Learn best practices for taking advantage of Amazon Redshift's columnar technology and parallel processing capabilities to improve your data warehouse performance.
(BDT401) Amazon Redshift Deep Dive: Tuning and Best PracticesAmazon Web Services
Get a look under the covers: Learn tuning best practices for taking advantage of Amazon Redshift's columnar technology and parallel processing capabilities to improve your delivery of queries and improve overall database performance. This session explains how to migrate from existing data warehouses, create an optimized schema, efficiently load data, use work load management, tune your queries, and use Amazon Redshift's interleaved sorting features. Finally, learn how TripAdvisor uses these best practices to give their entire organization access to analytic insights at scale.
by Dhanraj Pondicherry, Sr. Solutions Architecture Manager, AWS
Analyzing big data quickly and efficiently requires a data warehouse optimized to handle and scale for large datasets. Amazon Redshift is a fast, petabyte-scale data warehouse that makes it simple and cost-effective to analyze big data for a fraction of the cost of traditional data warehouses. In this session, we take an in-depth look at data warehousing with Amazon Redshift for big data analytics. We cover best practices to take advantage of Amazon Redshift's columnar technology and parallel processing capabilities to deliver high throughput and query performance. We also discuss how to design optimal schemas, load data efficiently, and use work load management. Level: 300
Best Practices for Data Warehousing with Amazon Redshift | AWS Public Sector ...Amazon Web Services
Get a look under the covers: Learn tuning best practices for taking advantage of Amazon Redshift's columnar technology and parallel processing capabilities to improve your delivery of queries and improve overall database performance. This session explains how to migrate from existing data warehouses, create an optimized schema, efficiently load data, use workload management, tune your queries, and use Amazon Redshift's interleaved sorting features.You’ll then hear from a customer who has leveraged Redshift in their industry and how they have adopted many of the best practices. Learn More: https://aws.amazon.com/government-education/
A quick tour in 16 slides of Amazon's Redshift clustered, massively parallel database.
Find out what differentiates it from the other database products Amazon has, including SimpleDB, DynamoDB and RDS (MySQL, SQL Server and Oracle).
Learn how it stores data on disk in a columnar format and how this relates to performance and interesting compression techniques.
Contrast the difference between Redshift and a MySQL instance and discover how the clustered architecture may help to dramatically reduce query time.
Amazon Redshift é um serviço gerenciado que lhe dá um Data Warehouse, pronto para usar. Você se preocupa com carregar dados e utilizá-lo. Os detalhes de infraestrutura, servidores, replicação, backup são administrados pela AWS.
(BDT401) Amazon Redshift Deep Dive: Tuning and Best PracticesAmazon Web Services
Get a look under the covers: Learn tuning best practices for taking advantage of Amazon Redshift's columnar technology and parallel processing capabilities to improve your delivery of queries and improve overall database performance. This session explains how to migrate from existing data warehouses, create an optimized schema, efficiently load data, use work load management, tune your queries, and use Amazon Redshift's interleaved sorting features. Finally, learn how TripAdvisor uses these best practices to give their entire organization access to analytic insights at scale.
by Dhanraj Pondicherry, Sr. Solutions Architecture Manager, AWS
Analyzing big data quickly and efficiently requires a data warehouse optimized to handle and scale for large datasets. Amazon Redshift is a fast, petabyte-scale data warehouse that makes it simple and cost-effective to analyze big data for a fraction of the cost of traditional data warehouses. In this session, we take an in-depth look at data warehousing with Amazon Redshift for big data analytics. We cover best practices to take advantage of Amazon Redshift's columnar technology and parallel processing capabilities to deliver high throughput and query performance. We also discuss how to design optimal schemas, load data efficiently, and use work load management. Level: 300
Best Practices for Data Warehousing with Amazon Redshift | AWS Public Sector ...Amazon Web Services
Get a look under the covers: Learn tuning best practices for taking advantage of Amazon Redshift's columnar technology and parallel processing capabilities to improve your delivery of queries and improve overall database performance. This session explains how to migrate from existing data warehouses, create an optimized schema, efficiently load data, use workload management, tune your queries, and use Amazon Redshift's interleaved sorting features.You’ll then hear from a customer who has leveraged Redshift in their industry and how they have adopted many of the best practices. Learn More: https://aws.amazon.com/government-education/
A quick tour in 16 slides of Amazon's Redshift clustered, massively parallel database.
Find out what differentiates it from the other database products Amazon has, including SimpleDB, DynamoDB and RDS (MySQL, SQL Server and Oracle).
Learn how it stores data on disk in a columnar format and how this relates to performance and interesting compression techniques.
Contrast the difference between Redshift and a MySQL instance and discover how the clustered architecture may help to dramatically reduce query time.
Amazon Redshift é um serviço gerenciado que lhe dá um Data Warehouse, pronto para usar. Você se preocupa com carregar dados e utilizá-lo. Os detalhes de infraestrutura, servidores, replicação, backup são administrados pela AWS.
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Delta Lake is an open-source storage layer that brings reliability to data lakes. Delta Lake offers ACID transactions, scalable metadata handling, and unifies the streaming and batch data processing. It runs on top of your existing data lake and is fully compatible with Apache Spark APIs.
In this talk, we will cover:
* What data quality problems Delta helps address
* How to convert your existing application to Delta Lake
* How the Delta Lake transaction protocol works internally
* The Delta Lake roadmap for the next few releases
* How to get involved!
