A short history of how we got stuck with the notion that web applications require an ORM on top of an RDBMS, and an examination of the pros and cons of such a tight coupling.
Perhaps ORM isn't as natural a fit for your application as a key-value store?
This document discusses the limitations of relational databases for modern applications and real-time architectures. It describes how NoSQL databases like Aerospike can provide better performance and scalability. Specific examples are given of how Aerospike has been used to power applications in domains like advertising technology, social media, travel portals, and financial services that require high throughput, low latency access to large datasets.
The document provides an overview of Redis, including:
- Redis is an in-memory database that supports data structures like strings, lists, sets, and hashes. It is often used for caching, messaging, and building real-time applications.
- Major companies like Twitter, GitHub, and Pinterest use Redis for its speed and support for complex data types.
- Redis can be deployed in standalone, master-slave, or cluster topologies to provide redundancy, scaling, and automatic failover. Persistence to disk can be configured using snapshots or append-only files.
- Redis offers advantages over other databases and caching solutions in terms of performance, data types, scalability, and availability. It has a simple
The document discusses SQL versus NoSQL databases. It provides background on SQL databases and their advantages, then explains why some large tech companies have adopted NoSQL databases instead. Specifically, it describes how companies like Amazon, Facebook, and Google have such massive amounts of data that traditional SQL databases cannot adequately handle the scale, performance, and flexibility needs. It then summarizes some popular NoSQL databases like Cassandra, Hadoop, MongoDB that were developed to solve the challenges of scaling to big data workloads.
Lessons Learned From Running Spark On DockerSpark Summit
Running Spark on Docker containers provides flexibility for data scientists and control for IT. Some key lessons learned include optimizing CPU and memory resources to avoid noisy neighbor problems, managing Docker images efficiently, using network plugins for multi-host connectivity, and addressing storage and security considerations. Performance testing showed Spark on Docker containers can achieve comparable performance to bare metal deployments for large-scale data processing workloads.
This document discusses various considerations and steps for migrating from an Oracle database to PostgreSQL. It begins by explaining some key differences between the two databases regarding transactions, schemas, views, and other concepts. It then outlines the main steps of the migration process: migrating the database schema, migrating the data, migrating stored code like PL/SQL, migrating SQL statements, and migrating the application itself. Specific challenges for each step are explored, such as data type translations, handling PL/SQL, and translating Oracle-specific SQL. Finally, several migration tools are briefly described.
The document discusses Ozone, which is designed to address HDFS scalability limitations and enable trillions of file system objects. It was created as HDFS struggles with hundreds of millions of files. Ozone uses a microservices architecture of Ozone Manager, Storage Container Managers, and Recon Server to divide responsibilities and scale independently. It provides seamless transition for applications like YARN, MapReduce, Hive and Spark, and supports Kubernetes deployments. The document outlines Ozone's architecture, deployment options, write and read paths, usage similarities to HDFS/S3, enterprise-grade features around security, high availability and roadmap.
Large Scale Data Analytics with Spark and Cassandra on the DSE PlatformDataStax Academy
In this talk will show how Large Scale Data Analytics can be done with Spark and Cassandra on the DataStax Enterprise Platform. First we will give an overview of what is the Spark Cassandra Connector and how it enables working with large data sets. Then we will use the Spark Notebook to show live examples in the browser of interacting with the data. The example will load a large Movies Database from Cassandra into Spark and then show how that data can be transformed and analyzed using Spark.
This document discusses the limitations of relational databases for modern applications and real-time architectures. It describes how NoSQL databases like Aerospike can provide better performance and scalability. Specific examples are given of how Aerospike has been used to power applications in domains like advertising technology, social media, travel portals, and financial services that require high throughput, low latency access to large datasets.
The document provides an overview of Redis, including:
- Redis is an in-memory database that supports data structures like strings, lists, sets, and hashes. It is often used for caching, messaging, and building real-time applications.
- Major companies like Twitter, GitHub, and Pinterest use Redis for its speed and support for complex data types.
- Redis can be deployed in standalone, master-slave, or cluster topologies to provide redundancy, scaling, and automatic failover. Persistence to disk can be configured using snapshots or append-only files.
- Redis offers advantages over other databases and caching solutions in terms of performance, data types, scalability, and availability. It has a simple
The document discusses SQL versus NoSQL databases. It provides background on SQL databases and their advantages, then explains why some large tech companies have adopted NoSQL databases instead. Specifically, it describes how companies like Amazon, Facebook, and Google have such massive amounts of data that traditional SQL databases cannot adequately handle the scale, performance, and flexibility needs. It then summarizes some popular NoSQL databases like Cassandra, Hadoop, MongoDB that were developed to solve the challenges of scaling to big data workloads.
Lessons Learned From Running Spark On DockerSpark Summit
Running Spark on Docker containers provides flexibility for data scientists and control for IT. Some key lessons learned include optimizing CPU and memory resources to avoid noisy neighbor problems, managing Docker images efficiently, using network plugins for multi-host connectivity, and addressing storage and security considerations. Performance testing showed Spark on Docker containers can achieve comparable performance to bare metal deployments for large-scale data processing workloads.
This document discusses various considerations and steps for migrating from an Oracle database to PostgreSQL. It begins by explaining some key differences between the two databases regarding transactions, schemas, views, and other concepts. It then outlines the main steps of the migration process: migrating the database schema, migrating the data, migrating stored code like PL/SQL, migrating SQL statements, and migrating the application itself. Specific challenges for each step are explored, such as data type translations, handling PL/SQL, and translating Oracle-specific SQL. Finally, several migration tools are briefly described.
