Covering how to work on migrating, backups and archive your important data to GCS:
1. Copying/Migrating Data.
2. Object Composition
3. Durable Reduced Availability Storage
The performance of mobile phone processors has been steadily increasing, causing the performance gap between server and mobile processors to narrow with mobile processors sporting superior performance per unit energy. Fueled by the slowing of Moore’s Law, the overall performance of single-chip mobile and server processors have likewise plateaued. These trends and the glut of used and partially broken smartphones which become environmental e-waste motivate creating cloud servers out of decommissioned mobile phones. This work proposes creating a compute dense server built out of used and partially broken smartphones (e.g. screen can be broken). This work evaluates the total cost of ownership (TCO) benefit of using servers based on decommissioned mobile devices and analyzes some of the architectural design trade-offs in creating such servers.
You may access the full article (free) here:
https://www.usenix.org/conference/hotcloud17/program/presentation/shahrad
MongoDB natively supports geospatial indexing and querying, and it integrates easily with open source visualization tools. In this webinar, learn high-performance techniques for querying and retrieving geospatial data, and how to create a rich visual representation of global weather data using Python, Monary, and Matplotlib.
The weather is everywhere and always. That makes for a lot of data. This talk will walk you through how you can use MongoDB to store and analyze worldwide weather data from the entire 20th century in a graphical application. We’ll discuss loading and indexing terabytes of data in a sharded cluster, and optimizing the schema design for interactive exploration. MongoDB also natively supports geospatial indexing and querying, and it integrates easily with open source visualization tools. You'll earn high-performance techniques for querying and retrieving geospatial data, and how to create a rich visual representation of global weather data using Python, Monary, and Matplotlib.
YouTube Link: https://youtu.be/mHezNgNBnuA
** Python Certification Training: https://www.edureka.co/python **
This Edureka PPT on 'Date and Time in Python' will train you to use the datetime and time modules to fetch, set and modify date and time in python.
Below are the topics covered in this PPT:
The time module
Built-in functions
Examples
The datetime module
Built-in functions
Examples
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
Covering how to work on migrating, backups and archive your important data to GCS:
1. Copying/Migrating Data.
2. Object Composition
3. Durable Reduced Availability Storage
The performance of mobile phone processors has been steadily increasing, causing the performance gap between server and mobile processors to narrow with mobile processors sporting superior performance per unit energy. Fueled by the slowing of Moore’s Law, the overall performance of single-chip mobile and server processors have likewise plateaued. These trends and the glut of used and partially broken smartphones which become environmental e-waste motivate creating cloud servers out of decommissioned mobile phones. This work proposes creating a compute dense server built out of used and partially broken smartphones (e.g. screen can be broken). This work evaluates the total cost of ownership (TCO) benefit of using servers based on decommissioned mobile devices and analyzes some of the architectural design trade-offs in creating such servers.
You may access the full article (free) here:
https://www.usenix.org/conference/hotcloud17/program/presentation/shahrad
MongoDB natively supports geospatial indexing and querying, and it integrates easily with open source visualization tools. In this webinar, learn high-performance techniques for querying and retrieving geospatial data, and how to create a rich visual representation of global weather data using Python, Monary, and Matplotlib.
The weather is everywhere and always. That makes for a lot of data. This talk will walk you through how you can use MongoDB to store and analyze worldwide weather data from the entire 20th century in a graphical application. We’ll discuss loading and indexing terabytes of data in a sharded cluster, and optimizing the schema design for interactive exploration. MongoDB also natively supports geospatial indexing and querying, and it integrates easily with open source visualization tools. You'll earn high-performance techniques for querying and retrieving geospatial data, and how to create a rich visual representation of global weather data using Python, Monary, and Matplotlib.
YouTube Link: https://youtu.be/mHezNgNBnuA
** Python Certification Training: https://www.edureka.co/python **
This Edureka PPT on 'Date and Time in Python' will train you to use the datetime and time modules to fetch, set and modify date and time in python.