Iceberg: a modern table format for big data (Ryan Blue & Parth Brahmbhatt, Netflix)
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Analyzing big data quickly and efficiently requires a data warehouse optimized to handle and scale for large datasets. Amazon Redshift is a fast, petabyte-scale data warehouse that makes it simple and cost-effective to analyze big data for a fraction of the cost of traditional data warehouses. In this session, we take an in-depth look at data warehousing with Amazon Redshift for big data analytics. We cover best practices to take advantage of Amazon Redshift's columnar technology and parallel processing capabilities to deliver high throughput and query performance. We also discuss how to design optimal schemas, load data efficiently, and use workload management.
Analyzing big data quickly and efficiently requires a data warehouse optimized to handle and scale for large datasets. Amazon Redshift is a fast, petabyte-scale data warehouse that makes it simple and cost-effective to analyze big data for a fraction of the cost of traditional data warehouses. By following a few best practices, you can take advantage of Amazon Redshift’s columnar technology and parallel processing capabilities to minimize I/O and deliver high throughput and query performance. This webinar will cover techniques to load data efficiently, design optimal schemas, and use work load management.
Learning Objectives:
• Get an inside look at Amazon Redshift's columnar technology and parallel processing capabilities
• Learn how to migrate from existing data warehouses, optimize schemas, and load data efficiently
• Learn best practices for managing workload, tuning your queries, and using Amazon Redshift's interleaved sorting features
Who Should Attend:
• Data Warehouse Developers, Big Data Architects, BI Managers, and Data Engineers
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As part of the Tungsten project, Spark has started an ongoing effort to dramatically improve performance to bring the execution closer to bare metal. In this talk, we’ll go over the progress that has been made so far and the areas we’re looking to invest in next. This talk will discuss the architectural changes that are being made as well as some discussion into how Spark users can expect their application to benefit from this effort. The focus of the talk will be on Spark SQL but the improvements are general and applicable to multiple Spark technologies.
Parquet performance tuning: the missing guideRyan Blue
Ryan Blue explains how Netflix is building on Parquet to enhance its 40+ petabyte warehouse, combining Parquet’s features with Presto and Spark to boost ETL and interactive queries. Information about tuning Parquet is hard to find. Ryan shares what he’s learned, creating the missing guide you need.
Topics include:
* The tools and techniques Netflix uses to analyze Parquet tables
* How to spot common problems
* Recommendations for Parquet configuration settings to get the best performance out of your processing platform
* The impact of this work in speeding up applications like Netflix’s telemetry service and A/B testing platform
Take an in-depth look at data warehousing with Amazon Redshift and get answers to your technical questions. We will cover performance tuning techniques that take advantage of Amazon Redshift's columnar technology and massively parallel processing architecture. We will also discuss best practices for migrating from existing data warehouses, optimizing your schema, loading data efficiently, and using work load management and interleaved sorting.
Spark + Parquet In Depth: Spark Summit East Talk by Emily Curtin and Robbie S...Spark Summit
What if you could get the simplicity, convenience, interoperability, and storage niceties of an old-fashioned CSV with the speed of a NoSQL database and the storage requirements of a gzipped file? Enter Parquet.
At The Weather Company, Parquet files are a quietly awesome and deeply integral part of our Spark-driven analytics workflow. Using Spark + Parquet, we’ve built a blazing fast, storage-efficient, query-efficient data lake and a suite of tools to accompany it.
We will give a technical overview of how Parquet works and how recent improvements from Tungsten enable SparkSQL to take advantage of this design to provide fast queries by overcoming two major bottlenecks of distributed analytics: communication costs (IO bound) and data decoding (CPU bound).
Migrating Apache Hive Workload to Apache Spark: Bridge the Gap with Zhan Zhan...Databricks
At Spark Summit 2017, we described our framework to migrate production Hive workload to Spark with minimal user intervention. After a year of migration, Spark now powers an important part of our batch processing workload. The migration framework supports syntax compatibility analysis, offline/online shadowing, and data validation.
In this session, we first introduce new features and improvements in the migration framework to support bucketed tables and increase automation. Next, we will deep dive into the top technical challenges we encountered and how we addressed them. We improved the the syntax compatibility between Hive and Spark from around 51% to 85% by identifying/developing top missing features, fixing incompatible UDFs, and implementing a UDF testing framework. In addition, we developed reliable join operators to improve Spark stability in production when leveraging optimizations such as ShuffledHashJoin.
Finally, we will share an update on our overall migration effort and examples of migrations wins. For example, we were able to migrate one of the most complicated workloads in Facebook from Hive to Spark with more than 2.5X performance gain.
Simplify CDC Pipeline with Spark Streaming SQL and Delta LakeDatabricks
Change Data Capture CDC is a typical use case in Real-Time Data Warehousing. It tracks the data change log -binlog- of a relational database [OLTP], and replay these change log timely to an external storage to do Real-Time OLAP, such as delta/kudu. To implement a robust CDC streaming pipeline, lots of factors should be concerned, such as how to ensure data accuracy , how to process OLTP source schema changed, whether it is easy to build for variety databases with less code.
Deep Dive into the New Features of Apache Spark 3.0Databricks
Continuing with the objectives to make Spark faster, easier, and smarter, Apache Spark 3.0 extends its scope with more than 3000 resolved JIRAs. We will talk about the exciting new developments in the Spark 3.0 as well as some other major initiatives that are coming in the future.