The document discusses Ozone, which is designed to address HDFS scalability limitations and enable trillions of file system objects. It was created as HDFS struggles with hundreds of millions of files. Ozone uses a microservices architecture of Ozone Manager, Storage Container Managers, and Recon Server to divide responsibilities and scale independently. It provides seamless transition for applications like YARN, MapReduce, Hive and Spark, and supports Kubernetes deployments. The document outlines Ozone's architecture, deployment options, write and read paths, usage similarities to HDFS/S3, enterprise-grade features around security, high availability and roadmap.
Large Scale Data Analytics with Spark and Cassandra on the DSE PlatformDataStax Academy
In this talk will show how Large Scale Data Analytics can be done with Spark and Cassandra on the DataStax Enterprise Platform. First we will give an overview of what is the Spark Cassandra Connector and how it enables working with large data sets. Then we will use the Spark Notebook to show live examples in the browser of interacting with the data. The example will load a large Movies Database from Cassandra into Spark and then show how that data can be transformed and analyzed using Spark.
Hadoop World 2011: Advanced HBase Schema Design - Lars George, ClouderaCloudera, Inc.
"While running a simple key/value based solution on HBase usually requires an equally simple schema, it is less trivial to operate a different application that has to insert thousands of records per second.
This talk will address the architectural challenges when designing for either read or write performance imposed by HBase. It will include examples of real world use-cases and how they can be implemented on top of HBase, using schemas that optimize for the given access patterns. "
Lessons Learned from Dockerizing Spark WorkloadsBlueData, Inc.
Many initiatives for running applications inside containers have been scoped to run on a single host. Using Docker containers for large-scale production environments poses interesting challenges, especially when deploying distributed Big Data applications like Apache Spark.
Some of these challenges include container lifecycle management, smart scheduling for optimal resource utilization, network configuration and security, and performance. BlueData is “all in” on Docker containers – with a specific focus on Spark applications. They’ve learned first-hand how to address these challenges for Fortune 500 enterprises and government organizations that want to deploy Big Data workloads using Docker.
This session at Spark Summit in February 2017 (by Thomas Phelan, co-founder and chief architect at BlueData) described lessons learned as well as some tips and tricks on how to Dockerize your Big Data applications in a reliable, scalable, and high-performance environment.
In this session, Tom described how to network Docker containers across multiple hosts securely. He discussed ways to achieve high availability across distributed Big Data applications and hosts in your data center. And since we’re talking about very large volumes of data, performance is a key factor. So Tom discussed some of the storage options that BlueData explored and implemented to achieve near bare-metal I/O performance for Spark using Docker.
https://spark-summit.org/east-2017/events/lessons-learned-from-dockerizing-spark-workloads
This document discusses WANdisco's Non-Stop Hadoop solution, which provides continuous availability of Hadoop across local and wide area networks using an active-active replication technique. It addresses key problems with multi-cluster Hadoop deployments like lack of 100% uptime and challenges sharing data globally. The solution utilizes WANdisco's patented distributed coordination engine to achieve consensus across data centers for metadata operations and absolute consistency. Use cases highlighted include eliminating single point of failures, enabling parallel data ingest across locations, optimizing resource utilization through cluster zoning, and achieving near-zero RTO disaster recovery.
The rise of NoSQL is characterized with confusion and ambiguity; very much like any fast-emerging organic movement in the absence of well-defined standards and adequate software solutions. Whether you are a developer or an architect, many questions come to mind when faced with the decision of where your data should be stored and how it should be managed. The following are some of these questions: What does the rise of all these NoSQL technologies mean to my enterprise? What is NoSQL to begin with? Does it mean "No SQL"? Could this be just another fad? Is it a good idea to bet the future of my enterprise on these new exotic technologies and simply abandon proven mature Relational DataBase Management Systems (RDBMS)? How scalable is scalable? Assuming that I am sold, how do I choose the one that fit my needs best? Is there a middle ground somewhere? What is this Polyglot Persistence I hear about? The answers to these questions and many more is the subject of this talk along with a survey of the most popular of NoSQL technologies. Be there or be square.
This document provides an overview of a NoSQL Night event presented by Clarence J M Tauro from Couchbase. The presentation introduces NoSQL databases and discusses some of their advantages over relational databases, including scalability, availability, and partition tolerance. It covers key concepts like the CAP theorem and BASE properties. The document also provides details about Couchbase, a popular document-oriented NoSQL database, including its architecture, data model using JSON documents, and basic operations. Finally, it advertises Couchbase training courses for getting started and administration.
HDFS Tiered Storage: Mounting Object Stores in HDFSDataWorks Summit
Most users know HDFS as the reliable store of record for big data analytics. HDFS is also used to store transient and operational data when working with cloud object stores, such as Azure HDInsight and Amazon EMR. In these settings- but also in more traditional, on premise deployments- applications often manage data stored in multiple storage systems or clusters, requiring a complex workflow for synchronizing data between filesystems to achieve goals for durability, performance, and coordination.
Building on existing heterogeneous storage support, we add a storage tier to HDFS to work with external stores, allowing remote namespaces to be "mounted" in HDFS. This capability not only supports transparent caching of remote data as HDFS blocks, it also supports synchronous writes to remote clusters for business continuity planning (BCP) and supports hybrid cloud architectures.
This idea was presented at last year’s Summit in San Jose. Lots of progress has been made since then and the feature is in active development at the Apache Software Foundation on branch HDFS-9806, driven by Microsoft and Western Digital. We will discuss the refined design & implementation and present how end-users and admins will be able to use this powerful functionality.
