This presentation gives an overview of the Apache Ignite project. It explains Ignite in relation to its architecture, scaleability, caching, datagrid and machine learning abilities.
Links for further information and connecting
http://www.amazon.com/Michael-Frampton/e/B00NIQDOOM/
https://nz.linkedin.com/pub/mike-frampton/20/630/385
https://open-source-systems.blogspot.com/
Improving Apache Spark™ In-Memory Computing with Apache Ignite™Tom Diederich
GridGain Systems Lead Architect Valentin (Val) Kulichenko presented the following talk at the May 17 Bay Area In-Memory Computing Meetup: Improving Apache Spark™ In-Memory Computing with Apache Ignite™
Val explained how Apache Ignite™ simplifies development and improves performance for Apache Spark™. He'll demonstrate how Apache Spark and Ignite are integrated, and how they are used to together for analytics, stream processing and machine learning.
The following was covered:
* How Apache Ignite’s native RDD and new native DataFrame APIs work
* How to use Ignite as an in-memory database and massively parallel processing (MPP) style collocated processing for preparing and managing data for Spark
* How to leverage Ignite to easily share state across Spark jobs using mutable RDDs and DataFrames
* How to leverage Ignite distributed SQL and advanced indexing in memory to improve SQL performance
Apache Cassandra Lunch #71: Creating a User Profile Using DataStax Astra and ...Anant Corporation
In Cassandra Lunch #71, we will discuss how DataStax Astra can be used as a back-end for a React client. We will demo a small application with a user profile.
Accompanying Blog: https://blog.anant.us/creating-a-user-profile-using-datastax-astra/
Accompanying YouTube: https://youtu.be/7n4PsYhGIfM
Sign Up For Our Newsletter: http://eepurl.com/grdMkn
Join Cassandra Lunch Weekly at 12 PM EST Every Wednesday: https://www.meetup.com/Cassandra-DataStax-DC/events/
Cassandra.Link:
https://cassandra.link/
Follow Us and Reach Us At:
Anant:
https://www.anant.us/
Awesome Cassandra:
https://github.com/Anant/awesome-cassandra
Cassandra.Lunch:
https://github.com/Anant/Cassandra.Lunch
Email:
solutions@anant.us
LinkedIn:
https://www.linkedin.com/company/anant/
Twitter:
https://twitter.com/anantcorp
Eventbrite:
https://www.eventbrite.com/o/anant-1072927283
Facebook:
https://www.facebook.com/AnantCorp/
Join The Anant Team:
https://www.careers.anant.us
Apache Cassandra Lunch #78: Deploy Cassandra using DSE Operator to KubernetesAnant Corporation
In Cassandra Lunch #78, we will deploy Cassandra using DSE Operator to Kubernetes
Accompanying Blog: https://blog.anant.us/apache-cassandra-lunch-78-cass-operator/
Accompanying YouTube: https://youtu.be/Cfvks4WBtKk
Sign Up For Our Newsletter: http://eepurl.com/grdMkn
Join Cassandra Lunch Weekly at 12 PM EST Every Wednesday: https://www.meetup.com/Cassandra-DataStax-DC/events/
Cassandra.Link:
https://cassandra.link/
Follow Us and Reach Us At:
Anant:
https://www.anant.us/
Awesome Cassandra:
https://github.com/Anant/awesome-cassandra
Cassandra.Lunch:
https://github.com/Anant/Cassandra.Lunch
Email:
solutions@anant.us
LinkedIn:
https://www.linkedin.com/company/anant/
Twitter:
https://twitter.com/anantcorp
Eventbrite:
https://www.eventbrite.com/o/anant-1072927283
Facebook:
https://www.facebook.com/AnantCorp/
Join The Anant Team:
https://www.careers.anant.us
Apache Spark is a powerful free handling engine built around speed, ease of use, and complex statistics. It was initially designed at UC Berkeley in 2009.
Cronicle is a multi-server task scheduler that can run jobs on multiple servers. Storreduce is a cloud storage deduplication solution that can reduce storage usage by up to 99% when backing up data to cloud object storage like S3. The proposed backup solution uses Cronicle to schedule backups, Storreduce for data deduplication, and named pipes for high-speed data transfer between servers and to S3. Differential backups are performed to reduce backup sizes and bandwidth usage.
ActiveSTAK provides customizable cloud environments tailored for businesses through its intelligent storage, multiple processing architectures, and automation features. The company offers tiered storage arrays, SSD and HDD hybrid storage, up to 320K IOPS random read and 200K random write, auto scaling groups for flexible resource distribution, and self-healing mechanisms to ensure high uptime. ActiveSTAK's clouds also feature private network integration, high availability data centers, multi-architecture processing, and compliance with various security and privacy protocols.
The document discusses Cloudera's enterprise data cloud platform. It notes that data management is spread across multiple cloud and on-premises environments. The platform aims to provide an integrated data lifecycle that is easier to use, manage and secure across various business use cases. Key components include environments, data lakes, data hub clusters, analytic experiences, and a central control plane for management. The platform offers both traditional and container-based consumption options to provide flexibility across cloud, private cloud and on-premises deployment.
This presentation gives an overview of the Apache Ignite project. It explains Ignite in relation to its architecture, scaleability, caching, datagrid and machine learning abilities.