Redshift at Lightspeed: How to continuously optimize and modify Redshift sche...Amazon Web Services
We live in an era of rapid dev cycles and continuous deployment, where the code we commit is instantly tested and deployed. Yet, maintaining data warehouse schemas remains a cumbersome, manual task. Redshift is an extremely powerful warehouse, and with little fine tuning it can adapt to the pace of daily changes to the code, data and query patterns by evolving and restructuring table schemas. In this talk we will present a methodology for identifying query bottlenecks and under-optimized configurations by reviewing actual explain plans. Then, we will discuss several techniques for schema settings modification, including data types, sortkeys and distribution keys, that are robust, continuous and without downtime.
Delta from a Data Engineer's PerspectiveDatabricks
Take a walk through the daily struggles of a data engineer in this presentation as we cover what is truly needed to create robust end to end Big Data solutions.
Join operations in Apache Spark is often the biggest source of performance problems and even full-blown exceptions in Spark. After this talk, you will understand the two most basic methods Spark employs for joining DataFrames – to the level of detail of how Spark distributes the data within the cluster. You’ll also find out how to work out common errors and even handle the trickiest corner cases we’ve encountered! After this talk, you should be able to write performance joins in Spark SQL that scale and are zippy fast!
This session will cover different ways of joining tables in Apache Spark.
Speaker: Vida Ha
This talk was originally presented at Spark Summit East 2017.
Making Apache Spark Better with Delta LakeDatabricks
Delta Lake is an open-source storage layer that brings reliability to data lakes. Delta Lake offers ACID transactions, scalable metadata handling, and unifies the streaming and batch data processing. It runs on top of your existing data lake and is fully compatible with Apache Spark APIs.
In this talk, we will cover:
* What data quality problems Delta helps address
* How to convert your existing application to Delta Lake
* How the Delta Lake transaction protocol works internally
* The Delta Lake roadmap for the next few releases
* How to get involved!
Iceberg: a modern table format for big data (Ryan Blue & Parth Brahmbhatt, Netflix)
Presto Summit 2018 (https://www.starburstdata.com/technical-blog/presto-summit-2018-recap/)
Analyzing big data quickly and efficiently requires a data warehouse optimized to handle and scale for large datasets. Amazon Redshift is a fast, petabyte-scale data warehouse that makes it simple and cost-effective to analyze big data for a fraction of the cost of traditional data warehouses. In this session, we take an in-depth look at data warehousing with Amazon Redshift for big data analytics. We cover best practices to take advantage of Amazon Redshift's columnar technology and parallel processing capabilities to deliver high throughput and query performance. We also discuss how to design optimal schemas, load data efficiently, and use workload management.
Analyzing big data quickly and efficiently requires a data warehouse optimized to handle and scale for large datasets. Amazon Redshift is a fast, petabyte-scale data warehouse that makes it simple and cost-effective to analyze big data for a fraction of the cost of traditional data warehouses. By following a few best practices, you can take advantage of Amazon Redshift’s columnar technology and parallel processing capabilities to minimize I/O and deliver high throughput and query performance. This webinar will cover techniques to load data efficiently, design optimal schemas, and use work load management.
Learning Objectives:
• Get an inside look at Amazon Redshift's columnar technology and parallel processing capabilities
• Learn how to migrate from existing data warehouses, optimize schemas, and load data efficiently
• Learn best practices for managing workload, tuning your queries, and using Amazon Redshift's interleaved sorting features
Who Should Attend:
• Data Warehouse Developers, Big Data Architects, BI Managers, and Data Engineers
Project Tungsten: Bringing Spark Closer to Bare MetalDatabricks
As part of the Tungsten project, Spark has started an ongoing effort to dramatically improve performance to bring the execution closer to bare metal. In this talk, we’ll go over the progress that has been made so far and the areas we’re looking to invest in next. This talk will discuss the architectural changes that are being made as well as some discussion into how Spark users can expect their application to benefit from this effort. The focus of the talk will be on Spark SQL but the improvements are general and applicable to multiple Spark technologies.
Parquet performance tuning: the missing guideRyan Blue
Ryan Blue explains how Netflix is building on Parquet to enhance its 40+ petabyte warehouse, combining Parquet’s features with Presto and Spark to boost ETL and interactive queries. Information about tuning Parquet is hard to find. Ryan shares what he’s learned, creating the missing guide you need.
Topics include:
* The tools and techniques Netflix uses to analyze Parquet tables
* How to spot common problems
* Recommendations for Parquet configuration settings to get the best performance out of your processing platform
* The impact of this work in speeding up applications like Netflix’s telemetry service and A/B testing platform
Take an in-depth look at data warehousing with Amazon Redshift and get answers to your technical questions. We will cover performance tuning techniques that take advantage of Amazon Redshift's columnar technology and massively parallel processing architecture. We will also discuss best practices for migrating from existing data warehouses, optimizing your schema, loading data efficiently, and using work load management and interleaved sorting.
Spark + Parquet In Depth: Spark Summit East Talk by Emily Curtin and Robbie S...Spark Summit
What if you could get the simplicity, convenience, interoperability, and storage niceties of an old-fashioned CSV with the speed of a NoSQL database and the storage requirements of a gzipped file? Enter Parquet.
At The Weather Company, Parquet files are a quietly awesome and deeply integral part of our Spark-driven analytics workflow. Using Spark + Parquet, we’ve built a blazing fast, storage-efficient, query-efficient data lake and a suite of tools to accompany it.
We will give a technical overview of how Parquet works and how recent improvements from Tungsten enable SparkSQL to take advantage of this design to provide fast queries by overcoming two major bottlenecks of distributed analytics: communication costs (IO bound) and data decoding (CPU bound).
Migrating Apache Hive Workload to Apache Spark: Bridge the Gap with Zhan Zhan...Databricks
At Spark Summit 2017, we described our framework to migrate production Hive workload to Spark with minimal user intervention. After a year of migration, Spark now powers an important part of our batch processing workload. The migration framework supports syntax compatibility analysis, offline/online shadowing, and data validation.
