Update talk on Cassandra at Netflix, presented at the Silicon Valley NoSQL meetup on 9 Feb 2012. Includes an introduction to Astyanax, an open source cassandra client written in java.
The Information Technology have led us into an era where the production, sharing and use of information are now part of everyday life and of which we are often unaware actors almost: it is now almost inevitable not leave a digital trail of many of the actions we do every day; for example, by digital content such as photos, videos, blog posts and everything that revolves around the social networks (Facebook and Twitter in particular). Added to this is that with the "internet of things", we see an increase in devices such as watches, bracelets, thermostats and many other items that are able to connect to the network and therefore generate large data streams. This explosion of data justifies the birth, in the world of the term Big Data: it indicates the data produced in large quantities, with remarkable speed and in different formats, which requires processing technologies and resources that go far beyond the conventional systems management and storage of data. It is immediately clear that, 1) models of data storage based on the relational model, and 2) processing systems based on stored procedures and computations on grids are not applicable in these contexts. As regards the point 1, the RDBMS, widely used for a great variety of applications, have some problems when the amount of data grows beyond certain limits. The scalability and cost of implementation are only a part of the disadvantages: very often, in fact, when there is opposite to the management of big data, also the variability, or the lack of a fixed structure, represents a significant problem. This has given a boost to the development of the NoSQL database. The website NoSQL Databases defines NoSQL databases such as "Next Generation Databases mostly addressing some of the points: being non-relational, distributed, open source and horizontally scalable." These databases are: distributed, open source, scalable horizontally, without a predetermined pattern (key-value, column-oriented, document-based and graph-based), easily replicable, devoid of the ACID and can handle large amounts of data. These databases are integrated or integrated with processing tools based on the MapReduce paradigm proposed by Google in 2009. MapReduce with the open source Hadoop framework represent the new model for distributed processing of large amounts of data that goes to supplant techniques based on stored procedures and computational grids (step 2). The relational model taught courses in basic database design, has many limitations compared to the demands posed by new applications based on Big Data and NoSQL databases that use to store data and MapReduce to process large amounts of data.
Course Website http://pbdmng.datatoknowledge.it/
Contact me for other informations and to download the slides
1. Introduction to the Course "Designing Data Bases with Advanced Data Models...Fabio Fumarola
The Information Technology have led us into an era where the production, sharing and use of information are now part of everyday life and of which we are often unaware actors almost: it is now almost inevitable not leave a digital trail of many of the actions we do every day; for example, by digital content such as photos, videos, blog posts and everything that revolves around the social networks (Facebook and Twitter in particular). Added to this is that with the "internet of things", we see an increase in devices such as watches, bracelets, thermostats and many other items that are able to connect to the network and therefore generate large data streams. This explosion of data justifies the birth, in the world of the term Big Data: it indicates the data produced in large quantities, with remarkable speed and in different formats, which requires processing technologies and resources that go far beyond the conventional systems management and storage of data. It is immediately clear that, 1) models of data storage based on the relational model, and 2) processing systems based on stored procedures and computations on grids are not applicable in these contexts. As regards the point 1, the RDBMS, widely used for a great variety of applications, have some problems when the amount of data grows beyond certain limits. The scalability and cost of implementation are only a part of the disadvantages: very often, in fact, when there is opposite to the management of big data, also the variability, or the lack of a fixed structure, represents a significant problem. This has given a boost to the development of the NoSQL database. The website NoSQL Databases defines NoSQL databases such as "Next Generation Databases mostly addressing some of the points: being non-relational, distributed, open source and horizontally scalable." These databases are: distributed, open source, scalable horizontally, without a predetermined pattern (key-value, column-oriented, document-based and graph-based), easily replicable, devoid of the ACID and can handle large amounts of data. These databases are integrated or integrated with processing tools based on the MapReduce paradigm proposed by Google in 2009. MapReduce with the open source Hadoop framework represent the new model for distributed processing of large amounts of data that goes to supplant techniques based on stored procedures and computational grids (step 2). The relational model taught courses in basic database design, has many limitations compared to the demands posed by new applications based on Big Data and NoSQL databases that use to store data and MapReduce to process large amounts of data.