Links for further information and connecting
http://www.amazon.com/Michael-Frampton/e/B00NIQDOOM/
https://nz.linkedin.com/pub/mike-frampton/20/630/385
https://open-source-systems.blogspot.com/
Improving Apache Spark™ In-Memory Computing with Apache Ignite™Tom Diederich
GridGain Systems Lead Architect Valentin (Val) Kulichenko presented the following talk at the May 17 Bay Area In-Memory Computing Meetup: Improving Apache Spark™ In-Memory Computing with Apache Ignite™
Val explained how Apache Ignite™ simplifies development and improves performance for Apache Spark™. He'll demonstrate how Apache Spark and Ignite are integrated, and how they are used to together for analytics, stream processing and machine learning.
The following was covered:
* How Apache Ignite’s native RDD and new native DataFrame APIs work
* How to use Ignite as an in-memory database and massively parallel processing (MPP) style collocated processing for preparing and managing data for Spark
* How to leverage Ignite to easily share state across Spark jobs using mutable RDDs and DataFrames
* How to leverage Ignite distributed SQL and advanced indexing in memory to improve SQL performance
Apache Cassandra Lunch #71: Creating a User Profile Using DataStax Astra and ...Anant Corporation
In Cassandra Lunch #71, we will discuss how DataStax Astra can be used as a back-end for a React client. We will demo a small application with a user profile.
Accompanying Blog: https://blog.anant.us/creating-a-user-profile-using-datastax-astra/
Accompanying YouTube: https://youtu.be/7n4PsYhGIfM
Sign Up For Our Newsletter: http://eepurl.com/grdMkn
Join Cassandra Lunch Weekly at 12 PM EST Every Wednesday: https://www.meetup.com/Cassandra-DataStax-DC/events/
Cassandra.Link:
https://cassandra.link/
Follow Us and Reach Us At:
Anant:
https://www.anant.us/
Awesome Cassandra:
https://github.com/Anant/awesome-cassandra
Cassandra.Lunch:
https://github.com/Anant/Cassandra.Lunch
Email:
solutions@anant.us
LinkedIn:
https://www.linkedin.com/company/anant/
Twitter:
https://twitter.com/anantcorp
Eventbrite:
https://www.eventbrite.com/o/anant-1072927283
Facebook:
https://www.facebook.com/AnantCorp/
Join The Anant Team:
https://www.careers.anant.us
Apache Cassandra Lunch #78: Deploy Cassandra using DSE Operator to KubernetesAnant Corporation
In Cassandra Lunch #78, we will deploy Cassandra using DSE Operator to Kubernetes
Accompanying Blog: https://blog.anant.us/apache-cassandra-lunch-78-cass-operator/
Accompanying YouTube: https://youtu.be/Cfvks4WBtKk
Sign Up For Our Newsletter: http://eepurl.com/grdMkn
Join Cassandra Lunch Weekly at 12 PM EST Every Wednesday: https://www.meetup.com/Cassandra-DataStax-DC/events/
Cassandra.Link:
https://cassandra.link/
Follow Us and Reach Us At:
Anant:
https://www.anant.us/
Awesome Cassandra:
https://github.com/Anant/awesome-cassandra
Cassandra.Lunch:
https://github.com/Anant/Cassandra.Lunch
Email:
solutions@anant.us
LinkedIn:
https://www.linkedin.com/company/anant/
Twitter:
https://twitter.com/anantcorp
Eventbrite:
https://www.eventbrite.com/o/anant-1072927283
Facebook:
https://www.facebook.com/AnantCorp/
Join The Anant Team:
https://www.careers.anant.us
Apache Spark is a powerful free handling engine built around speed, ease of use, and complex statistics. It was initially designed at UC Berkeley in 2009.
Cronicle is a multi-server task scheduler that can run jobs on multiple servers. Storreduce is a cloud storage deduplication solution that can reduce storage usage by up to 99% when backing up data to cloud object storage like S3. The proposed backup solution uses Cronicle to schedule backups, Storreduce for data deduplication, and named pipes for high-speed data transfer between servers and to S3. Differential backups are performed to reduce backup sizes and bandwidth usage.
ActiveSTAK provides customizable cloud environments tailored for businesses through its intelligent storage, multiple processing architectures, and automation features. The company offers tiered storage arrays, SSD and HDD hybrid storage, up to 320K IOPS random read and 200K random write, auto scaling groups for flexible resource distribution, and self-healing mechanisms to ensure high uptime. ActiveSTAK's clouds also feature private network integration, high availability data centers, multi-architecture processing, and compliance with various security and privacy protocols.
The document discusses Cloudera's enterprise data cloud platform. It notes that data management is spread across multiple cloud and on-premises environments. The platform aims to provide an integrated data lifecycle that is easier to use, manage and secure across various business use cases. Key components include environments, data lakes, data hub clusters, analytic experiences, and a central control plane for management. The platform offers both traditional and container-based consumption options to provide flexibility across cloud, private cloud and on-premises deployment.