In this session, we first introduce new features and improvements in the migration framework to support bucketed tables and increase automation. Next, we will deep dive into the top technical challenges we encountered and how we addressed them. We improved the the syntax compatibility between Hive and Spark from around 51% to 85% by identifying/developing top missing features, fixing incompatible UDFs, and implementing a UDF testing framework. In addition, we developed reliable join operators to improve Spark stability in production when leveraging optimizations such as ShuffledHashJoin.
Finally, we will share an update on our overall migration effort and examples of migrations wins. For example, we were able to migrate one of the most complicated workloads in Facebook from Hive to Spark with more than 2.5X performance gain.
Simplify CDC Pipeline with Spark Streaming SQL and Delta LakeDatabricks
Change Data Capture CDC is a typical use case in Real-Time Data Warehousing. It tracks the data change log -binlog- of a relational database [OLTP], and replay these change log timely to an external storage to do Real-Time OLAP, such as delta/kudu. To implement a robust CDC streaming pipeline, lots of factors should be concerned, such as how to ensure data accuracy , how to process OLTP source schema changed, whether it is easy to build for variety databases with less code.
Deep Dive into the New Features of Apache Spark 3.0Databricks
Continuing with the objectives to make Spark faster, easier, and smarter, Apache Spark 3.0 extends its scope with more than 3000 resolved JIRAs. We will talk about the exciting new developments in the Spark 3.0 as well as some other major initiatives that are coming in the future.
Redshift at Lightspeed: How to continuously optimize and modify Redshift sche...Amazon Web Services
We live in an era of rapid dev cycles and continuous deployment, where the code we commit is instantly tested and deployed. Yet, maintaining data warehouse schemas remains a cumbersome, manual task. Redshift is an extremely powerful warehouse, and with little fine tuning it can adapt to the pace of daily changes to the code, data and query patterns by evolving and restructuring table schemas. In this talk we will present a methodology for identifying query bottlenecks and under-optimized configurations by reviewing actual explain plans. Then, we will discuss several techniques for schema settings modification, including data types, sortkeys and distribution keys, that are robust, continuous and without downtime.
Delta from a Data Engineer's PerspectiveDatabricks
Take a walk through the daily struggles of a data engineer in this presentation as we cover what is truly needed to create robust end to end Big Data solutions.
Join operations in Apache Spark is often the biggest source of performance problems and even full-blown exceptions in Spark. After this talk, you will understand the two most basic methods Spark employs for joining DataFrames – to the level of detail of how Spark distributes the data within the cluster. You’ll also find out how to work out common errors and even handle the trickiest corner cases we’ve encountered! After this talk, you should be able to write performance joins in Spark SQL that scale and are zippy fast!
This session will cover different ways of joining tables in Apache Spark.
Speaker: Vida Ha
This talk was originally presented at Spark Summit East 2017.
In this performance-oriented session, we will cover tuning techniques that take advantage of Amazon Redshift's columnar technology and massively parallel processing architecture. We will also discuss best practices for migrating from existing data warehouses, optimizing your schema, loading data efficiently, and using work load management and interleaved sorting.
Optimizing Query is very important to improve the performance of the database. Analyse query using query execution plan, create cluster index and non-cluster index and create indexed views
AWS June 2016 Webinar Series - Amazon Redshift or Big Data AnalyticsAmazon Web Services
Analyzing big data quickly and efficiently requires a data warehouse optimized to handle and scale for large datasets. Amazon Redshift is a fast, petabyte-scale data warehouse that makes it simple and cost-effective to analyze big data for a fraction of the cost of traditional data warehouses. By following a few best practices, you can take advantage of Amazon Redshift’s columnar technology and parallel processing capabilities to minimize I/O and deliver high throughput and query performance. This webinar will cover techniques to load data efficiently, design optimal schemas, and tune query and database performance.
Learning Objectives:
Get an inside look at Amazon Redshift's columnar technology and parallel processing capabilities
Learn how to migrate from existing data warehouses, optimize schemas, and load data efficiently
Learn best practices for managing workload, tuning your queries, and using Amazon Redshift's interleaved sorting features
MemSQL 201: Advanced Tips and Tricks WebcastSingleStore
Topics discussed include differences between columnstore and rowstore engines, data ingestion, data sharding and query tuning, lastly memory and workload management.
Watch the replay at https://memsql.wistia.com/medias/4siccvlorm
Amazon Athena is a new serverless query service that makes it easy to analyze data in Amazon S3, using standard SQL. With Athena, there is no infrastructure to setup or manage, and you can start analyzing your data immediately. You don’t even need to load your data into Athena, it works directly with data stored in S3.
Geek Sync I Need for Speed: In-Memory Databases in Oracle and SQL ServerIDERA Software
You can watch the replay for this Geek Sync webcast in the IDERA Resource Center: http://ow.ly/S6MG50A5ok5
Microsoft introduced IN-MEMORY OLTP, widely referred to as “Hekaton” in SQL Server 2014. Hekaton allows for the creation of fully transactionally consistent memory-resident tables designed for high concurrency and no blocking. With SQL 2016, many of the original restrictions and limitations of this feature have been reduced. IDERA’s Vicky Harp will give an overview of this feature, including how to compile T-SQL code into machine code for an even greater performance boost.
