real-time & historical analytics
• Fast response, flexible query capabilities
• Target - for business user
• Insulate us from underlying software
• Hide complexity
• Scale for ingesting data-in-motion
• Scale for storing data-at-rest
• Elasticity & Operational efficiency
• Ease of monitoring & management
2
3. Technologies we considered?
• SQL (Oracle, MySQL, etc.)
• No scale. We have had a lot of experience our customer’s issues
with this at our parent company Nastel…
• RAM was “the” bottleneck. Commits take too long and while that is
happening everything else stops
• NoSQL
• Cassandra/Solr (DSE)
• Hadoop/MapReduce
• MongoDB
• Clustered Computing Platforms
• STORM
• MapReduce
• Spark (we learned about this while building jKool)
3
4. Why we chose Cassandra/Solr?
• Pros:
• Simple to setup & scale for clustered deployments
• Scalable, resilient, fault-tolerant (easy replication)
• Ability to have data automatically expire (TTL – necessary for our pricing model)
• Configurable replication strategy
• Great for heavy write workloads
• Write performance was better than Hadoop.
• Insert rate was of paramount importance for us – get data in as fast as possible was
our goal
• Java driver balances the load amongst the nodes in a cluster for us (master-slave
would never have worked for us)
• Solr provides a way to index all incoming data - essential
• DSE provides a nice integration between Cassandra and Solr
• Cons:
• Susceptible to GC pauses (memory management)
• The more memory the more GC pauses
• Less memory and more nodes seems a better approach than one big “honking” server
(we see 6-8GB optimal, so far)
• Data compaction tasks may hang
4
5. Why not Hadoop MapReduce?
• MapReduce too slow for real-time workloads
• Ok for batch, not so great for real-time
• Need to be paired with other technologies for query (Hive/Pig)
• Complex to setup, run and operate
• Our goals were simplicity first…
• Opted for STORM/SPARK wrapped with our own micro
services platform FatPipes instead of the Map Reduce
functionality
5
6. Why we chose Cassandra/Solr vs. Mongo?
• Why not Mongo?
• Global write-lock performance concerns…
• Cassandra/Solr
• Java based (our project was in Java)
• Easy to scale, replicate data,
• Flexible write & write consistency levels (ALL, QUORUM, ANY,
etc.)
• Did we say Java? Yes.(we like Java…)
• Flexible choice of platform coverage
• Great for time-series data streams (market focus for jKool)
• Inherent query limitations in Cassandra solved via Solr
integration (provided with DSE – as mentioned earlier)
6
7. How we achieved near real-time analytics?
• Created our own micro-services architecture (FatPipes)
which runs on top of:
• STORM/JMS/Kafka
• FatPipes can be embedded or distributed
• Real-time Grid
• Feeds tracking data and real-time queries to CEP and back
• User interacts with Real-time via JKQL (jKool Query Language)
• English like query language for analyzing data in motion and at rest.
• “Subscribe” verb for real-time updates
Real-time (Real-time.png)
7
8. Why clustered computing platforms?
• STORM paired with Kafka/JMS and CEP
• Clustered way to process incoming real-time streams
• STORM handles clustering/distribution
• Kafka/JMS for a messaging between grids
• Split streaming workload across the cluster
• Achieve linear scalability for incoming real-time streams
• Apache Spark (alternative to MapReduce)
• For distributing queries and trend analysis
• Micro batching for historical analytics
• Loading large dataset into memory (across different nodes)
• Running queries against large data-sets
8
9. Key to Real-time Analytics
• Process streams as they come while at the same time
avoiding IO
• Streams are split into real-time queue and persistence queue with
eventual consistency (eventually… both real-time and historical
must reconcile)
• Both have to be processed in parallel
• Writing to persistence layer and then analyzing will not achieve
near-real time processing
9
11. Deeper View
Web Application Server Web Application Server Web Application Server
jKool Web Grid
Cassandra
Cassandra
Cassandra
Cassandra
Storage Grid
Solr
Solr
Solr
Solr
Search Grid
Digest, Index
Real-time Grid
JKQL
FatPipes Micro Services (INGEST)
Compute Grid
FatPipes Micro Services (REAL-TIME)
(STORM/CEP)
Distributed Messaging (JMS or Kafka)
11
12. Challenges we ran into?
• So many technology options (…so little time…)
• Deciding on the right combination is key early on
• Cassandra/Solr deployment – (it was a learning experience for us)
• Lots of configuration, memory management, replication options
• Monitoring, managing clusters
• Cassandra/Solr, STORM, Zookeeper, Messaging
• +Leverage parent company’s AutoPilot Technology
• Achieving near real-time analytics proved extremely
challenging – but we did it!
• Keeping track of latencies across cluster
• Estimating computational capacity required to crunch incoming
streams
12