Disney+ Hotstar: Scaling NoSQL for Millions of Video On-Demand UsersScyllaDB
Disney+ Hotstar is the fastest growing branch of Disney+. Join Disney+ Hotstar Architect Vamsi Subhash and senior data engineer Balakrishnan Kaliyamoorthy to learn…
How Disney+ Hotstar architected their systems to handle massive data loads
Why they chose to replace both Redis and Elasticsearch
Their requirements for massively scalable data infrastructure and evolving data models
How they migrated their data to Scylla Cloud, ScyllaDB’s fully managed NoSQL database-as-a-service, without suffering downtime
This document introduces the HyperStore Smart Storage Platform, a software-defined object storage system that provides scalable, always-on, and durable storage across hybrid cloud environments. Some key features include using the S3 protocol, replication for high availability, erasure coding for data protection, and smart policies to control data placement, access, and tiering. The system offers multi-tenancy, quality of service controls, security, analytics capabilities, and APIs to programmatically manage storage and integrate with applications.
Apache Iceberg Presentation for the St. Louis Big Data IDEAAdam Doyle
Presentation on Apache Iceberg for the February 2021 St. Louis Big Data IDEA. Apache Iceberg is an alternative database platform that works with Hive and Spark.
Zabbix was experiencing performance issues due to large history tables in the database. To address this, the architecture was changed to store history data in Elasticsearch instead of database tables. This improved scalability and performance. The basic item and event data remained in the MariaDB database cluster. Zabbix proxies were also used to distribute load across multiple network segments. With this new architecture, history data is indexed in Elasticsearch without database tables, improving query speed and reducing database size.
Vitalii Bondarenko "Machine Learning on Fast Data"DataConf
This document discusses machine learning on fast data. It presents an agenda covering ML on production systems, TensorFlow, Kafka, Docker and Kubernetes. It then describes the machine learning process and shows how an enterprise analytics platform can integrate data sources, a machine learning cluster using Kafka, and data destinations. Details are provided on using TensorFlow for linear regression and neural networks. Apache Kafka is explained as a distributed streaming platform using topics, brokers, and consumer groups. The Confluent platform, KStream and KTable APIs are also summarized. Docker and Kubernetes are mentioned for containerization.
Apache Ignite: In-Memory Hammer for Your Data Science ToolkitDenis Magda
Machine learning is a method of data analysis that automates the building of analytical models. By using algorithms that iteratively learn from data, computers are able to find hidden insights without the help of explicit programming. These insights bring tremendous benefits into many different domains. For business users, in particular, these insights help organizations improve customer experience, become more competitive, and respond much faster to opportunities or threats.
The availability of very powerful in-memory computing platforms, such as Apache Ignite, means that more organizations can benefit from machine learning today. In this presentation, we will discuss how the Compute Grid, Data Grid, and Machine Learning Grid components of Apache Ignite work together to enable your business to start reaping the benefits of machine learning. Through examples, attendees will learn how Apache Ignite can be used for data analysis and be the in-memory hammer in your machine learning toolkit.
Big data requires service that can orchestrate and operationalize processes to refine the enormous stores of raw data into actionable business insights. Azure Data Factory is a managed cloud service that's built for these complex hybrid extract-transform-load (ETL), extract-load-transform (ELT), and data integration projects.
The document discusses the benefits and challenges of running big data workloads on cloud native platforms. Some key points discussed include:
- Big data workloads are migrating to the cloud to take advantage of scalability, flexibility and cost effectiveness compared to on-premises solutions.
- Enterprise cloud platforms need to provide centralized management and monitoring of multiple clusters, secure data access, and replication capabilities.
- Running big data on cloud introduces challenges around storage, networking, compute resources, and security that systems need to address, such as consistency issues with object storage, network throughput reductions, and hardware variations across cloud vendors.
- The open source community is helping users address these challenges to build cloud native data architectures
Azure Data Lakes allow for storing and analyzing large amounts of data from multiple sources using frameworks like HDInsight, Spark, and machine learning. Data is stored in Azure Data Lakes Store using WebHDFS in 2GB chunks called extents that are replicated three times for availability and reliability. Azure Data Lake Storage Gen 2 adds additional features from Azure Blob storage like fault tolerance, high availability, and lower costs. Data lakes help companies gain a unified view of data to improve analysis and act on business opportunities faster.
The document discusses modern data architectures. It presents conceptual models for data ingestion, storage, processing, and insights/actions. It compares traditional vs modern architectures. The modern architecture uses a data lake for storage and allows for on-demand analysis. It provides an example of how this could be implemented on Microsoft Azure using services like Azure Data Lake Storage, Azure Data Bricks, and Azure Data Warehouse. It also outlines common data management functions such as data governance, architecture, development, operations, and security.
Keeping Up with the ELK Stack: Elasticsearch, Kibana, Beats, and LogstashAmazon Web Services
Version 7 of the Elastic Stack adds powerful new features to the popular open source platform for search, logging, and analytics. Come hear directly from Elastic engineers and architecture team members on powerful new additions like GIS functionality and frozen-tier search. Plus, hear about the full range of orchestration options for getting the most out of your deployments, however and wherever you choose to run them. This session is sponsored by Elastic.
Scylla Summit 2018: Adventures in AdTech: Processing 50 Billion User Profiles...ScyllaDB
AdTech requires high speed at massive scale. Sizmek serves millions of requests every second. Requests need to be processed in tens of milliseconds, while involving 10 simultaneous lookups into a database that contains tens of billions of profiles. In this presentation, you will discover how Scylla enables Sizmek’s real-time bidders to query a gigantic user profile store quickly and reliably with only a few nodes. We’ll discuss data modeling, server and driver configuration, techniques to minimize disk access, as well as considerations for leveraging Spark while migrating from HBase.