There’s also been a lot of buzz about Oracle 12c’s new IN-MEMORY COLUMN STORE. Oracle ACE Bert Scalzo will cover this new feature, how it works, it’s benefits, scripts to measure/monitor it and more. He will also touch on performance observations from benchmarking this new feature against more traditional SGA memory allocations plus Oracle 11g R2’s Database Smart Flash Cache. All findings, scripts and conclusions from this exercise will be shared. In addition, two very popular database benchmarking tools will be highlighted.
Take an in-depth look at data warehousing with Amazon Redshift and get answers to your technical questions. We will cover performance tuning techniques that take advantage of Amazon Redshift's columnar technology and massively parallel processing architecture. We will also discuss best practices for migrating from existing data warehouses, optimizing your schema, loading data efficiently, and using work load management and interleaved sorting.
Managing user Online Training in IBM Netezza DBA Development by www.etraining...Ravikumar Nandigam
Dear Student,
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Number of Hours: 24 hours
*Please note the course also includes Netezza certification assitance.
If there is any opportunity, we will be very happy to serve you. Appreciate if you can explore other training opportunities in our website as well.
We can be reachable at info@etraining.guru (or) 91-996-669-2446 for any further info/details.
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www.etraining.guru"
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Vuoi conoscere le opzioni per eseguire Microsoft Active Directory su AWS? Quando si spostano carichi di lavoro Microsoft in AWS, è importante considerare come distribuire Microsoft Active Directory per supportare la gestione, l'autenticazione e l'autorizzazione dei criteri di gruppo. In questa sessione, discuteremo le opzioni per la distribuzione di Microsoft Active Directory su AWS, incluso AWS Directory Service per Microsoft Active Directory e la distribuzione di Active Directory su Windows su Amazon Elastic Compute Cloud (Amazon EC2). Trattiamo argomenti quali l'integrazione del tuo ambiente Microsoft Active Directory locale nel cloud e l'utilizzo di applicazioni SaaS, come Office 365, con AWS Single Sign-On.
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Amazon Web Services e VMware organizzano un evento virtuale gratuito il prossimo mercoledì 14 Ottobre dalle 12:00 alle 13:00 dedicato a VMware Cloud ™ on AWS, il servizio on demand che consente di eseguire applicazioni in ambienti cloud basati su VMware vSphere® e di accedere ad una vasta gamma di servizi AWS, sfruttando a pieno le potenzialità del cloud AWS e tutelando gli investimenti VMware esistenti.
Molte organizzazioni sfruttano i vantaggi del cloud migrando i propri carichi di lavoro Oracle e assicurandosi notevoli vantaggi in termini di agilità ed efficienza dei costi.
La migrazione di questi carichi di lavoro, può creare complessità durante la modernizzazione e il refactoring delle applicazioni e a questo si possono aggiungere rischi di prestazione che possono essere introdotti quando si spostano le applicazioni dai data center locali.
Crea la tua prima serverless ledger-based app con QLDB e NodeJSAmazon Web Services
Molte aziende oggi, costruiscono applicazioni con funzionalità di tipo ledger ad esempio per verificare lo storico di accrediti o addebiti nelle transazioni bancarie o ancora per tenere traccia del flusso supply chain dei propri prodotti.
Alla base di queste soluzioni ci sono i database ledger che permettono di avere un log delle transazioni trasparente, immutabile e crittograficamente verificabile, ma sono strumenti complessi e onerosi da gestire.
Amazon QLDB elimina la necessità di costruire sistemi personalizzati e complessi fornendo un database ledger serverless completamente gestito.
In questa sessione scopriremo come realizzare un'applicazione serverless completa che utilizzi le funzionalità di QLDB.
Con l’ascesa delle architetture di microservizi e delle ricche applicazioni mobili e Web, le API sono più importanti che mai per offrire agli utenti finali una user experience eccezionale. In questa sessione impareremo come affrontare le moderne sfide di progettazione delle API con GraphQL, un linguaggio di query API open source utilizzato da Facebook, Amazon e altro e come utilizzare AWS AppSync, un servizio GraphQL serverless gestito su AWS. Approfondiremo diversi scenari, comprendendo come AppSync può aiutare a risolvere questi casi d’uso creando API moderne con funzionalità di aggiornamento dati in tempo reale e offline.
Inoltre, impareremo come Sky Italia utilizza AWS AppSync per fornire aggiornamenti sportivi in tempo reale agli utenti del proprio portale web.
Database Oracle e VMware Cloud™ on AWS: i miti da sfatareAmazon Web Services
Molte organizzazioni sfruttano i vantaggi del cloud migrando i propri carichi di lavoro Oracle e assicurandosi notevoli vantaggi in termini di agilità ed efficienza dei costi.
La migrazione di questi carichi di lavoro, può creare complessità durante la modernizzazione e il refactoring delle applicazioni e a questo si possono aggiungere rischi di prestazione che possono essere introdotti quando si spostano le applicazioni dai data center locali.
In queste slide, gli esperti AWS e VMware presentano semplici e pratici accorgimenti per facilitare e semplificare la migrazione dei carichi di lavoro Oracle accelerando la trasformazione verso il cloud, approfondiranno l’architettura e dimostreranno come sfruttare a pieno le potenzialità di VMware Cloud ™ on AWS.
Amazon Elastic Container Service (Amazon ECS) è un servizio di gestione dei container altamente scalabile, che semplifica la gestione dei contenitori Docker attraverso un layer di orchestrazione per il controllo del deployment e del relativo lifecycle. In questa sessione presenteremo le principali caratteristiche del servizio, le architetture di riferimento per i differenti carichi di lavoro e i semplici passi necessari per poter velocemente migrare uno o più dei tuo container.