Below are the topics covered in this PPT:
The time module
Built-in functions
Examples
The datetime module
Built-in functions
Examples
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
Amazon EBS provides persistent block-level storage volumes for use with Amazon EC2 instances. In this technical session, you will discover how Amazon EBS can take your application deployments on EC2 to the next level. Session attendees will learn about the Amazon EBS features and benefits, how to identify applications that are appropriate for use with Amazon EBS, best practices, and details about its performance and volume types. We discuss how to maximize Amazon EBS performance, with a special emphasis on low-latency, high-throughput applications like transactional and NoSQL databases, and big data analysis frameworks like Hadoop and Kafka. We will also dive deep and discuss Elastic Volumes, our latest EBS feature that allows you to dynamically increase capacity, tune performance, and change the type of EBS volumes on the fly. Throughout, we share tips for success.
Scaling your Kafka streaming pipeline can be a pain - but it doesn’t have to ...HostedbyConfluent
"Kafka data pipeline maintenance can be painful.
It usually comes with complicated and lengthy recovery processes, scaling difficulties, traffic ‘moodiness’, and latency issues after downtimes and outages.
It doesn’t have to be that way!
We’ll examine one of our multi-petabyte scale Kafka pipelines, and go over some of the pitfalls we’ve encountered. We’ll offer solutions that alleviate those problems, and go over comparisons between the before and after . We’ll then explain why some common sense solutions do not work well and offer an improved, scalable and resilient way of processing your stream.
We’ll cover:
• Costs of processing in stream compared to in batch
• Scaling out for bursts and reprocessing
• Making the tradeoff between wait times and costs
• Recovering from outages
• And much more…"
Google BigQuery is the future of Analytics! (Google Developer Conference)Rasel Rana
Google Developer Group (GDG) Sonargaon is a community based focused group for developers on Google and related technologies. I tried to cover a topic on Big Data & BigQuery which is the future of analytics.
How many MB in a GB? Are there 1000 or 1024 MB in a GB? Find the answer for MB to GB in this post. You can also learn more about bytes, KB, MB, GB, TB, etc.
AWS re:Invent 2016: Case Study: Librato's Experience Running Cassandra Using ...Amazon Web Services
At Librato, a Solarwinds company, we run hundreds of Cassandra instances across multiple rings and use it as our primary data store. In the past year, we embarked on a process to upgrade our fleet of Cassandra Amazon EC2 instances from instance store to instances using Amazon EBS and attached elastic network interfaces (ENIs). We find running Cassandra on EBS gives us the flexibility to choose the best instances for the best performance of our workload while saving us significant costs on infrastructure. In this session, we discuss how Librato operates Cassandra on EBS. Topics include how we chose the right instance for our workload, use detached EBS volumes and ENI mobility to reduce MTTR, use mixed EBS storage types for the best cost/performance tradeoff, debug performance issues, and continuously monitor Cassandra to get the most from AWS. We also look at performance tradeoffs made in the implementation of storage engines of large data systems like Cassandra.
Galaxy Big Data with MariaDB 10 by Bernard Garros, Sandrine Chirokoff and Stéphane Varoqui.
Presented 26.6.2014 at the MariaDB Roadshow in Paris, France.
Building an Amazon Datawarehouse and Using Business Intelligence Analytics ToolsAmazon Web Services
Using AWS has never been easier or more affordable to solve business problems and uncover new opportunities using data. Now, businesses of all sizes and across all industries can take advantage of big data technologies and easily collect, store, process, analyze, and share their data. Gain a thorough understanding of what AWS offers across the big data lifecycle and learn architectural best practices for applying these technologies to your projects. We will also deep dive into how to use AWS services such as Kinesis, DynamoDB, Redshift, and Quicksight to optimize logging, build real-time applications, and analyze and visualize data at any scale.
Amazon EBS provides persistent block-level storage volumes for use with Amazon EC2 instances. In this technical session, you will discover how Amazon EBS can take your application deployments on EC2 to the next level. Session attendees will learn about the Amazon EBS features and benefits, how to identify applications that are appropriate for use with Amazon EBS, best practices, and details about its performance and volume types. We discuss how to maximize Amazon EBS performance, with a special emphasis on low-latency, high-throughput applications like transactional and NoSQL databases, and big data analysis frameworks like Hadoop and Kafka. We will also dive deep and discuss Elastic Volumes, our latest EBS feature that allows you to dynamically increase capacity, tune performance, and change the type of EBS volumes on the fly. Throughout, we share tips for success.