Polyglot Persistence - Two Great Tastes That Taste Great TogetherJohn Wood
The days of the relational database being a one-stop-shop for all of your persistence needs are over. Although NoSQL databases address some issues that can’t be addressed by relational databases, the opposite is true as well. The relational database offers an unparalleled feature set and rock solid stability. One cannot underestimate the importance of using the right tool for the job, and for some jobs, one tool is not enough. This talk focuses on the strength and weaknesses of both relational and NoSQL databases, the benefits and challenges of polyglot persistence, and examples of polyglot persistence in the wild.
These slides were presented at WindyCityDB 2010.
This presentation, delivered by Howard Marks at Interop in Las Vegas May 2013 explores how system administrators can provide high performance storage for VDI implementations.
This document provides an overview of patterns for scalability, availability, and stability in distributed systems. It discusses general recommendations like immutability and referential transparency. It covers scalability trade-offs around performance vs scalability, latency vs throughput, and availability vs consistency. It then describes various patterns for scalability including managing state through partitioning, caching, sharding databases, and using distributed caching. It also covers patterns for managing behavior through event-driven architecture, compute grids, load balancing, and parallel computing. Availability patterns like fail-over, replication, and fault tolerance are discussed. The document provides examples of popular technologies that implement many of these patterns.
My talk on NOSQL at OGF29.[Update with OSCON'10 presentation!] But updates do not work reliably in slideshare. So I also have latest version with my blog.
Have you ever heard the buzzword "big data"? Big data is briefly described to collect massive amounts of data and extract all the small details and larger trends that are available. Summarize the output and generate important insight about customers and competitors.
Enterprises seem to have sensed that something is in the air and have started to shop technology. So what has the world to offer for enterprises that have an unknown amount of petabytes flowing through their systems on a daily basis? There are a few options, but really few that can catch up with the popularity of Hadoop. Hadoop can store and process large amounts of data. It has a large and diverse toolset for integrations, operations and processing and it is open source!
The document discusses running Hadoop clusters in the cloud and the challenges that presents. It introduces CloudFarmer, a tool that allows defining roles for VMs and dynamically allocating VMs to roles. This allows building agile Hadoop clusters in the cloud that can adapt as needs change without static configurations. CloudFarmer provides a web UI to manage roles and hosts.
This document summarizes a talk about Facebook's use of HBase for messaging data. It discusses how Facebook migrated data from MySQL to HBase to store metadata, search indexes, and small messages in HBase for improved scalability. It also outlines performance improvements made to HBase, such as for compactions and reads, and future plans such as cross-datacenter replication and running HBase in a multi-tenant environment.
Hadoop Operations - Best Practices from the FieldDataWorks Summit
This document discusses best practices for Hadoop operations based on analysis of support cases. Key learnings include using HDFS ACLs and snapshots to prevent accidental data deletion and improve recoverability. HDFS improvements like pausing block deletion and adding diagnostics help address incidents around namespace mismatches and upgrade failures. Proper configuration of hardware, JVM settings, and monitoring is also emphasized.
This document presents an introduction to NoSQL databases. It begins with an overview comparing SQL and NoSQL databases, describing the architecture of NoSQL databases. Examples of different types of NoSQL databases are provided, including key-value stores, column family stores, document databases and graph databases. MapReduce programming is also introduced. Popular NoSQL databases like Cassandra, MongoDB, HBase, and CouchDB are described. The document concludes that NoSQL is well-suited for large, highly distributed data problems.
Dave Shuttleworth - Platform performance comparisons, bare metal and cloud ho...huguk
Choosing the right database technology and deployment platform can have a major impact on performance and total cost of ownership in production environments. Using the industry TPC-DS benchmark, Dave will present findings from a performance and TCO comparison of EXASOL on dedicated servers, Bigstep bare metal cloud and AWS. The presentation will be a tutorial on performance benchmarking.
Ruby on Rails (RoR) as a back-end processor for Apex Espen Brækken
This document discusses using Ruby and Ruby on Rails (RoR) as a supplement to Oracle Application Express (Apex). It provides an overview of why a supplement may be needed, why Ruby and Rails were chosen, and how ActiveRecord in Rails simplifies database access through object mapping. Key points covered include conventions over configuration in Rails, the anatomy of Rails including ActiveRecord, and examples of ActiveRecord usage with database configuration through YAML files rather than direct connection hashes.
Hadoop World 2011: Advanced HBase Schema Design - Lars George, ClouderaCloudera, Inc.
"While running a simple key/value based solution on HBase usually requires an equally simple schema, it is less trivial to operate a different application that has to insert thousands of records per second.
This talk will address the architectural challenges when designing for either read or write performance imposed by HBase. It will include examples of real world use-cases and how they can be implemented on top of HBase, using schemas that optimize for the given access patterns. "
Lessons Learned from Dockerizing Spark WorkloadsBlueData, Inc.
Many initiatives for running applications inside containers have been scoped to run on a single host. Using Docker containers for large-scale production environments poses interesting challenges, especially when deploying distributed Big Data applications like Apache Spark.
Some of these challenges include container lifecycle management, smart scheduling for optimal resource utilization, network configuration and security, and performance. BlueData is “all in” on Docker containers – with a specific focus on Spark applications. They’ve learned first-hand how to address these challenges for Fortune 500 enterprises and government organizations that want to deploy Big Data workloads using Docker.
This session at Spark Summit in February 2017 (by Thomas Phelan, co-founder and chief architect at BlueData) described lessons learned as well as some tips and tricks on how to Dockerize your Big Data applications in a reliable, scalable, and high-performance environment.