This presentation by Morris Kleiner (University of Minnesota), was made during the discussion “Competition and Regulation in Professions and Occupations” held at the Working Party No. 2 on Competition and Regulation on 10 June 2024. More papers and presentations on the topic can be found out at oe.cd/crps.
This presentation was uploaded with the author’s consent.
0x01 - Newton's Third Law: Static vs. Dynamic AbusersOWASP Beja
f you offer a service on the web, odds are that someone will abuse it. Be it an API, a SaaS, a PaaS, or even a static website, someone somewhere will try to figure out a way to use it to their own needs. In this talk we'll compare measures that are effective against static attackers and how to battle a dynamic attacker who adapts to your counter-measures.
About the Speaker
===============
Diogo Sousa, Engineering Manager @ Canonical
An opinionated individual with an interest in cryptography and its intersection with secure software development.
Acorn Recovery: Restore IT infra within minutesIP ServerOne
Introducing Acorn Recovery as a Service, a simple, fast, and secure managed disaster recovery (DRaaS) by IP ServerOne. A DR solution that helps restore your IT infra within minutes.
Sharpen existing tools or get a new toolbox? Contemporary cluster initiatives...Orkestra
UIIN Conference, Madrid, 27-29 May 2024
James Wilson, Orkestra and Deusto Business School
Emily Wise, Lund University
Madeline Smith, The Glasgow School of Art
Have you ever wondered how search works while visiting an e-commerce site, internal website, or searching through other types of online resources? Look no further than this informative session on the ways that taxonomies help end-users navigate the internet! Hear from taxonomists and other information professionals who have first-hand experience creating and working with taxonomies that aid in navigation, search, and discovery across a range of disciplines.
4. Relational data warehouse
Massively parallel; petabyte scale
Fully managed
HDD and SSD platforms
$1,000/TB/year; starts at $0.25/hour
Amazon
Redshift
a lot faster
a lot simpler
a lot cheaper
6. Amazon Redshift system architecture
Leader node
• SQL endpoint
• Stores metadata
• Coordinates query execution
Compute nodes
• Local, columnar storage
• Executes queries in parallel
• Load, backup, restore via
Amazon S3; load from
Amazon DynamoDB, Amazon EMR, or SSH
Two hardware platforms
• Optimized for data processing
• DS2: HDD; scale from 2 TB to 2 PB
• DC1: SSD; scale from 160 GB to 326 TB
10 GigE
(HPC)
Ingestion
Backup
Restore
JDBC/ODBC
7. A deeper look at compute node architecture
Each node contains multiple slices
• DS2 – 2 slices on XL, 16 on 8 XL
• DC1 – 2 slices on L, 32 on 8 XL
Each slice is allocated CPU and
table data
Each slice processes a piece of
the workload in parallel
Leader Node
9. Issue #1: Incorrect column encoding
• Amazon Redshift is a column-oriented database
• well suited to analytics queries on tables with a large
number of columns
• Only able to access those blocks on disk that are for
columns included in the SELECT or WHERE clause, and
doesn’t have to read all table data to evaluate a query
• Data stored by column should also be encoded
• Clusters without column encoding is not considered a
best practice
10. Solution #1: the v_extended_table_info view
• To determine if you are deviating from this best practice,
you can use the v_extended_table_info view from the
Amazon Redshift Utils GitHub repository
• Once view is created, run the following view:
• SELECT database, tablename, columns FROM
admin.v_extended_table_info ORDER BY database;
11. • Afterward, review the tables and columns which aren’t
encoded by running the following query:
SELECT trim(n.nspname || '.' || c.relname) AS "table",
trim(a.attname) AS "column",
format_type(a.atttypid, a.atttypmod) AS "type",
format_encoding(a.attencodingtype::integer) AS "encoding",
a.attsortkeyord AS "sortkey"
FROM pg_namespace n, pg_class c, pg_attribute a
WHERE n.oid = c.relnamespace
AND c.oid = a.attrelid
AND a.attnum > 0
AND NOT a.attisdropped and n.nspname NOT IN ('information_schema','pg_catalog','pg_toast')
AND format_encoding(a.attencodingtype::integer) = 'none'
AND c.relkind='r'
AND a.attsortkeyord != 1
ORDER BY n.nspname, c.relname, a.attnum;
Solution #1: the v_extended_table_info view
12. • If you find that you have tables without optimal column
encoding, then use the Amazon Redshift Column
Encoding Utility on AWS Labs GitHub to apply encoding.
• This utility uses the ANALYZE COMPRESSION
command on each table.
• If encoding is required, it generates a SQL script which
creates a new table with the correct encoding, copies all
the data into the new table, and then transactionally
renames the new table to the old name while retaining
the original data.
Solution #1: Redshift Column Encoding Utility
13. Issue #2 – Skewed table data
• Redshift nodes are managed by the number of slices
(CPUs) per node
• When a table is created, you decide whether to spread
the data evenly among slices (default), or assign data to
specific slices on the basis of one of the columns.
• By choosing columns for distribution that are commonly
joined together, you can minimize the amount of data
transferred over the network during the join.
• This can significantly increase performance on these
types of queries.
14. • The selection of a good distribution key is the topic of
many AWS articles, including Choose the Best
Distribution Style; see a definitive guide to distribution
and sorting of star schemas in the Optimizing for Star
Schemas and Interleaved Sorting on Amazon Redshift
blog post.
• A skewed distribution key results in slices not working
equally hard as each other during query execution,
requiring unbalanced CPU or memory, and ultimately
only running as fast as the slowest slice:
Issue #2 – Skewed table data
15. Issue #2 – Skewed table data
If skewing is an issue:
• Use one of the admin
scripts in the
Amazon Redshift
Utils GitHub
repository, such as
table_inspector.sql,
to see how data
blocks in a
distribution key map
to the slices and
nodes in the cluster.