Progress® DataDirect ® Spark SQL ODBC and JDBC drivers deliver the fastest, high-performance connectivity so your existing BI and analytics applications can access Big Data in Apache Spark.
Scylla Summit 2022: Multi-cloud State for k8s: Anthos and ScyllaDBScyllaDB
One cloud is hard enough, am I right? Now everyone expects that you can deploy containerized applications "everywhere" and things will "just work." Our customer sure did! Join Miles Ward, CTO, and Jenn Viau, Staff Solutions Architect, at SADA on a detailed, data-filled exploration of the complexities and constraints of modern multi-cloud and hybrid scenarios, rooted in the pursuit of almighty uptime and SLO adherence. They'll show what worked, and what didn't, in a detailed architectural review, as well as demonstrate (and perf test live!) components of the final production system.
To watch all of the recordings hosted during Scylla Summit 2022 visit our website here: https://www.scylladb.com/summit.
As cloud computing continues to gather speed, organizations with years’ worth of data stored on legacy on-premise technologies are facing issues with scale, speed, and complexity. Your customers and business partners are likely eager to get data from you, especially if you can make the process easy and secure.
Challenges with performance are not uncommon and ongoing interventions are required just to “keep the lights on”.
Discover how Snowflake empowers you to meet your analytics needs by unlocking the potential of your data.
Agenda of Webinar :
~Understand Snowflake and its Architecture
~Quickly load data into Snowflake
~Leverage the latest in Snowflake’s unlimited performance and scale to make the data ready for analytics
~Deliver secure and governed access to all data – no more silos
Reporting from the Trenches: Intuit & CassandraDataStax
Rekha Joshi presents on how Intuit uses the Cassandra database to enable personalized A/B testing and improve customer experiences. Intuit handles large volumes of customer data and required a database with high security, scalability, availability and tunable performance. Cassandra met these requirements and became Intuit's standard NoSQL database. Rekha discusses how Intuit leverages Cassandra's capabilities and provides best practices for effective Cassandra usage, configuration, and performance tuning.
This document provides an overview of Azure SQL Data Warehouse. It discusses what Azure SQL Data Warehouse is, how it is provisioned and scaled, best practices for designing tables in Azure SQL DW including distribution keys and data types, and methods for loading and querying data including PolyBase and labeling queries for monitoring. The presentation also covers tuning aspects like statistics, indexing, and resource classes.
Simplicity, accuracy, speed are three things everyone wants from their data architecture. A content delivery network based in LA, was looking to achieve these goals and developed a framework that handled batch and stream processing with open source software. The objective was to manage the real-time aggregation of over 32 TB of daily web server log data. The problem? Everything. Listen as Dennis Duckworth explains how VoltDB reduced the number of environments, used 1/10th the CPU cycles, and achieved 100% billing accuracy on 32 TB of daily web server data.
This document discusses Azure big data capabilities including the 5 V's of big data: volume, velocity, variety, veracity, and value. It notes that 60% of big data projects fail to move beyond pilot according to Gartner. It then provides details on Azure persistence choices for storing big data including storage, Data Lake, HDInsight, DocumentDB, SQL databases, and Hadoop options. It also discusses load and data cleaning choices on Azure like Stream Analytics, SQL Server, and Azure Machine Learning. Finally, it presents 5 architectural patterns for using Azure big data capabilities.
Tegile Intelligent Flash Storage Arrays provide high performance storage with data reduction up to 10:1 through inline deduplication and compression. They use an IntelliFlash architecture that separates metadata from data storage for improved performance. Models range from hybrid to all-flash configurations with varying storage capacities and performance levels.
We will examine most of the features that this “Swiss knife” software provides. It is an in-memory fabric that fits between the database and the application layer. Apache Ignite is powered by the H2 engine. They have used it to create an in-memory distributed ACID, fully ANSI-99 complaint, Highly Available (HA) and scalable database. They have used a non-consensus (https://en.wikipedia.org/wiki/Rendezvous_hashing) clustering algorithm to be even more scalable compared to other NoSql solutions. This tool respects the relational data model that we have used for so many years and eliminates traditional problems like the “expensive joins” since it uses the RAM as the primary storage medium. We will see what this tool can do in action through hands-on examples.
Disney+ Hotstar: Scaling NoSQL for Millions of Video On-Demand UsersScyllaDB
Disney+ Hotstar is the fastest growing branch of Disney+. Join Disney+ Hotstar Architect Vamsi Subhash and senior data engineer Balakrishnan Kaliyamoorthy to learn…
How Disney+ Hotstar architected their systems to handle massive data loads
Why they chose to replace both Redis and Elasticsearch
Their requirements for massively scalable data infrastructure and evolving data models
How they migrated their data to Scylla Cloud, ScyllaDB’s fully managed NoSQL database-as-a-service, without suffering downtime
This document introduces the HyperStore Smart Storage Platform, a software-defined object storage system that provides scalable, always-on, and durable storage across hybrid cloud environments. Some key features include using the S3 protocol, replication for high availability, erasure coding for data protection, and smart policies to control data placement, access, and tiering. The system offers multi-tenancy, quality of service controls, security, analytics capabilities, and APIs to programmatically manage storage and integrate with applications.