In this session, Tom described how to network Docker containers across multiple hosts securely. He discussed ways to achieve high availability across distributed Big Data applications and hosts in your data center. And since we’re talking about very large volumes of data, performance is a key factor. So Tom discussed some of the storage options that BlueData explored and implemented to achieve near bare-metal I/O performance for Spark using Docker.
https://spark-summit.org/east-2017/events/lessons-learned-from-dockerizing-spark-workloads
This document discusses WANdisco's Non-Stop Hadoop solution, which provides continuous availability of Hadoop across local and wide area networks using an active-active replication technique. It addresses key problems with multi-cluster Hadoop deployments like lack of 100% uptime and challenges sharing data globally. The solution utilizes WANdisco's patented distributed coordination engine to achieve consensus across data centers for metadata operations and absolute consistency. Use cases highlighted include eliminating single point of failures, enabling parallel data ingest across locations, optimizing resource utilization through cluster zoning, and achieving near-zero RTO disaster recovery.
The rise of NoSQL is characterized with confusion and ambiguity; very much like any fast-emerging organic movement in the absence of well-defined standards and adequate software solutions. Whether you are a developer or an architect, many questions come to mind when faced with the decision of where your data should be stored and how it should be managed. The following are some of these questions: What does the rise of all these NoSQL technologies mean to my enterprise? What is NoSQL to begin with? Does it mean "No SQL"? Could this be just another fad? Is it a good idea to bet the future of my enterprise on these new exotic technologies and simply abandon proven mature Relational DataBase Management Systems (RDBMS)? How scalable is scalable? Assuming that I am sold, how do I choose the one that fit my needs best? Is there a middle ground somewhere? What is this Polyglot Persistence I hear about? The answers to these questions and many more is the subject of this talk along with a survey of the most popular of NoSQL technologies. Be there or be square.
This document provides an overview of a NoSQL Night event presented by Clarence J M Tauro from Couchbase. The presentation introduces NoSQL databases and discusses some of their advantages over relational databases, including scalability, availability, and partition tolerance. It covers key concepts like the CAP theorem and BASE properties. The document also provides details about Couchbase, a popular document-oriented NoSQL database, including its architecture, data model using JSON documents, and basic operations. Finally, it advertises Couchbase training courses for getting started and administration.
HDFS Tiered Storage: Mounting Object Stores in HDFSDataWorks Summit
Most users know HDFS as the reliable store of record for big data analytics. HDFS is also used to store transient and operational data when working with cloud object stores, such as Azure HDInsight and Amazon EMR. In these settings- but also in more traditional, on premise deployments- applications often manage data stored in multiple storage systems or clusters, requiring a complex workflow for synchronizing data between filesystems to achieve goals for durability, performance, and coordination.
Building on existing heterogeneous storage support, we add a storage tier to HDFS to work with external stores, allowing remote namespaces to be "mounted" in HDFS. This capability not only supports transparent caching of remote data as HDFS blocks, it also supports synchronous writes to remote clusters for business continuity planning (BCP) and supports hybrid cloud architectures.
This idea was presented at last year’s Summit in San Jose. Lots of progress has been made since then and the feature is in active development at the Apache Software Foundation on branch HDFS-9806, driven by Microsoft and Western Digital. We will discuss the refined design & implementation and present how end-users and admins will be able to use this powerful functionality.
Polyglot Persistence - Two Great Tastes That Taste Great TogetherJohn Wood
The days of the relational database being a one-stop-shop for all of your persistence needs are over. Although NoSQL databases address some issues that can’t be addressed by relational databases, the opposite is true as well. The relational database offers an unparalleled feature set and rock solid stability. One cannot underestimate the importance of using the right tool for the job, and for some jobs, one tool is not enough. This talk focuses on the strength and weaknesses of both relational and NoSQL databases, the benefits and challenges of polyglot persistence, and examples of polyglot persistence in the wild.
These slides were presented at WindyCityDB 2010.
This presentation, delivered by Howard Marks at Interop in Las Vegas May 2013 explores how system administrators can provide high performance storage for VDI implementations.
This document provides an overview of patterns for scalability, availability, and stability in distributed systems. It discusses general recommendations like immutability and referential transparency. It covers scalability trade-offs around performance vs scalability, latency vs throughput, and availability vs consistency. It then describes various patterns for scalability including managing state through partitioning, caching, sharding databases, and using distributed caching. It also covers patterns for managing behavior through event-driven architecture, compute grids, load balancing, and parallel computing. Availability patterns like fail-over, replication, and fault tolerance are discussed. The document provides examples of popular technologies that implement many of these patterns.
My talk on NOSQL at OGF29.[Update with OSCON'10 presentation!] But updates do not work reliably in slideshare. So I also have latest version with my blog.
Have you ever heard the buzzword "big data"? Big data is briefly described to collect massive amounts of data and extract all the small details and larger trends that are available. Summarize the output and generate important insight about customers and competitors.
Enterprises seem to have sensed that something is in the air and have started to shop technology. So what has the world to offer for enterprises that have an unknown amount of petabytes flowing through their systems on a daily basis? There are a few options, but really few that can catch up with the popularity of Hadoop. Hadoop can store and process large amounts of data. It has a large and diverse toolset for integrations, operations and processing and it is open source!
The document discusses running Hadoop clusters in the cloud and the challenges that presents. It introduces CloudFarmer, a tool that allows defining roles for VMs and dynamically allocating VMs to roles. This allows building agile Hadoop clusters in the cloud that can adapt as needs change without static configurations. CloudFarmer provides a web UI to manage roles and hosts.
This document summarizes a talk about Facebook's use of HBase for messaging data. It discusses how Facebook migrated data from MySQL to HBase to store metadata, search indexes, and small messages in HBase for improved scalability. It also outlines performance improvements made to HBase, such as for compactions and reads, and future plans such as cross-datacenter replication and running HBase in a multi-tenant environment.