16. Solution #2: Use Correct Distribution Key
• If you find that you have tables with skewed distribution
keys, then consider changing the distribution key to a
column that exhibits high cardinality and uniform
distribution.
• Evaluate a candidate column as a distribution key by
creating a new table using CTAS:
CREATE TABLE my_test_table DISTKEY (<column name>) AS SELECT <column name> FROM <table name>;
• Run the table_inspector.sql script against the table again
to analyze data skew.
17. • If there is no good distribution key in any of your records,
you may find that moving to EVEN distribution works
better.
• For small tables (for example, dimension tables with a
couple of million rows), you can also use DISTSTYLE
ALL to place table data onto every node in the cluster.
Solution #2: Use Correct Distribution Key
18. Issue #3 – Queries not benefiting from sort keys
• Sort keys acts like an index in other databases, but
which does not incur a storage cost as with other
platforms. (for more information, see Choosing Sort Keys)
• To determine which tables don’t have sort keys, run the
following query against the v_extended_table_info view
from the Amazon Redshift Utils repository:
SELECT * FROM admin.v_extended_table_info WHERE sortkey IS null
19. Solution #3: Use Sort Key on Columns used in
Where Clause
• A sort key should be created on those columns which
are most commonly used in WHERE clauses.
• If you have a known query pattern, then COMPOUND sort
keys give the best performance;
• If using compound sort keys, review your queries to ensure
that their WHERE clauses specify the sort columns in the
same order they were defined in the compound key.
• if end users query different columns equally, then use an
INTERLEAVED sort key.
20. Solution #3: Run Sort Key Recommendation
Query
• You can run a tutorial that walks you through how to address unsorted
tables in the Amazon Redshift Developer Guide.
• You can also run the following query to generate a list of recommended sort
keys based on query activity:
http://tinyurl.com/redshift-sortkey-recommender
• Queries evaluated against a sort key column must not apply a SQL function
to the sort key; instead, ensure that you apply the functions to the
compared values so that the sort key is used. This is commonly found on
TIMESTAMP columns that are used as sort keys.
21. Issue #4 – Tables without statistics or which
need vacuum
• Bad Stats: Amazon Redshift, like other databases, requires statistics
about tables and the composition of data blocks being stored in
order to make good decisions when planning a query (for more
information, see Analyzing Tables).
• Stale Data: When rows are DELETED or UPDATED, they are
simply logically deleted (flagged for deletion) but not physically
removed from disk. Updates result in a new block being written with
new data appended. As a result, table storage space is increased
and performance degraded
22. Solution #4: missing_table_stats.sql admin script
• The ANALYZE Command History topic in the Amazon
Redshift Developer Guide supplies queries to help you
address missing or stale statistics
• Or, you can also simply run the missing_table_stats.sql
admin script to determine which tables are missing stats,
or the statement below to determine tables that have
stale statistics:
SELECT database, schema || '.' || "table" AS "table", stats_off
FROM svv_table_info
WHERE stats_off > 5
ORDER BY 2;
23. • You can use the perf_alert.sql admin script to identify
tables for which alerts about scanning a large number of
deleted rows have been raised in the last seven days.
• To address issues with tables with missing or stale
statistics or where vacuum is required, run another AWS
Labs utility, Analyze & Vacuum Schema. This ensures
that you always keep up-to-date statistics, and only
vacuum tables that actually need re-organisation.
Solution #4: perf_alert.sql admin script
24. Issue #5 – Tables with very large VARCHAR columns
During processing of complex queries, intermediate query
results can be stored in temporary uncompressed blocks,
consuming excessive memory and temporary disk space,
affecting query performance. For more information, see
Use the Smallest Possible Column Size.
25. Solution #5 – Inspect and Deep Copy to Optimized Table
Use the following query to generate a list of tables that should have their
maximum column widths reviewed:
SELECT database, schema || '.' || "table" AS "table", max_varchar
FROM svv_table_info
WHERE max_varchar > 150
ORDER BY 2
After you have a list of tables, identify which table columns have wide varchar
columns and then determine the true maximum width for each wide column,
using the following query:
SELECT max(len(rtrim(column_name)))
FROM table_name;
If you find that the table has columns that are wider than necessary, then you
need to re-create a version of the table with appropriate column widths by
performing a deep copy.
26. Solution #5 – Working with JSON Columns
• In some cases, you may have large VARCHAR type
columns because you are storing JSON fragments in the
table, which you then query with JSON functions.
• Pay special attention to SELECT * queries which include
the JSON fragment column, from the top_queries.sql
admin script.
• If end users query these large JSON columns but don’t
execute JSON functions against them, consider moving them
into another table that only contains the primary key column
of the original table and the JSON column.
27. Issue #6 - Queries waiting on queue slots
Amazon Redshift runs queries using a queuing system
known as workload management (WLM), allowing up to 8
separate workloads.
In some cases, the queue to which a user or query has
been assigned is completely busy and a user’s query must
wait for a slot to be open. During this time, the system is
not executing the query at all, which is a sign that you may
need to increase concurrency.
28. Solution #6 – Assess Queuing History and configure
WLM
• determine if any queries are queuing, using the
queuing_queries.sql admin script.
• Review the maximum concurrency that your cluster has
needed in the past with wlm_apex.sql,
• down to an hour-by-hour historical analysis with
wlm_apex_hourly.sql.