Scaling your Kafka streaming pipeline can be a pain - but it doesn’t have to ...HostedbyConfluent
"Kafka data pipeline maintenance can be painful.
It usually comes with complicated and lengthy recovery processes, scaling difficulties, traffic ‘moodiness’, and latency issues after downtimes and outages.
It doesn’t have to be that way!
We’ll examine one of our multi-petabyte scale Kafka pipelines, and go over some of the pitfalls we’ve encountered. We’ll offer solutions that alleviate those problems, and go over comparisons between the before and after . We’ll then explain why some common sense solutions do not work well and offer an improved, scalable and resilient way of processing your stream.
We’ll cover:
• Costs of processing in stream compared to in batch
• Scaling out for bursts and reprocessing
• Making the tradeoff between wait times and costs
• Recovering from outages
• And much more…"
Google BigQuery is the future of Analytics! (Google Developer Conference)Rasel Rana
Google Developer Group (GDG) Sonargaon is a community based focused group for developers on Google and related technologies. I tried to cover a topic on Big Data & BigQuery which is the future of analytics.
How many MB in a GB? Are there 1000 or 1024 MB in a GB? Find the answer for MB to GB in this post. You can also learn more about bytes, KB, MB, GB, TB, etc.
AWS re:Invent 2016: Case Study: Librato's Experience Running Cassandra Using ...Amazon Web Services
At Librato, a Solarwinds company, we run hundreds of Cassandra instances across multiple rings and use it as our primary data store. In the past year, we embarked on a process to upgrade our fleet of Cassandra Amazon EC2 instances from instance store to instances using Amazon EBS and attached elastic network interfaces (ENIs). We find running Cassandra on EBS gives us the flexibility to choose the best instances for the best performance of our workload while saving us significant costs on infrastructure. In this session, we discuss how Librato operates Cassandra on EBS. Topics include how we chose the right instance for our workload, use detached EBS volumes and ENI mobility to reduce MTTR, use mixed EBS storage types for the best cost/performance tradeoff, debug performance issues, and continuously monitor Cassandra to get the most from AWS. We also look at performance tradeoffs made in the implementation of storage engines of large data systems like Cassandra.
Galaxy Big Data with MariaDB 10 by Bernard Garros, Sandrine Chirokoff and Stéphane Varoqui.
Presented 26.6.2014 at the MariaDB Roadshow in Paris, France.
Building an Amazon Datawarehouse and Using Business Intelligence Analytics ToolsAmazon Web Services
Using AWS has never been easier or more affordable to solve business problems and uncover new opportunities using data. Now, businesses of all sizes and across all industries can take advantage of big data technologies and easily collect, store, process, analyze, and share their data. Gain a thorough understanding of what AWS offers across the big data lifecycle and learn architectural best practices for applying these technologies to your projects. We will also deep dive into how to use AWS services such as Kinesis, DynamoDB, Redshift, and Quicksight to optimize logging, build real-time applications, and analyze and visualize data at any scale.
Implementation of Dense Storage Utilizing HDDs with SSDs and PCIe Flash Acc...Red_Hat_Storage
At Red Hat Storage Day New York on 1/19/16, Red Hat partner Seagate presented on how to implement dense storage using HDDs with SSDs and PCIe flash accelerator cards.
5. Gmail’s quota counter var CP = [ [ 1167638400000, 2800 ], [ 1175414400000, 2835 ], [ 1207033200000, 2980 ], [ 1238569200000, 3125 ], [ 1270105200000, 3270 ], [ 1301641200000, 3415 ], [ 1333263600000, 3560 ] ]; This is a code fragment from the “Welcome to Gmail” page: Dates in serial form Storage quota in Mb
6. Gmail’s quota counter var CP = [ [ 01/01/2007 08:00 -> 2800 Mb ], [ 01/04/2007 08:00 -> 2835 Mb ], [ 01/04/2008 07:00 -> 2980 Mb ], [ 01/04/2009 07:00 -> 3125 Mb ], [ 01/04/2010 07:00 -> 3270 Mb ], [ 01/04/2011 07:00 -> 3415 Mb ], [ 01/04/2012 07:00 -> 3560 Mb ] ]; Gmail calculates the current quota using a linear interpolation between the date “points”. The quota counter table in human readable form: