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.
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.
In this presentation, you will get a look under the covers of Amazon Redshift, a fast, fully-managed, petabyte-scale data warehouse service for less than $1,000 per TB per year. Learn how Amazon Redshift uses columnar technology, optimized hardware, and massively parallel processing to deliver fast query performance on data sets ranging in size from hundreds of gigabytes to a petabyte or more. We'll also walk through techniques for optimizing performance and, you’ll hear from a specific customer and their use case to take advantage of fast performance on enormous datasets leveraging economies of scale on the AWS platform.
Speakers:
Ian Meyers, AWS Solutions Architect
Toby Moore, Chief Technology Officer, Space Ape
This document discusses performance tuning in Redshift. It covers factors to consider like database design, execution queues, and query tips. For database design, it recommends usage of sort keys and distribution keys, as well as column compression. It also discusses query diagnosis using EXPLAIN and provides tips on common performance issues like unoptimized queries, disk I/O, memory limits, and table fragmentation.
Traditional data warehouses become expensive and slow down as the volume of your data grows. Amazon Redshift is a fast, petabyte-scale data warehouse that makes it easy to analyze all of your data using existing business intelligence tools for 1/10th the traditional cost. This session will provide an introduction to Amazon Redshift and cover the essentials you need to deploy your data warehouse in the cloud so that you can achieve faster analytics and save costs.
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.
Learn best practices for taking advantage of Amazon Redshift's columnar technology and parallel processing capabilities to improve your data warehouse performance.
Archaic database technologies just don't scale under the always on, distributed demands of modern IOT, mobile and web applications. We'll start this Intro to Cassandra by discussing how its approach is different and why so many awesome companies have migrated from the cold clutches of the relational world into the warm embrace of peer to peer architecture. After this high-level opening discussion, we'll briefly unpack the following:
• Cassandra's internal architecture and distribution model
• Cassandra's Data Model
• Reads and Writes
Amazon Redshift is a fully managed data warehouse service that makes it fast, simple and cost effective to analyze data using SQL and existing business intelligence tools. The document provides an overview of Amazon Redshift and its benefits including speed, low cost, security, scalability and ease of use. It also provides examples of how various companies use Redshift for big data analytics including analyzing social media firehoses, mobile usage and real-time IoT streaming data.
Designing ETL Pipelines with Structured Streaming and Delta Lake—How to Archi...Databricks
Structured Streaming has proven to be the best platform for building distributed stream processing applications. Its unified SQL/Dataset/DataFrame APIs and Spark’s built-in functions make it easy for developers to express complex computations. Delta Lake, on the other hand, is the best way to store structured data because it is a open-source storage layer that brings ACID transactions to Apache Spark and big data workloads Together, these can make it very easy to build pipelines in many common scenarios. However, expressing the business logic is only part of the larger problem of building end-to-end streaming pipelines that interact with a complex ecosystem of storage systems and workloads. It is important for the developer to truly understand the business problem that needs to be solved. Apache Spark, being a unified analytics engine doing both batch and stream processing, often provides multiples ways to solve the same problem. So understanding the requirements carefully helps you to architect your pipeline that solves your business needs in the most resource efficient manner.
In this talk, I am going examine a number common streaming design patterns in the context of the following questions.
WHAT are you trying to consume? What are you trying to produce? What is the final output that the business wants? What are your throughput and latency requirements?
WHY do you really have those requirements? Would solving the requirements of the individual pipeline actually solve your end-to-end business requirements?
HOW are going to architect the solution? And how much are you willing to pay for it?
Clarity in understanding the ‘what and why’ of any problem can automatically much clarity on the ‘how’ to architect it using Structured Streaming and, in many cases, Delta Lake.
by Manish Mohite, Solutions Architect, AWS
How do you get data from your sources into your Redshift data warehouse? We'll show how to use AWS Glue and Amazon Kinesis Firehose to make it easy to automate the work to get data loaded.
Amazon Redshift is a fast, fully managed, petabyte-scale data warehouse service that makes it simple and cost-effective to efficiently analyze all your data using your existing business intelligence tools. You can start small for just $0.25 per hour with no commitment or upfront costs and scale to a petabyte or more for $1,000 per terabyte per year, less than a tenth of most other data warehousing solutions.
See a recording of the webinar based on this presentation here on YouTube: https://youtu.be/GgLKodmL5xE
Masterclass series webinars, including on-demand access to all of this years recorded webinars: http://aws.amazon.com/campaigns/emea/masterclass/
Journey Through the Cloud webinar series, including on-demand access to all webinars so far this year: http://aws.amazon.com/campaigns/emea/journey/
Infrastructure at Scale: Apache Kafka, Twitter Storm & Elastic Search (ARC303...Amazon Web Services
"This is a technical architect's case study of how Loggly has employed the latest social-media-scale technologies as the backbone ingestion processing for our multi-tenant, geo-distributed, and real-time log management system. This presentation describes design details of how we built a second-generation system fully leveraging AWS services including Amazon Route 53 DNS with heartbeat and latency-based routing, multi-region VPCs, Elastic Load Balancing, Amazon Relational Database Service, and a number of pro-active and re-active approaches to scaling computational and indexing capacity.
The talk includes lessons learned in our first generation release, validated by thousands of customers; speed bumps and the mistakes we made along the way; various data models and architectures previously considered; and success at scale: speeds, feeds, and an unmeltable log processing engine."
Spark SQL Deep Dive @ Melbourne Spark MeetupDatabricks
This document summarizes a presentation on Spark SQL and its capabilities. Spark SQL allows users to run SQL queries on Spark, including HiveQL queries with UDFs, UDAFs, and SerDes. It provides a unified interface for reading and writing data in various formats. Spark SQL also allows users to express common operations like selecting columns, joining data, and aggregation concisely through its DataFrame API. This reduces the amount of code users need to write compared to lower-level APIs like RDDs.
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.
Performance Optimizations in Apache ImpalaCloudera, Inc.
Apache Impala is a modern, open-source MPP SQL engine architected from the ground up for the Hadoop data processing environment. Impala provides low latency and high concurrency for BI/analytic read-mostly queries on Hadoop, not delivered by batch frameworks such as Hive or SPARK. Impala is written from the ground up in C++ and Java. It maintains Hadoop’s flexibility by utilizing standard components (HDFS, HBase, Metastore, Sentry) and is able to read the majority of the widely-used file formats (e.g. Parquet, Avro, RCFile).
To reduce latency, such as that incurred from utilizing MapReduce or by reading data remotely, Impala implements a distributed architecture based on daemon processes that are responsible for all aspects of query execution and that run on the same machines as the rest of the Hadoop infrastructure. Impala employs runtime code generation using LLVM in order to improve execution times and uses static and dynamic partition pruning to significantly reduce the amount of data accessed. The result is performance that is on par or exceeds that of commercial MPP analytic DBMSs, depending on the particular workload. Although initially designed for running on-premises against HDFS-stored data, Impala can also run on public clouds and access data stored in various storage engines such as object stores (e.g. AWS S3), Apache Kudu and HBase. In this talk, we present Impala's architecture in detail and discuss the integration with different storage engines and the cloud.
RocksDB is an embedded key-value store written in C++ and optimized for fast storage environments like flash or RAM. It uses a log-structured merge tree to store data by writing new data sequentially to an in-memory log and memtable, periodically flushing the memtable to disk in sorted SSTables. It reads from the memtable and SSTables, and performs background compaction to merge SSTables and remove overwritten data. RocksDB supports two compaction styles - level style, which stores SSTables in multiple levels sorted by age, and universal style, which stores all SSTables in level 0 sorted by time.
Top 5 Mistakes When Writing Spark ApplicationsSpark Summit
This document discusses 5 common mistakes when writing Spark applications:
1) Improperly sizing executors by not considering cores, memory, and overhead. The optimal configuration depends on the workload and cluster resources.
2) Applications failing due to shuffle blocks exceeding 2GB size limit. Increasing the number of partitions helps address this.
3) Jobs running slowly due to data skew in joins and shuffles. Techniques like salting keys can help address skew.
4) Not properly managing the DAG to avoid shuffles and bring work to the data. Using ReduceByKey over GroupByKey and TreeReduce over Reduce when possible.
5) Classpath conflicts arising from mismatched library versions, which can be addressed using sh
This document provides an overview of Amazon Redshift, including its history, architecture, concepts, terminology, storage subsystem, and query lifecycle. It discusses how Redshift uses a massively parallel processing (MPP) architecture with columnar storage to improve query performance and reduce storage requirements through data compression. Key concepts explained include slices, sorting, data distribution styles, and how data is stored across disks and persisted to blocks at the physical level.
"Structured Streaming was a new streaming API introduced to Spark over 2 years ago in Spark 2.0, and was announced GA as of Spark 2.2. Databricks customers have processed over a hundred trillion rows in production using Structured Streaming. We received dozens of questions on how to best develop, monitor, test, deploy and upgrade these jobs. In this talk, we aim to share best practices around what has worked and what hasn't across our customer base.