Hadoop Operations - Best Practices from the FieldDataWorks Summit
This document discusses best practices for Hadoop operations based on analysis of support cases. Key learnings include using HDFS ACLs and snapshots to prevent accidental data deletion and improve recoverability. HDFS improvements like pausing block deletion and adding diagnostics help address incidents around namespace mismatches and upgrade failures. Proper configuration of hardware, JVM settings, and monitoring is also emphasized.
This document presents an introduction to NoSQL databases. It begins with an overview comparing SQL and NoSQL databases, describing the architecture of NoSQL databases. Examples of different types of NoSQL databases are provided, including key-value stores, column family stores, document databases and graph databases. MapReduce programming is also introduced. Popular NoSQL databases like Cassandra, MongoDB, HBase, and CouchDB are described. The document concludes that NoSQL is well-suited for large, highly distributed data problems.
Dave Shuttleworth - Platform performance comparisons, bare metal and cloud ho...huguk
Choosing the right database technology and deployment platform can have a major impact on performance and total cost of ownership in production environments. Using the industry TPC-DS benchmark, Dave will present findings from a performance and TCO comparison of EXASOL on dedicated servers, Bigstep bare metal cloud and AWS. The presentation will be a tutorial on performance benchmarking.
Ruby on Rails (RoR) as a back-end processor for Apex Espen Brækken
This document discusses using Ruby and Ruby on Rails (RoR) as a supplement to Oracle Application Express (Apex). It provides an overview of why a supplement may be needed, why Ruby and Rails were chosen, and how ActiveRecord in Rails simplifies database access through object mapping. Key points covered include conventions over configuration in Rails, the anatomy of Rails including ActiveRecord, and examples of ActiveRecord usage with database configuration through YAML files rather than direct connection hashes.
Why Traditional Databases Fail so Miserably to Scale with E-Commerce Site GrowthClustrix
Traditional SQL database scaling in e-commerce is a difficult, tedious, labor-intensive, and ultimately unsustainable process. Many DBAs and IT organizations have come to the conclusion that the traditional SQL databases, like MySQL, fundamentally cannot keep up and scale with the explosive growth of e-commerce. They say it's just too unwieldy and costly because SQL databases were not designed to truly scale, and especially to e-commerce cloud scale. Yet there are plenty of database professionals that hold the contrarian view that anyone that believes traditional databases don't scale simple lacks the knowledge, experience, and expertise to actually make them do so.
So who's right? Do traditional SQL databases have an e-commerce cloud scale issue or not?
During this webinar Marc Staimer, President and CDS of Dragon Slayer Consulting and Tony Barbagallo, Chief Marketing Officer for Clustrix, will examine this issue in detail, how traditional SQL databases scale, common workarounds to known e-commerce cloud scale problems, e-commerce scaling requirements, and organizational tolerance for manually labor-intensive sweat equity.
Please watch the recording of this lively, entertaining, and educational discussion: https://www.brighttalk.com/webcast/7485/128253
This document provides an introduction to assembler programming on IBM System z mainframes. It discusses why programmers may choose to use assembler, prerequisites for assembler programming, an overview of the z/Architecture including registers and memory organization. It also covers binary and hexadecimal number representation, and base-displacement addressing which allows specifying a memory address using a register and offset value. The intended audience is those with basic computer programming and z/OS knowledge.
The document provides an overview of the Aerospike architecture, including the client, cluster, storage, primary and secondary indexes, RAM, flash storage, and cross datacenter replication (XDR). The Aerospike architecture aims to handle extremely high read/write rates over persistent data at low latency while ensuring consistency and scalability across datacenters with no downtime.
This document discusses topics related to NoSQL data management and distribution models in big data analytics. It covers key-value and document data models, as well as graph databases and schema-less databases. It then describes several distribution models including single server, sharding, master-slave replication, peer-to-peer replication, and combining sharding and replication. Specific examples of these models in MongoDB and Cassandra are provided. The next session will cover Cassandra's data model.
What enterprises can learn from Real Time BiddingAerospike
Brian Bulkowski, CTO of Aerospike, the NoSQL database, discusses the software architecture pioneered in cutting edge advertising optimizations companies in 2008, made popular between 2009 and 2013, and now becoming more widely used in Financial Services, Retail, Social Media, Travel companies, and others. This new technology architecture focuses on multiple big data analytics sources - HDFS based batch engines, using Hadoop, Hive, Hbase, Vertica, Spark, and others depending on analysis and query patterns - with an operational and application layer. The operational application level consists of new internet application stacks, such as Node.js, Nginx, Jetty, Scala, and Go, and in-memory NoSQL databases such as MongoDB, Cassandra, and Aerospike.
Specific recommendations regarding building a high-performance operational layer are presented. In particular, focusing on primary-key access at the operational layer, using Flash for the random in-memory nosql layer, and the benefits of Open Source were presented.
This presentation was given at the Big Data Gurus meetup in Santa Clara, CA, on July 29, 2014. http://www.meetup.com/BigDataGurus/
This document discusses how real-time bidding platforms achieve operational big data capabilities. It describes how real-time bidding platforms process billions of events per second to conduct auctions in under 100 milliseconds. It also outlines several lessons learned from real-time bidding that can be applied to other systems requiring operational big data capabilities, such as using an in-memory NoSQL database and optimizing for fast reads and writes.
A brave new world in mutable big data relational storage (Strata NYC 2017)Todd Lipcon
The ever-increasing interest in running fast analytic scans on constantly updating data is stretching the capabilities of HDFS and NoSQL storage. Users want the fast online updates and serving of real-time data that NoSQL offers, as well as the fast scans, analytics, and processing of HDFS. Additionally, users are demanding that big data storage systems integrate natively with their existing BI and analytic technology investments, which typically use SQL as the standard query language of choice. This demand has led big data back to a familiar friend: relationally structured data storage systems.