Apache Iceberg Presentation for the St. Louis Big Data IDEAAdam Doyle
Presentation on Apache Iceberg for the February 2021 St. Louis Big Data IDEA. Apache Iceberg is an alternative database platform that works with Hive and Spark.
Zabbix was experiencing performance issues due to large history tables in the database. To address this, the architecture was changed to store history data in Elasticsearch instead of database tables. This improved scalability and performance. The basic item and event data remained in the MariaDB database cluster. Zabbix proxies were also used to distribute load across multiple network segments. With this new architecture, history data is indexed in Elasticsearch without database tables, improving query speed and reducing database size.
Vitalii Bondarenko "Machine Learning on Fast Data"DataConf
This document discusses machine learning on fast data. It presents an agenda covering ML on production systems, TensorFlow, Kafka, Docker and Kubernetes. It then describes the machine learning process and shows how an enterprise analytics platform can integrate data sources, a machine learning cluster using Kafka, and data destinations. Details are provided on using TensorFlow for linear regression and neural networks. Apache Kafka is explained as a distributed streaming platform using topics, brokers, and consumer groups. The Confluent platform, KStream and KTable APIs are also summarized. Docker and Kubernetes are mentioned for containerization.
Apache Ignite: In-Memory Hammer for Your Data Science ToolkitDenis Magda
Machine learning is a method of data analysis that automates the building of analytical models. By using algorithms that iteratively learn from data, computers are able to find hidden insights without the help of explicit programming. These insights bring tremendous benefits into many different domains. For business users, in particular, these insights help organizations improve customer experience, become more competitive, and respond much faster to opportunities or threats.
The availability of very powerful in-memory computing platforms, such as Apache Ignite, means that more organizations can benefit from machine learning today. In this presentation, we will discuss how the Compute Grid, Data Grid, and Machine Learning Grid components of Apache Ignite work together to enable your business to start reaping the benefits of machine learning. Through examples, attendees will learn how Apache Ignite can be used for data analysis and be the in-memory hammer in your machine learning toolkit.
Big data requires service that can orchestrate and operationalize processes to refine the enormous stores of raw data into actionable business insights. Azure Data Factory is a managed cloud service that's built for these complex hybrid extract-transform-load (ETL), extract-load-transform (ELT), and data integration projects.
The document discusses the benefits and challenges of running big data workloads on cloud native platforms. Some key points discussed include:
- Big data workloads are migrating to the cloud to take advantage of scalability, flexibility and cost effectiveness compared to on-premises solutions.
- Enterprise cloud platforms need to provide centralized management and monitoring of multiple clusters, secure data access, and replication capabilities.
- Running big data on cloud introduces challenges around storage, networking, compute resources, and security that systems need to address, such as consistency issues with object storage, network throughput reductions, and hardware variations across cloud vendors.
- The open source community is helping users address these challenges to build cloud native data architectures
Azure Data Lakes allow for storing and analyzing large amounts of data from multiple sources using frameworks like HDInsight, Spark, and machine learning. Data is stored in Azure Data Lakes Store using WebHDFS in 2GB chunks called extents that are replicated three times for availability and reliability. Azure Data Lake Storage Gen 2 adds additional features from Azure Blob storage like fault tolerance, high availability, and lower costs. Data lakes help companies gain a unified view of data to improve analysis and act on business opportunities faster.
The document discusses modern data architectures. It presents conceptual models for data ingestion, storage, processing, and insights/actions. It compares traditional vs modern architectures. The modern architecture uses a data lake for storage and allows for on-demand analysis. It provides an example of how this could be implemented on Microsoft Azure using services like Azure Data Lake Storage, Azure Data Bricks, and Azure Data Warehouse. It also outlines common data management functions such as data governance, architecture, development, operations, and security.
Keeping Up with the ELK Stack: Elasticsearch, Kibana, Beats, and LogstashAmazon Web Services
Version 7 of the Elastic Stack adds powerful new features to the popular open source platform for search, logging, and analytics. Come hear directly from Elastic engineers and architecture team members on powerful new additions like GIS functionality and frozen-tier search. Plus, hear about the full range of orchestration options for getting the most out of your deployments, however and wherever you choose to run them. This session is sponsored by Elastic.
Scylla Summit 2018: Adventures in AdTech: Processing 50 Billion User Profiles...ScyllaDB
AdTech requires high speed at massive scale. Sizmek serves millions of requests every second. Requests need to be processed in tens of milliseconds, while involving 10 simultaneous lookups into a database that contains tens of billions of profiles. In this presentation, you will discover how Scylla enables Sizmek’s real-time bidders to query a gigantic user profile store quickly and reliably with only a few nodes. We’ll discuss data modeling, server and driver configuration, techniques to minimize disk access, as well as considerations for leveraging Spark while migrating from HBase.
Progress® DataDirect ® Spark SQL ODBC and JDBC drivers deliver the fastest, high-performance connectivity so your existing BI and analytics applications can access Big Data in Apache Spark.
Scylla Summit 2022: Multi-cloud State for k8s: Anthos and ScyllaDBScyllaDB
One cloud is hard enough, am I right? Now everyone expects that you can deploy containerized applications "everywhere" and things will "just work." Our customer sure did! Join Miles Ward, CTO, and Jenn Viau, Staff Solutions Architect, at SADA on a detailed, data-filled exploration of the complexities and constraints of modern multi-cloud and hybrid scenarios, rooted in the pursuit of almighty uptime and SLO adherence. They'll show what worked, and what didn't, in a detailed architectural review, as well as demonstrate (and perf test live!) components of the final production system.