• Modify WLM to optimal queuing configuration based on
history stats
29. Solution #6 – Configure WLM
-Note: while increasing concurrency allows more queries to
run, each query will get a smaller share of the memory
allocated to its queue (unless you increase it).
You may find that by increasing concurrency, some queries
must use temporary disk storage to complete, which is also
sub-optimal
30. Issue #7 - Queries that are disk-based
• If a query isn’t able to completely execute in memory, it
may need to use disk-based temporary storage for parts
of an explain plan.
• The additional disk I/O slows down the query, and can
be addressed by increasing the amount of memory
allocated to a session (for more information, see WLM
Dynamic Memory Allocation).
31. Solution #7 – Disk Write Check and Configure
• To determine if any queries have been writing to disk,
use the following query: http://tinyurl.com/redshift-
diskquery
• Based on the user or the queue assignment rules, you
can increase the amount of memory given to the
selected queue to prevent queries needing to spill to
disk to complete.
• You can also increase the
WLM_QUERY_SLOT_COUNT …use with care!
32. Issue #8 – Commit Queue Waits
• Amazon Redshift is designed for analytics queries,
rather than transaction processing.
• The cost of COMMIT is relatively high, and excessive
use of COMMIT can result in queries waiting for access
to a commit queue.
33. Solution 8: Analyze Commit Workloads
• If you are committing too often on your database, you
will start to see waits on the commit queue increase,
• Use the commit_stats.sql admin script to show the
largest queue length and queue time for queries run in
the past two days.
• If you have queries that are waiting on the commit
queue, then look for sessions that are committing
multiple times per session, such as ETL jobs that are
logging progress or inefficient data loads.
34. Issue #9 - Inefficient data loads
• Anti-Pattern: Insert data directly into Amazon Redshift,
with single record inserts or the use of a multi-value
INSERT statement,
• These INSERTs allow up to a 16 MB ingest of data at
one time.
• These are leader node–based operations, and can
create significant performance bottlenecks by maxing
out the leader node network as data is distributed by the
leader to the compute nodes.
35. Solution #9 – Use COPY cmd, and Compression
• Amazon Redshift best practices suggest the use of the
COPY command to perform data loads. This API
operation uses all compute nodes in the cluster to load
data in parallel.
• Compress the files to be loaded whenever possible;
Amazon Redshift supports both GZIP and LZO
compression.
36. Solution #9 – Use COPY cmd, and Compression
• It is more efficient to load a large number of small files
than one large one, and the ideal file count is a multiple
of the slice count.
• The number of slices per node depends on the node
size of the cluster.
• By ensuring you have an equal number of files per slice,
you can know that COPY execution will evenly use
cluster resources and complete as quickly as possible.
37. Solution #9 – Table Load Statistics Scripts
The following query calculates statistics for each load:
http://preview.tinyurl.com/redshift-ingest-load-stats
The following query shows the time taken to load a table,
and the time taken to update the table statistics, both in
seconds and as a percentage of the overall load process:
http://tinyurl.com/redshift-tableload-stat-time
38. Issue #10 - Inefficient use of Temporary Tables
Overview of Temp Tables:
• Amazon Redshift provides temporary tables, which are like normal
tables except that they are only visible within a single session.
• When the user disconnects the session, the tables are automatically
deleted.
• Temporary tables can be created using the CREATE TEMPORARY
TABLE syntax, or by issuing a SELECT … INTO #TEMP_TABLE
query.
• The CREATE TABLE statement gives you complete control over the
definition of the temporary table, while the SELECT … INTO and
C(T)TAS commands use the input data to determine column names,
sizes and data types, and use default storage properties.
39. • These default storage properties may cause issues if not
carefully considered.
• Amazon Redshift’s default table structure is to use
EVEN distribution with no column encoding.
• This is a sub-optimal data structure for many types of
queries, and if you are using SELECT…INTO syntax you
cannot set the column encoding or distribution and sort
keys.
• If you use the CREATE TABLE AS (CTAS) syntax, you
can specify a distribution style and sort keys, but you still
can’t set the column encodings.
Issue #10 - Inefficient use of Temporary Tables
40. Solution #10 – Apply Source Table configurations to Target Temp Table
If you are creating temporary tables, it is highly
recommended that you convert all SELECT…INTO syntax
to use the CREATE statement. This ensures that your
temporary tables have column encodings and are
distributed in a fashion that is sympathetic to the other
entities that are part of the workflow.
41. If you typically do this:
SELECT column_a, column_b INTO #my_temp_table FROM my_table;
You would analyze the temporary table for optimal column
encoding:
Solution #10 – Apply Source Table configurations to Target Temp Table
42. And then convert the select/into statement to:
BEGIN;
CREATE TEMPORARY TABLE my_temp_table(
column_a varchar(128) encode lzo,
column_b char(4) encode bytedict)
distkey (column_a) -- Assuming you intend to join this table on column_a
sortkey (column_b); -- Assuming you are sorting or grouping by column_b
INSERT INTO my_temp_table SELECT column_a, column_b FROM my_table;
COMMIT;
You may also wish to analyze statistics on the temporary
table, if it is used as a join table for subsequent queries:
ANALYZE my_temp_table;
Solution #10 – Apply Source Table configurations to Target Temp Table
43. Solution #11 - Using explain plan alerts
• The last tip is to use diagnostic information from the
cluster during query execution. This is stored in an
extremely useful view called STL_ALERT_EVENT_LOG.
• Use the perf_alert.sql admin script to diagnose issues
that the cluster has encountered over the last seven
days. This is an invaluable resource in understanding
how your cluster develops over time.