Course Website http://pbdmng.datatoknowledge.it/
Big Challenges in Data Modeling: NoSQL and Data ModelingDATAVERSITY
Big Data and NoSQL have led to big changes In the data environment, but are they all in the best interest of data? Are they technologies that "free us from the harsh limitations of relational databases?"
In this month's webinar, we will be answering questions like these, plus:
Have we managed to free organizations from having to do Data Modeling?
Is there a need for a Data Modeler on NoSQL projects?
If we build Data Models, which types will work?
If we build Data Models, how will they be used?
If we build Data Models, when will they be used?
Who will use Data Models?
Where does Data Quality happen?
Finally, we will wrap with 10 tips for data modelers in organizations incorporating NoSQL in their modern Data Architectures.
In this lecture we analyze document oriented databases. In particular we consider why there are the first approach to nosql and what are the main features. Then, we analyze as example MongoDB. We consider the data model, CRUD operations, write concerns, scaling (replication and sharding).
Finally we presents other document oriented database and when to use or not document oriented databases.
The Information Technology have led us into an era where the production, sharing and use of information are now part of everyday life and of which we are often unaware actors almost: it is now almost inevitable not leave a digital trail of many of the actions we do every day; for example, by digital content such as photos, videos, blog posts and everything that revolves around the social networks (Facebook and Twitter in particular). Added to this is that with the "internet of things", we see an increase in devices such as watches, bracelets, thermostats and many other items that are able to connect to the network and therefore generate large data streams. This explosion of data justifies the birth, in the world of the term Big Data: it indicates the data produced in large quantities, with remarkable speed and in different formats, which requires processing technologies and resources that go far beyond the conventional systems management and storage of data. It is immediately clear that, 1) models of data storage based on the relational model, and 2) processing systems based on stored procedures and computations on grids are not applicable in these contexts. As regards the point 1, the RDBMS, widely used for a great variety of applications, have some problems when the amount of data grows beyond certain limits. The scalability and cost of implementation are only a part of the disadvantages: very often, in fact, when there is opposite to the management of big data, also the variability, or the lack of a fixed structure, represents a significant problem. This has given a boost to the development of the NoSQL database. The website NoSQL Databases defines NoSQL databases such as "Next Generation Databases mostly addressing some of the points: being non-relational, distributed, open source and horizontally scalable." These databases are: distributed, open source, scalable horizontally, without a predetermined pattern (key-value, column-oriented, document-based and graph-based), easily replicable, devoid of the ACID and can handle large amounts of data. These databases are integrated or integrated with processing tools based on the MapReduce paradigm proposed by Google in 2009. MapReduce with the open source Hadoop framework represent the new model for distributed processing of large amounts of data that goes to supplant techniques based on stored procedures and computational grids (step 2). The relational model taught courses in basic database design, has many limitations compared to the demands posed by new applications based on Big Data and NoSQL databases that use to store data and MapReduce to process large amounts of data.