To watch all of the recordings hosted during Scylla Summit 2022 visit our website here: https://www.scylladb.com/summit.
As cloud computing continues to gather speed, organizations with years’ worth of data stored on legacy on-premise technologies are facing issues with scale, speed, and complexity. Your customers and business partners are likely eager to get data from you, especially if you can make the process easy and secure.
Challenges with performance are not uncommon and ongoing interventions are required just to “keep the lights on”.
Discover how Snowflake empowers you to meet your analytics needs by unlocking the potential of your data.
Agenda of Webinar :
~Understand Snowflake and its Architecture
~Quickly load data into Snowflake
~Leverage the latest in Snowflake’s unlimited performance and scale to make the data ready for analytics
~Deliver secure and governed access to all data – no more silos
Reporting from the Trenches: Intuit & CassandraDataStax
Rekha Joshi presents on how Intuit uses the Cassandra database to enable personalized A/B testing and improve customer experiences. Intuit handles large volumes of customer data and required a database with high security, scalability, availability and tunable performance. Cassandra met these requirements and became Intuit's standard NoSQL database. Rekha discusses how Intuit leverages Cassandra's capabilities and provides best practices for effective Cassandra usage, configuration, and performance tuning.
This document provides an overview of Azure SQL Data Warehouse. It discusses what Azure SQL Data Warehouse is, how it is provisioned and scaled, best practices for designing tables in Azure SQL DW including distribution keys and data types, and methods for loading and querying data including PolyBase and labeling queries for monitoring. The presentation also covers tuning aspects like statistics, indexing, and resource classes.
Simplicity, accuracy, speed are three things everyone wants from their data architecture. A content delivery network based in LA, was looking to achieve these goals and developed a framework that handled batch and stream processing with open source software. The objective was to manage the real-time aggregation of over 32 TB of daily web server log data. The problem? Everything. Listen as Dennis Duckworth explains how VoltDB reduced the number of environments, used 1/10th the CPU cycles, and achieved 100% billing accuracy on 32 TB of daily web server data.
This document discusses Azure big data capabilities including the 5 V's of big data: volume, velocity, variety, veracity, and value. It notes that 60% of big data projects fail to move beyond pilot according to Gartner. It then provides details on Azure persistence choices for storing big data including storage, Data Lake, HDInsight, DocumentDB, SQL databases, and Hadoop options. It also discusses load and data cleaning choices on Azure like Stream Analytics, SQL Server, and Azure Machine Learning. Finally, it presents 5 architectural patterns for using Azure big data capabilities.
Tegile Intelligent Flash Storage Arrays provide high performance storage with data reduction up to 10:1 through inline deduplication and compression. They use an IntelliFlash architecture that separates metadata from data storage for improved performance. Models range from hybrid to all-flash configurations with varying storage capacities and performance levels.
We will examine most of the features that this “Swiss knife” software provides. It is an in-memory fabric that fits between the database and the application layer. Apache Ignite is powered by the H2 engine. They have used it to create an in-memory distributed ACID, fully ANSI-99 complaint, Highly Available (HA) and scalable database. They have used a non-consensus (https://en.wikipedia.org/wiki/Rendezvous_hashing) clustering algorithm to be even more scalable compared to other NoSql solutions. This tool respects the relational data model that we have used for so many years and eliminates traditional problems like the “expensive joins” since it uses the RAM as the primary storage medium. We will see what this tool can do in action through hands-on examples.
The document provides an overview of SAP HANA, an in-memory platform for real-time processing of transactions and analytics. It can process any type of data from any source in real time using SQL. Key features include processing both OLTP and OLAP simultaneously with no latency, supporting advanced text and geospatial analytics within SQL queries, and integrating streaming data.
The document discusses testing done by IBM to evaluate the performance improvements provided by the IBM MAX5 memory expansion technology. The testing showed that by adding 512GB of memory via a MAX5 unit, increasing total memory to 1TB, the following benefits were achieved:
- Response time for business intelligence reports was 1.5-2.8 times faster.
- The cost of producing business intelligence reports could be decreased by 31%-64% over 3 years.
- The throughput of web-facing applications was 2.4-4.9 times greater.
- Read/write response time was decreased by 60%-80%.
Accelerate Your Apache Spark with Intel Optane DC Persistent MemoryDatabricks
The capacity of data grows rapidly in big data area, more and more memory are consumed either in the computation or holding the intermediate data for analytic jobs. For those memory intensive workloads, end-point users have to scale out the computation cluster or extend memory with storage like HDD or SSD to meet the requirement of computing tasks. For scaling out the cluster, the extra cost from cluster management, operation and maintenance will increase the total cost if the extra CPU resources are not fully utilized. To address the shortcoming above, Intel Optane DC persistent memory (Optane DCPM) breaks the traditional memory/storage hierarchy and scale up the computing server with higher capacity persistent memory. Also it brings higher bandwidth & lower latency than storage like SSD or HDD. And Apache Spark is widely used in the analytics like SQL and Machine Learning on the cloud environment. For cloud environment, low performance of remote data access is typical a stop gap for users especially for some I/O intensive queries. For the ML workload, it's an iterative model which I/O bandwidth is the key to the end-2-end performance. In this talk, we will introduce how to accelerate Spark SQL with OAP (https://github.com/Intel-bigdata/OAP) to accelerate SQL performance on Cloud to archive 8X performance gain and RDD cache to improve K-means performance with 2.5X performance gain leveraging Intel Optane DCPM. Also we will have a deep dive how Optane DCPM for these performance gains.