Todd Lipcon explores the advantages of relational storage and reviews new developments, including Google Cloud Spanner and Apache Kudu, which provide a scalable relational solution for users who have too much data for a legacy high-performance analytic system. Todd explains how to address use cases that fall between HDFS and NoSQL with technologies like Apache Kudu or Google Cloud Spanner and how the combination of relational data models, SQL query support, and native API-based access enables the next generation of big data applications. Along the way, he also covers suggested architectures, the performance characteristics of Kudu and Spanner, and the deployment flexibility each option provides.
“NoSQL” is more than just a popular industry buzzword. It’s been 17 years since Carlo Strozzi coined the neologism. The concept though, is older than that. In fact, the world had only NoSQL databases long before SQL was invented in the 1970s, thus proving the adage everything old is new again. In recent years a new wave of enterprise-class NoSQL databases have come to the fore to challenge the supremacy of Relational Database Management Systems (RDBMS). One such example is Aerospike, a Flash/SSD-optimized key-value store. In his presentation, Aerospike’s Director of Application Engineering, Peter Milne, will take you through a deep dive comparison. What are the conceptual differences between NoSQL and RDBMS? Why consider one versus the other for your use case? What data modeling, architectural best practices and practical migration steps should you apply to smoothly transition your business to the new world of NoSQL?
This document provides an overview of Dynamo, Amazon's highly available key-value store. It discusses that Dynamo sacrifices strong consistency for availability and uses an eventually consistent model. Dynamo uses consistent hashing to partition and replicate data across nodes, and employs data versioning to resolve conflicts from concurrent updates. The system architecture of Dynamo includes components for data partitioning, replication, versioning, APIs, and membership management.
This document contains slides from a presentation on writing better MySQL queries for beginners. The presentation covers SQL history and syntax, data storage and types, table design, indexes, query monitoring and optimization. It emphasizes selecting only necessary columns, using appropriate data types, normalizing tables, indexing columns used in WHERE clauses, and monitoring queries to optimize performance. Resources for learning more about MySQL are provided at the end.
Presentation given during a tour of Australia, in May 2009. The targeted audience are people who are already familiar with the fundamentals of Semantic Web, and this presentation gives an overview of what is happening at W3C
[db tech showcase Tokyo 2016] E32: My Life as a Disruptor by Jim StarkeyInsight Technology, Inc.
I’ve championed or developed four distinct disruptive technologies in database management. I started working on databases for the ARPAnet - the precursor of the Internet which had 47 nodes and was the largest network on earth. I advocated relational technology when it was considered an academic curiosity and introduced a new concurrency control technology that made consistency practical. More recently I created a radically new architecture for distributed ACID SQL databases. Now, my project is a critical re-evaluation of where we are, how we got here, and where we should be going. It’s going to be a wild ride.
Boston meetup : MySQL Innodb Cluster - May 1st 2017Frederic Descamps
MySQL InnoDB Cluster provides an easy-to-use high availability solution for MySQL databases. It utilizes Group Replication, which replicates data across multiple database nodes to provide redundancy and automatic failover. This allows the database to continue operating even if individual nodes fail. MySQL InnoDB Cluster handles replication, provisioning, and failover automatically without complex configuration needed for traditional asynchronous replication topologies.
Recommendation engine using Aerospike and/OR MongoDBPeter Milne
Recommendations are used through out the online world to recommend to a user other products or service they may be interested in. For example, an e commerce site may recommend other products for sale, or an online entertainment site, like Hulu or NetFlicks, may offer entertainment recommendations. This presentation discusses a very elementary recommendation engine implemented in Aerospike and MongoDB
The document discusses some of the shortcomings of object-relational mapping (ORM) frameworks. It argues that ORMs can lead developers to go fast initially but end up going slow, as ORMs don't allow for efficient database use and can result in poor code quality. The document also explains that relational data is about subsets of data, not individual objects, and that developers should take control of SQL and understand database concepts rather than relying solely on ORM frameworks.
This document discusses CouchDB, a database that stores data in JSON documents without a predefined schema. It notes that while relational databases require upfront schema design, CouchDB stores isolated document records without a schema, which allows for flexibility. The document also introduces Jan Lehnardt as a project member and web developer at Apache who can answer questions about CouchDB.
Microservice Teams - How the cloud changes the way we workSven Peters
A lot of technical challenges and complexity come with building a cloud-native and distributed architecture. The way we develop backend software has fundamentally changed in the last ten years. Managing a microservices architecture demands a lot of us to ensure observability and operational resiliency. But did you also change the way you run your development teams?
Sven will talk about Atlassian’s journey from a monolith to a multi-tenanted architecture and how it affected the way the engineering teams work. You will learn how we shifted to service ownership, moved to more autonomous teams (and its challenges), and established platform and enablement teams.
Takashi Kobayashi and Hironori Washizaki, "SWEBOK Guide and Future of SE Education," First International Symposium on the Future of Software Engineering (FUSE), June 3-6, 2024, Okinawa, Japan
Transform Your Communication with Cloud-Based IVR SolutionsTheSMSPoint
Discover the power of Cloud-Based IVR Solutions to streamline communication processes. Embrace scalability and cost-efficiency while enhancing customer experiences with features like automated call routing and voice recognition. Accessible from anywhere, these solutions integrate seamlessly with existing systems, providing real-time analytics for continuous improvement. Revolutionize your communication strategy today with Cloud-Based IVR Solutions. Learn more at: https://thesmspoint.com/channel/cloud-telephony
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI AppGoogle
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI App
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✅Create Stunning AI App Suite Fully Powered By Google's Latest AI technology, Gemini
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See My Other Reviews Article:
(1) AI Genie Review: https://sumonreview.com/ai-genie-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
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Flutter is a popular open source, cross-platform framework developed by Google. In this webinar we'll explore Flutter and its architecture, delve into the Flutter Embedder and Flutter’s Dart language, discover how to leverage Flutter for embedded device development, learn about Automotive Grade Linux (AGL) and its consortium and understand the rationale behind AGL's choice of Flutter for next-gen IVI systems. Don’t miss this opportunity to discover whether Flutter is right for your project.