Course Website http://pbdmng.datatoknowledge.it/
Contact me for other informations and to download the slides
1. Introduction to the Course "Designing Data Bases with Advanced Data Models...Fabio Fumarola
The Information Technology have led us into an era where the production, sharing and use of information are now part of everyday life and of which we are often unaware actors almost: it is now almost inevitable not leave a digital trail of many of the actions we do every day; for example, by digital content such as photos, videos, blog posts and everything that revolves around the social networks (Facebook and Twitter in particular). Added to this is that with the "internet of things", we see an increase in devices such as watches, bracelets, thermostats and many other items that are able to connect to the network and therefore generate large data streams. This explosion of data justifies the birth, in the world of the term Big Data: it indicates the data produced in large quantities, with remarkable speed and in different formats, which requires processing technologies and resources that go far beyond the conventional systems management and storage of data. It is immediately clear that, 1) models of data storage based on the relational model, and 2) processing systems based on stored procedures and computations on grids are not applicable in these contexts. As regards the point 1, the RDBMS, widely used for a great variety of applications, have some problems when the amount of data grows beyond certain limits. The scalability and cost of implementation are only a part of the disadvantages: very often, in fact, when there is opposite to the management of big data, also the variability, or the lack of a fixed structure, represents a significant problem. This has given a boost to the development of the NoSQL database. The website NoSQL Databases defines NoSQL databases such as "Next Generation Databases mostly addressing some of the points: being non-relational, distributed, open source and horizontally scalable." These databases are: distributed, open source, scalable horizontally, without a predetermined pattern (key-value, column-oriented, document-based and graph-based), easily replicable, devoid of the ACID and can handle large amounts of data. These databases are integrated or integrated with processing tools based on the MapReduce paradigm proposed by Google in 2009. MapReduce with the open source Hadoop framework represent the new model for distributed processing of large amounts of data that goes to supplant techniques based on stored procedures and computational grids (step 2). The relational model taught courses in basic database design, has many limitations compared to the demands posed by new applications based on Big Data and NoSQL databases that use to store data and MapReduce to process large amounts of data.
Course Website http://pbdmng.datatoknowledge.it/
Big Challenges in Data Modeling: NoSQL and Data ModelingDATAVERSITY
Big Data and NoSQL have led to big changes In the data environment, but are they all in the best interest of data? Are they technologies that "free us from the harsh limitations of relational databases?"
In this month's webinar, we will be answering questions like these, plus:
Have we managed to free organizations from having to do Data Modeling?
Is there a need for a Data Modeler on NoSQL projects?
If we build Data Models, which types will work?
If we build Data Models, how will they be used?
If we build Data Models, when will they be used?
Who will use Data Models?
Where does Data Quality happen?
Finally, we will wrap with 10 tips for data modelers in organizations incorporating NoSQL in their modern Data Architectures.
In this lecture we analyze document oriented databases. In particular we consider why there are the first approach to nosql and what are the main features. Then, we analyze as example MongoDB. We consider the data model, CRUD operations, write concerns, scaling (replication and sharding).
Finally we presents other document oriented database and when to use or not document oriented databases.
The Information Technology have led us into an era where the production, sharing and use of information are now part of everyday life and of which we are often unaware actors almost: it is now almost inevitable not leave a digital trail of many of the actions we do every day; for example, by digital content such as photos, videos, blog posts and everything that revolves around the social networks (Facebook and Twitter in particular). Added to this is that with the "internet of things", we see an increase in devices such as watches, bracelets, thermostats and many other items that are able to connect to the network and therefore generate large data streams. This explosion of data justifies the birth, in the world of the term Big Data: it indicates the data produced in large quantities, with remarkable speed and in different formats, which requires processing technologies and resources that go far beyond the conventional systems management and storage of data. It is immediately clear that, 1) models of data storage based on the relational model, and 2) processing systems based on stored procedures and computations on grids are not applicable in these contexts. As regards the point 1, the RDBMS, widely used for a great variety of applications, have some problems when the amount of data grows beyond certain limits. The scalability and cost of implementation are only a part of the disadvantages: very often, in fact, when there is opposite to the management of big data, also the variability, or the lack of a fixed structure, represents a significant problem. This has given a boost to the development of the NoSQL database. The website NoSQL Databases defines NoSQL databases such as "Next Generation Databases mostly addressing some of the points: being non-relational, distributed, open source and horizontally scalable." These databases are: distributed, open source, scalable horizontally, without a predetermined pattern (key-value, column-oriented, document-based and graph-based), easily replicable, devoid of the ACID and can handle large amounts of data. These databases are integrated or integrated with processing tools based on the MapReduce paradigm proposed by Google in 2009. MapReduce with the open source Hadoop framework represent the new model for distributed processing of large amounts of data that goes to supplant techniques based on stored procedures and computational grids (step 2). The relational model taught courses in basic database design, has many limitations compared to the demands posed by new applications based on Big Data and NoSQL databases that use to store data and MapReduce to process large amounts of data.