Speakers: Cheng Xu, Piotr Balcer
The document discusses storage challenges facing organizations such as increasing data volumes and dynamic workloads. It introduces Oracle's approach to engineered systems that integrate optimized hardware and software to simplify storage management. Key benefits highlighted include automatic database and storage tuning, advanced data compression techniques, and optimized solutions for Oracle databases and applications.
The document provides an overview of performance tuning for Oracle databases. It discusses tuning goals such as accessing the least number of blocks and caching blocks in memory. It outlines the tuning process which includes tuning the design, application, memory, I/O, contention and operating system. Common performance issues for OLTP systems like I/O bottlenecks are also covered. Various tools for identifying performance problems are presented.
Enterprise Storage Solutions for Overcoming Big Data and Analytics ChallengesINFINIDAT
Big Data and analytics workloads represent a new frontier for organizations. Data is being collected from sources that did not exist 10 years ago. Mobile phone data, machine-generated data, and website interaction data are all being collected and analyzed. In addition, as IT budgets are already being pressured down, Big Data footprints are getting larger and posing a huge storage challenge.
This paper provides information on the issues that Big Data applications pose for storage systems and how choosing the correct storage infrastructure can streamline and consolidate Big Data and analytics applications without breaking the bank.
InfiniBox bridges the gap between high performance and high capacity for Big Data applications. InfiniBox allows an organization implementing Big Data and Analytics projects to truly attain its business goals: cost reduction, continual and deep capacity scaling, and simple and effective management — and without any compromises in performance or reliability. All of this to effectively and efficiently support Big Data applications at a disruptive price point.
Learn more at www.infinidat.com.
The material was created around 2010-end, but published February'2011.
The main purposes of creating this document were as follows:
- very very few people working on SAP HANA at that time
- information regarding SAP HANA not really available and if available, it was scattered !!
- always pulled for presentations and technical demos which generally hampered my own hands-on work :). And I was alone at San Jose whereas Ulrich used to busy in Walldorf coordinating with SAP.
BTW, it was my full-fledged first SAP HANA presentation in end of 2010, although published in 2011. The document is quite old now but most of the part still holds good as of today.
Big Data Real Time Analytics - A Facebook Case StudyNati Shalom
Building Your Own Facebook Real Time Analytics System with Cassandra and GigaSpaces.
Facebook's real time analytics system is a good reference for those looking to build their real time analytics system for big data.
The first part covers the lessons from Facebook's experience and the reason they chose HBase over Cassandra.
In the second part of the session, we learn how we can build our own Real Time Analytics system, achieve better performance, gain real business insights, and business analytics on our big data, and make the deployment and scaling significantly simpler using the new version of Cassandra and GigaSpaces Cloudify.
InTech Event | Cognitive Infrastructure for Enterprise AIInTTrust S.A.
The document introduces the IBM Power Systems AC922 system as a cognitive infrastructure for enterprise AI. Some key points:
- Existing server infrastructures are not well-suited for modern AI workloads and large-scale cognitive data volumes.
- The AC922 is designed specifically for AI with accelerated computing capabilities like GPUs and fast interconnects to enable faster model training, larger models, and quicker time to value from AI projects.
- Features include the POWER9 processor, high-bandwidth NVLink connections between CPUs and multiple GPUs, support for large memory and accelerated databases/frameworks, and scaling to warehouse-sized deployments through distributed deep learning.
The document describes SGI UV for SAP HANA, a purpose-built in-memory computing appliance for large SAP HANA environments. It has a scale-up architecture that can scale from 4 to 32 sockets in a single node with up to 24TB of shared memory. Key features include Intel Xeon E7 processors, SUSE Linux, NetApp storage, and an SGI NUMAlink 7 interconnect that provides cache coherence across the unified memory.
GridGain Systems provides an in-memory data grid (IMDG) that offers extremely low latency access to application data stored fully in memory. Key features of GridGain's IMDG include support for distributed ACID transactions, scalable data partitioning, integration with in-memory compute grids, and datacenter replication to ensure high availability. The IMDG allows applications to work directly with domain objects and provides SQL querying capabilities for fast analysis of in-memory data.
Lessons learned processing 70 billion data points a day using the hybrid cloudDataWorks Summit
NetApp receives 70 billion data points of telemetry information each day from its customer’s storage systems. This telemetry data contains configuration information, performance counters, and logs. All of this data is processed using multiple Hadoop clusters, and feeds a machine learning pipeline and a data serving infrastructure that produces insights for customers via an application called Active IQ. We describe the evolution of our Hadoop infrastructure from a traditional on-premises architecture to the hybrid cloud, and lessons learned.
We’ll discuss the insights we are able to produce for our customers, and the techniques used. Finally, we describe the data management challenges with our multi-petabyte Hadoop data lake. We solved these problems by building a unified data lake on-premises and using the NetApp Data Fabric to seamlessly connect to public clouds for data science and machine learning compute resources.
Architecting a truly hybrid cloud implementation allowed NetApp to free up our data scientists to use any software on any cloud, kept the customer log data safe on NetApp Private Storage in Equinix, resulted in faster ability to innovate and release new code and provided flexibility to use any public cloud at the same time with data on NetApp in Equinix.
Speaker
Pranoop Erasani, NetApp, Senior Technical Director, ONTAP
Shankar Pasupathy, NetApp, Technical Director, ACE Engineering
The document provides an overview of performance tuning for Oracle databases. It discusses tuning goals such as accessing the least number of blocks and caching blocks in memory. It outlines the tuning process which includes tuning the design, application, memory, I/O, contention and operating system. Common performance issues for OLTP systems like I/O bottlenecks are also covered. Various tools for identifying performance problems are listed.
This presentation provides a clear overview of how Oracle Database In-Memory optimizes both analytics and mixed workloads, delivering outstanding performance while supporting real-time analytics, business intelligence, and reporting. It provides details on what you can expect from Database In-Memory in both Oracle Database 12.1.0.2 and 12.2.
Exadata architecture and internals presentationSanjoy Dasgupta
The document provides an overview of Oracle's Exadata database machine. It describes the Exadata X7-2 and X7-8 models, which feature the latest Intel Xeon processors, high-capacity flash storage, and an improved InfiniBand internal network. The document highlights how Exadata's unique smart database software optimizes performance for analytics, online transaction processing, and database consolidation workloads through techniques like smart scan query offloading to storage servers.
The Samsung Galaxy S8 Edge is Samsung's new flagship phone. It has an edge-to-edge display with almost no bezels and an impressive screen-to-body ratio. It has top-of-the-line specs including Bluetooth 5.0, IP68 water resistance, and powerful processors. However, some criticisms include poor battery life, an awkwardly placed fingerprint sensor, and fragility.
Differences between data lakes and datawarehouseamarkayam
The main reason for writing this article is to project the difference between data lakes and data warehouses for helping you to know more about data management.
The critical thing to remember about Spark and Hadoop is they are not mutually exclusive or inclusive but they work well together and makes the combination strong enough for lots of big data applications.
Reliance jio fi vs airtel 4g hotspot: a comparative analysisamarkayam
JustInReviews would like to give you a head-to-head comparison between Reliance JioFi and Airtel 4G Hotspot, as a part of this technology communication review.
Reliance jio fi vs airtel 4g hotspot a comparative analysis amarkayam
Reliance JioFi Vs Airtel 4G Hotspot. A point-wise comparison.
JustInReviews would like to give you a head-to-head comparison between Reliance JioFi and Airtel 4G Hotspot, as a part of this technology communication review.
Apache Kafka is a scalable, fault-tolerant, publish-subscribe messaging system that allows for high throughput and reliable delivery of data streams. It is commonly used for streaming data and real-time analysis, supporting use cases like processing geospatial data from sensors or trucks. Kafka provides high scalability, durability which ensures data is not lost, and reliability through replication. It works by having producers write data to topics which are stored as a commit log, and consumers can then read from topics.
Data modeling is a process of structuring and organizing data in a database management system. It defines how data is organized and poses constraints on the structure. A data model describes the data structures and how the data is organized. For example, in a database management system. Generic data modeling represents entities, relationships between entities, and individual instances of entities. It follows rules such as representing relationships to other entity types and naming entity types based on their underlying nature.
Data management distinguishes the organization components of data rebase management from the technology used to manage data; it is more carefully arranged with the actual organization customers of data.
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...Social Samosa
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
State of Artificial intelligence Report 2023kuntobimo2016
Artificial intelligence (AI) is a multidisciplinary field of science and engineering whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
Predictions: What we believe will happen in the next 12 months and a 2022 performance review to keep us honest.
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
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This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
2. Stay Connected For More Updates
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An in-memory computing platform called as Apache Ignite can be
inputed between a user's application layer and data layer. From the
current disk-based storage layer into RAM, enhancing six orders of
magnitude and performance.
For handling peta bytes of data to which the in-memory data
capacity can be easily scaled. Both the ACID transactions and SQL
queries are further supported. Scale, performance, and
comprehensive capabilities far above and beyond what traditional in
memory databases, data grids are offered by Ignite.
3. Stay Connected For More Updates
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Key Features
There is an in-memory data grid for handling distributed in-memory
data management and it is contained in Apache Ignite. You will find
object based, ACID transactional, failover, in-memory key value
store, etc. On the contrary to traditional database management
systems, primary storage mechanism are used by the Apache Ignite.
Instead of disk if you are using the memory then it increase its speed
upto 1 million times faster than traditional databases.
4. Stay Connected For More Updates
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The field queries concept of backing up to reduce the serialization
and network overhead is also supported by Ignite.
A computer grid for enabling parallel in memory processing is
included in the Apache Ignite. There are other CPU-intensive or
other resource-intensive tasks like traditional MPP, HPC, fork-join,
and Map Reduce processing. For Standard Java Executor Service
asynchronous processing is backed up by Apache.
5. Stay Connected For More Updates
• Join the DBA course to make your career in this field.
• Stay connected to CRB Tech for more technical optimization and
other updates and information.
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