E-commerce Application Development Company.pdfHornet Dynamics
Your business can reach new heights with our assistance as we design solutions that are specifically appropriate for your goals and vision. Our eCommerce application solutions can digitally coordinate all retail operations processes to meet the demands of the marketplace while maintaining business continuity.
Zoom is a comprehensive platform designed to connect individuals and teams efficiently. With its user-friendly interface and powerful features, Zoom has become a go-to solution for virtual communication and collaboration. It offers a range of tools, including virtual meetings, team chat, VoIP phone systems, online whiteboards, and AI companions, to streamline workflows and enhance productivity.
Enterprise Resource Planning System includes various modules that reduce any business's workload. Additionally, it organizes the workflows, which drives towards enhancing productivity. Here are a detailed explanation of the ERP modules. Going through the points will help you understand how the software is changing the work dynamics.
To know more details here: https://blogs.nyggs.com/nyggs/enterprise-resource-planning-erp-system-modules/
Odoo ERP software
Odoo ERP software, a leading open-source software for Enterprise Resource Planning (ERP) and business management, has recently launched its latest version, Odoo 17 Community Edition. This update introduces a range of new features and enhancements designed to streamline business operations and support growth.
The Odoo Community serves as a cost-free edition within the Odoo suite of ERP systems. Tailored to accommodate the standard needs of business operations, it provides a robust platform suitable for organisations of different sizes and business sectors. Within the Odoo Community Edition, users can access a variety of essential features and services essential for managing day-to-day tasks efficiently.
This blog presents a detailed overview of the features available within the Odoo 17 Community edition, and the differences between Odoo 17 community and enterprise editions, aiming to equip you with the necessary information to make an informed decision about its suitability for your business.
May Marketo Masterclass, London MUG May 22 2024.pdfAdele Miller
Can't make Adobe Summit in Vegas? No sweat because the EMEA Marketo Engage Champions are coming to London to share their Summit sessions, insights and more!
This is a MUG with a twist you don't want to miss.
Introducing Crescat - Event Management Software for Venues, Festivals and Eve...Crescat
Crescat is industry-trusted event management software, built by event professionals for event professionals. Founded in 2017, we have three key products tailored for the live event industry.
Crescat Event for concert promoters and event agencies. Crescat Venue for music venues, conference centers, wedding venues, concert halls and more. And Crescat Festival for festivals, conferences and complex events.
With a wide range of popular features such as event scheduling, shift management, volunteer and crew coordination, artist booking and much more, Crescat is designed for customisation and ease-of-use.
Over 125,000 events have been planned in Crescat and with hundreds of customers of all shapes and sizes, from boutique event agencies through to international concert promoters, Crescat is rigged for success. What's more, we highly value feedback from our users and we are constantly improving our software with updates, new features and improvements.
If you plan events, run a venue or produce festivals and you're looking for ways to make your life easier, then we have a solution for you. Try our software for free or schedule a no-obligation demo with one of our product specialists today at crescat.io
OpenMetadata Community Meeting - 5th June 2024OpenMetadata
The OpenMetadata Community Meeting was held on June 5th, 2024. In this meeting, we discussed about the data quality capabilities that are integrated with the Incident Manager, providing a complete solution to handle your data observability needs. Watch the end-to-end demo of the data quality features.
* How to run your own data quality framework
* What is the performance impact of running data quality frameworks
* How to run the test cases in your own ETL pipelines
* How the Incident Manager is integrated
* Get notified with alerts when test cases fail
Watch the meeting recording here - https://www.youtube.com/watch?v=UbNOje0kf6E
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Revolutionizing Visual Effects Mastering AI Face Swaps.pdfUndress Baby
The quest for the best AI face swap solution is marked by an amalgamation of technological prowess and artistic finesse, where cutting-edge algorithms seamlessly replace faces in images or videos with striking realism. Leveraging advanced deep learning techniques, the best AI face swap tools meticulously analyze facial features, lighting conditions, and expressions to execute flawless transformations, ensuring natural-looking results that blur the line between reality and illusion, captivating users with their ingenuity and sophistication.
Web:- https://undressbaby.com/
Hi, my name is Ronen Botzer, and if my shirt didn't give it away already, I'm an engineer at Aerospike. I help to get the clients for dynamic languages to a fully featured state. I wanted to thank Ed and Diana from SouthBay PHP and Claire from Scale Warriors of Silicon Valley for organizing this meetup.
I've worked in a stream of startups since 1999. First as an engineer in several web teams. Next as a data engineer at an ad-network, a social network, and a mobile SDK vendor (Appcelerator), where we were handling larger volumes of data each time. Most recently I was the architect for a mobile parking startup called QuickPay.
I have a hard time staying serious. You probably noticed that the abstract for this talk invoked a pseduo-relationship metaphor. Objects and relationships, get it? Yeah, that gets tired as a joke fairly quickly.
I am guilty of doing pretty much everything I'm going to mention in this talk.