Course Website http://pbdmng.datatoknowledge.it/
Contact me for other informations and to download
This presentation contains the introduction to NOSQL databases, it's types with examples, differentiation with 40 year old relational database management system, it's usage, why and we should use it.
“not only SQL.”
NoSQL databases are databases store data in a format other than relational tables.
NoSQL databases or non-relational databases don’t store relationship data well.
NoSQL, as many of you may already know, is basically a database used to manage huge sets of unstructured data, where in the data is not stored in tabular relations like relational databases. Most of the currently existing Relational Databases have failed in solving some of the complex modern problems like:
• Continuously changing nature of data - structured, semi-structured, unstructured and polymorphic data.
• Applications now serve millions of users in different geo-locations, in different timezones and have to be up and running all the time, with data integrity maintained
• Applications are becoming more distributed with many moving towards cloud computing.
NoSQL plays a vital role in an enterprise application which needs to access and analyze a massive set of data that is being made available on multiple virtual servers (remote based) in the cloud infrastructure and mainly when the data set is not structured. Hence, the NoSQL database is designed to overcome the Performance, Scalability, Data Modelling and Distribution limitations that are seen in the Relational Databases.
158ltd.com gives a rapid introduction to NoSQL databases: where they came from, the nature of the data models they use, and the different way you have to think about consistency.
The Information Technology have led us into an era where the production, sharing and use of information are now part of everyday life and of which we are often unaware actors almost: it is now almost inevitable not leave a digital trail of many of the actions we do every day; for example, by digital content such as photos, videos, blog posts and everything that revolves around the social networks (Facebook and Twitter in particular). Added to this is that with the "internet of things", we see an increase in devices such as watches, bracelets, thermostats and many other items that are able to connect to the network and therefore generate large data streams. This explosion of data justifies the birth, in the world of the term Big Data: it indicates the data produced in large quantities, with remarkable speed and in different formats, which requires processing technologies and resources that go far beyond the conventional systems management and storage of data. It is immediately clear that, 1) models of data storage based on the relational model, and 2) processing systems based on stored procedures and computations on grids are not applicable in these contexts. As regards the point 1, the RDBMS, widely used for a great variety of applications, have some problems when the amount of data grows beyond certain limits. The scalability and cost of implementation are only a part of the disadvantages: very often, in fact, when there is opposite to the management of big data, also the variability, or the lack of a fixed structure, represents a significant problem. This has given a boost to the development of the NoSQL database. The website NoSQL Databases defines NoSQL databases such as "Next Generation Databases mostly addressing some of the points: being non-relational, distributed, open source and horizontally scalable." These databases are: distributed, open source, scalable horizontally, without a predetermined pattern (key-value, column-oriented, document-based and graph-based), easily replicable, devoid of the ACID and can handle large amounts of data. These databases are integrated or integrated with processing tools based on the MapReduce paradigm proposed by Google in 2009. MapReduce with the open source Hadoop framework represent the new model for distributed processing of large amounts of data that goes to supplant techniques based on stored procedures and computational grids (step 2). The relational model taught courses in basic database design, has many limitations compared to the demands posed by new applications based on Big Data and NoSQL databases that use to store data and MapReduce to process large amounts of data.
Course Website http://pbdmng.datatoknowledge.it/
Contact me for other informations and to download
This presentation contains the introduction to NOSQL databases, it's types with examples, differentiation with 40 year old relational database management system, it's usage, why and we should use it.
“not only SQL.”
NoSQL databases are databases store data in a format other than relational tables.
NoSQL databases or non-relational databases don’t store relationship data well.
NoSQL, as many of you may already know, is basically a database used to manage huge sets of unstructured data, where in the data is not stored in tabular relations like relational databases. Most of the currently existing Relational Databases have failed in solving some of the complex modern problems like:
• Continuously changing nature of data - structured, semi-structured, unstructured and polymorphic data.
• Applications now serve millions of users in different geo-locations, in different timezones and have to be up and running all the time, with data integrity maintained
• Applications are becoming more distributed with many moving towards cloud computing.
NoSQL plays a vital role in an enterprise application which needs to access and analyze a massive set of data that is being made available on multiple virtual servers (remote based) in the cloud infrastructure and mainly when the data set is not structured. Hence, the NoSQL database is designed to overcome the Performance, Scalability, Data Modelling and Distribution limitations that are seen in the Relational Databases.
158ltd.com gives a rapid introduction to NoSQL databases: where they came from, the nature of the data models they use, and the different way you have to think about consistency.
Basic Introduction to Cassandra with Architecture and strategies.
with big data challenge. What is NoSQL Database.
The Big Data Challenge
The Cassandra Solution
The CAP Theorem
The Architecture of Cassandra
The Data Partition and Replication
1) Apache Cassandra in term of CAP Theorem
2) What makes Apache Cassandra "Available"?
3) How Apache Cassandra ensures data consistency?
4) Cassandra advantages and disadvantages
5) Frameworks/libraries to access Apache Cassandra + performance comparison
Use a data parallel approach to process large volumes of data (typically terabytes or petabytes) known as big data.
Focus on reliability and availability of data
The No SQL Principles and Basic Application Of Casandra ModelRishikese MR
The slides discuss various matters of the No SQL and casandra Models, the slide gives a complete picture of the both topics and its relations. Also it discuss the merits and demerits of the topics and its features and examples are also described.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Essentials of Automations: Optimizing FME Workflows with Parameters
Cassandra from the trenches: migrating Netflix (update)
1. Cassandra from the trenches:
migrating Netflix
Jason Brown
Senior Software Engineer
Netflix
@jasobrown jasedbrown@gmail.com
http://www.linkedin.com/in/jasedbrown
2. History, 2008
• In the beginning, there was the webapp
– And a database
– In one datacenter
• Then we grew, and grew, and grew
– More databases, all conjoined
– Database links, PL/SQL, Materialized views
– Multi-Master replication (MMR)
• Then it melted down
– Couldn’t ship DVDs for ~3 days
3. History, 2009
• Time to rethink everything
– Abandon our datacenter
– Ditch the monolithic webapp
– Migrate single point of failure database to …
4. History, 2010
• SimpleDB/S3
– Managed by Amazon, not us
– Got us started with NoSQL in the cloud
– Problems:
• High latency, rate limiting (throttling)
• (no) auto-sharding, no backups
5. Shiny new toy (2011)
• We switched to Cassandra
– Similar to SimpleDB, with limits removed
– Dynamo-model appealed to us
– Column-based, key-value data model seemed
sufficient for most needs
– Performance looked great (rudimentary tests)
7. About Netflix’s AB Testing
• Basic concepts
– Test – An experiment where several competing
behaviors are implemented and compared
– Cell – different experiences within a test that are
being compared against each other
– Allocation – a customer-specific assignment to a
cell within a test
8. Data Modeling - background
• AB has two sets of data
– metadata about tests
– allocations
9. AB - allocations
• Single table to hold allocations
– Currently at > 1 billion records
– Plus indices!
• One record for every test that every customer
is allocated into
• Unique constraint on customer/test
10. AB – relational model
• Typical parent-child table relationship
• Not updated frequently, so service can cache
11. Data modeling in Cassandra
• Every where I looked, the Internet told me to
understand my data use patterns
• Identify the questions that you need to
answer from the data
• Know how to query your data set and make
the persistence model match
12. Identifying the AB questions that need
to be answered
• High traffic
– get all allocations for a customer
• Low traffic
– get count of customers in test/cell
– find all customers in a test/cell
– find all customers in a test who were added within
a date range
13. Modeling allocations in Cassandra
• Read all allocations for a customer
– as fast as possible
• Find all of customers in a test/cell
– reverse index
• Get count of customers in test/cell
– count the entries in the reverse index
14. Denormalization - HOWTO
• No real world examples
– ‘Normalization is for sissies’, Pat Helland
• Denormalize allocations per customer
– Trivial with a schema-less database
16. Implementing allocations
• As allocation for a customer has a handful of
data points, they logically can be grouped
together
• Avoided blobs, json or otherwise
• Using a standard column family, with
composite columns
17. Composite columns
• Composite columns are sorted by each ‘token’
in name
• Allocation column naming convention
– <testId>:<field>
– 42:cell = 2
– 42:enabled = Y
– 47:cell = 0
– 47:enabled = Y
18. Modeling AB metadata in cassandra
• Explored several models, including json
blobs, spreading across multiple CFs, differing
degrees of denormalization
• Reverse index to identify all tests for loading
19. Implementing metadata
• One CF, one row for all test’s data
– Every data point is a column – no blobs
• Composite columns
– type:id:field
• Types = base info, cells, allocation plans
• Id = cell number, allocation plan (gu)id
• Field = type-specific
– Base info = test name, description, enabled
– Cell’s name / description
– Plan’s start/end dates, country to allocate to
20. Implementing indices
• Cassandra’s secondary indices vs. hand-built
and maintained alternate indices
• Secondary indices work great on uniform data
between rows
• But sparse column data not easy to index
21. Hand-built Indices, 1
• Reverse index
– Test/cell (key) to custIds (columns)
• Column value is timestamp
• Updating index when allocating a customer
into test (double write)
22. Hand-built indices, 2
• Counter column family
– Test/cell to count of customers in test columns
– Mutate on allocating a customer into test
• Counters are not idempotent!
• Mutates need to write to every node that
hosts that key
23. Index rebuilding
• To keep the index consistent, it needs to be
rebuilt occasionally
• Even Oracle needs to have it’s indices rebuilt
32. Astyanax latency aware
• Samples response times from Cassandra
nodes
• Favors faster responding nodes in pool
• Use with token aware connection pooling
33. Allocation mutates
• AB allocations are immutable, so we need to
prevent mutating
• Oracle - unique table constraint
• Cassandra - read before write
– data race!
34. Running cassandra
• Compactions happen
– how Cassandra is maintained
– Mutations are written to memory (Memtable)
– Flushed to disk (SSTable) on triggering threshold
– Eventually, Cassandra merges SSTables as data for
individual rows becomes scattered
35. Compactions, 2
• Latency spikes happen, especially on read-
heavy systems
– Everything can slow down
– Throttling in newer Cassandra versions helps
– Astyanax avoids this problem with latency
awareness
36. Tunings, 1
• Key and row caches
– Left unbounded can consume JVM memory
needed for normal work
– Latencies will spike as the JVM fights for free
memory
– Off-heap row cache is better but still maintains
data structures on-heap
37. Tunings, 2
• mmap() as in-memory cache
– When the Cassandra process is terminated, mmap
pages are returned to the free list
• Row cache helps at startup
38. Tunings, 3
• Sizing memtable flushes for optimizing
compactions
– Easier when writes are uniformly
distributed, timewise – easier to reason about
flush patterns
– Best to optimize flushes based on memtable
size, not time
39. Tunings, 4
• Sharding
– If a single row has disproportionately high
gets/mutates, the nodes holding it will become
hot spots
– If a row grows too large, it can’t fit into memory
40. Takeaways
• Netflix is making all of our components
distributed and fault tolerant as we grow
domestically and internationally.
• Cassandra is a core piece of our cloud
infrastructure.
• Netflix is open sourcing it’s cloud
platform, including Cassandra support