We can expand on it over beers later, but here goes
You fill in girlfriend/boyfriend as you see fit
Ok, fine, there also names like Hibernate for the subdued "let's watch a period piece movie on the couch" metaphor. But in general, your ORM slows down your data access with its code abstractions. It produces suboptimal performance.
Can someone decipher the acronym?
How did we get stuck with applications "needing" an ORM layer above an RDBMS?
An interesting guy who earned degrees in math and chemistry, served as a RAF pilot in WW2, and then worked for IBM as an actual ‘programmer’. He gets his PhD at UMich, and moves to IBM’s San Jose Research Center.
Later renamed SQL due to copyright issues.
A bad decision in hindsight, as during the early 80s Oracle’s SQL proves more popular, and is a reason for Ingres slipping in share of deployments.
A post-Ingres database that includes his insights about system architecture.
Who knows what's so special about Mosaic? Yes! a GUI!
Can someone please translate WORA? Write Once Run Anywhere.
Some software architects quit their day jobs and settle on the profitable business of spreading the Gospel of Patterns. Writing voluminous books they continue to unearth new ways for us all to get more enterprise-y.
It was like Invasion of The Body Snatchers. Everybody you knew in the late 90s had at least one book about design patterns. If you acted like you found those cumbersome or a waste of time they’d point and go <hhhhhhh>
Seriously, if a language is lucky and it had one gorilla show up early (like ActiveRecord along with Ruby-on-Rails) it will have ONLY 4-5 ORMs. Java, PHP? You’re in the dozens of known ORMs. Likely one ORM per-startup that never got published. There are well over 100 published ones
Laravel has Eloquent ORM, and I’ve just seen a twitter shit storm of people critiquing it and threatening Taylor Otwell with statements like "You haven't seen what I've been working on the last four years…Be scared, be very scared". Well, I personally am scared.
Which is one tell-tale sign that such a database is not designed to scale.
An ORM user who is 'non-SQL' is a unicorn. If you see one, grab him by the bald head, rub it, and demand three wishes. :: A leaky abstraction refers to the way implementation details become visible through the abstraction when the operation is more complex (Joel Spolsky).
Schemas are rigid, so developers need to have understanding of SQL, DML, and DDL. You can’t change a schema as easily as an object - you need migrations. Therefore you learn SQL anyway.
For example ‘the best’ ORMs support a fluent style, which basically is pseudo SQL
Something that has to run against some unknown data store. The web applications with the highest loads (Facebook, Twitter, MediaWiki) are not portable.
Shocking fact: RDBMSs do not implement SQL the same way.
Sometimes really good ones like hints for the query optimizer.
When an ORM hides direct access to a database feature, then re-implements on the application-side. Auto increment.
Your app is not just dependent on your database, but also on the DBAL. Now you can have data access and persistence bugs in two places, and you have to worry whether two technologies you depend on will become obsolete.
Show of hands - does anybody think that switching a web application from one database to the next is trivial if you use a DBAL?
ORMs are overhead, see the previous SELECT wrapper example.
Allegedly you don’t need a specialist such as a DBA if you use an ORM. But if you want to figure out why your ORM is generating slow queries you’ll need to get one, or become an expert. At which point you need to hope your ORM makes it easy to switch to a raw query.
Queries fetch individual relationships in a loop when we have a many:one relationships instead of a properly using a join.
The philosophy and behavior of objects and RDBMSs doesn’t match up well. This is a big topic.
Some ORMs like ActiveRecord do address class hierarchies with a ‘type’ column.
Data structures on the application side are not - lists, maps, sets, etc. Complex data types are poorly expressed in RDBMSs.
If you don't know what first, second, third, boyce-codd normal form, etc is, I won't get into it in detail now.
Unless it wants to melt down early and often. Fail whales abound.
WikiMedia example - direct access to MySQL and PostgreSQL
If you’ve been viewing SQL as something ugly you need to abstract away from, hello! Use NoSQL. Your persistence operations become much closer to how your application side objects behave
Stop making up for the inadequacies of your database by manually clustering. Remove sharding logic from your application. Again, faster and leaner application on top of a faster, leaner stack.
* Doesn't hurt to have founders who have been at a big internet company and saw first hand how massive loads bring traditional databases to their knees.
* What design works:
with new hardware new designs can perform better. Always develop with the current and upcoming hardware in mind.
optimize on those hardware technology advances - utilize all cores, make optimal use of memory, use the best disk access patterns (low IOPS bulk reads for example).
voltDB wrote an in-memory column-oriented database with a SQL dialect, but thinks about rotational disks for storage. Redis runs fully in-memory with weak durability (1 second fsync at best for the AOF)
Aerospike utilizes flash as storage and additional memory in a hybrid system with DRAM, because it is the most economical way to scale, while keeping speed advantage and predictable low latency as a feature.
Aerospike is a real distributed database. Clustering was built-in from the very beginning and is core to the operation and performance on the database. It is not an after-the-fact bolted-on feature.
masterless with replication
Smart client connects and learns about the cluster topology. It only needs a single IP address.
records are identified by a RIPEMD-160 digest of the PK. indexes are always 20-bytes.
The client knows the partition map and will seek to write to the master and replica partitions synchronously.
The client knows which partition to read a record from, and knows where the replica is for failover.
Stop writing sharding logic
Scaling in DRAM only is simply not economical.
SSDs are formatted, no filesystem, memory mapped I/O.
Raw device, direct access pattern.
Indexes are kept in DRAM to save on an extra IOP. Enterprise feature: fast restart (shared-memory)
Records ('rows') in sets ('tables') are kept contiguous for efficient bulk reads.
Each time a write happens the record is written to a new block, with the previous one marked for GC. This ensures an even wear on the flash drive.