We will tackle questions around how to plan ahead, what kind of code changes are safe for structured streaming jobs, how to architect streaming pipelines which can give you the most flexibility without sacrificing performance by using tools like Databricks Delta, how to best monitor your streaming jobs and alert if your streams are falling behind or are actually failing, as well as how to best test your code."
Streaming Data Analytics with Amazon Redshift and Kinesis FirehoseAmazon Web Services
Kinesis Firehose and Redshift are used to build a streaming data analytics solution for log analysis. Data is sent to a Firehose delivery stream, transformed, and loaded into an Amazon Redshift database table. The data in Redshift can then be queried and analyzed. CloudWatch is used to monitor the streaming data pipeline and check metrics and logs.
Best Practices for Migrating your Data Warehouse to Amazon RedshiftAmazon Web Services
You can gain substantially more business insights and save costs by migrating your existing data warehouse to Amazon Redshift. This session will cover the key benefits of migrating to Amazon Redshift, migration strategies, and tools and resources that can help you in the process.
This document provides best practices for deploying Microsoft SQL Server on Amazon EC2. It discusses using multiple Amazon EBS volumes for tempdb and data files to improve performance. It also covers high availability options like AlwaysOn Availability Groups across Availability Zones and failover cluster instances. The document recommends configuring security groups and network access control lists for security in a VPC.
What’s New in Amazon RDS for Open-Source and Commercial DatabasesAmazon Web Services
In the past year, Amazon Relational Database Service has continued to expand functionality, scalability, availability and ease of use for all supported database engines (PostgreSQL, MySQL, MariaDB, Oracle and Microsoft SQL Server). We’ll take a close look at RDS use cases and new capabilities, splitting the time between open-source and commercial database engines.
Making (Almost) Any Database Faster and Cheaper with CachingAmazon Web Services
Learn how to make your AWS databases up to 10x faster and up to 90% less expensive with Amazon ElastiCache for Redis. We’ll look at how to determine whether caching will benefit your database environment and show how to easily test and implement a high speed solution.
It’s been an exciting year for Amazon Aurora, the MySQL-compatible relational database engine that combines the speed and availability of high-end commercial databases with the simplicity and cost-effectiveness of open source databases. In this deep dive session, we’ll discuss best practices and explore new features, include high availability options and new integrations with AWS services. We’ll also discuss the recently-announced Aurora with PostgreSQL compatibility.
In this session, we explore Amazon DynamoDB capabilities and benefits in detail and discusses how to get the most out of your DynamoDB database. We go over schema design best practices with DynamoDB across multiple use cases, including gaming, AdTech, IoT, and others. We also explore designing efficient indexes, scanning, and querying, and go into detail on a number of recently released features, including JSON document support, Streams, and more.
This document provides an overview of AWS Database Migration Service (DMS) and Schema Conversion Tool (SCT) for migrating and replicating databases to AWS. It discusses how DMS supports migration across many database engines like Oracle, SQL Server, MySQL, PostgreSQL and more. It also outlines when to use DMS and SCT for modernization, migration or replication use cases. The document then demonstrates how DMS works through database migration and change data capture processes. It shares examples of how customers have used DMS and SCT for heterogeneous, scale up and split migrations. It concludes with details on DMS pricing and available resources.
Amazon RDS allows you to launch an optimally configured, secure and highly available database with just a few clicks. It provides cost-efficient and resizable capacity while managing time-consuming database administration tasks, freeing you up to focus on your applications and business. We’ll discuss Amazon RDS fundamentals, learn about the six available database engines (with the seventh on the way), and examine customer success stories.
This session will begin with an introduction to non-relational (NoSQL) databases and compare them with relational (SQL) databases. Learn the fundamentals of Amazon DynamoDB, a fully managed NoSQL database service, and see the DynamoDB console first-hand. See a walk-through demo of building a serverless web application using this high-performance key-value and JSON document store.
1) Hive was created as a startup within a larger company to address issues with customer churn, bill shock, and wasted energy from inefficient heating schedules.
2) The startup took a lean startup approach and was given independence from the parent company's IT systems to develop a new front end and product management organization.
3) Over time Hive has expanded from managing heating to integrating other smart home devices through acquisitions and new product lines, with an architecture based around microservices and AWS services rather than trying to replicate Google or Facebook.
You know gaming. You know how to create compelling gameplay with cool graphics. Let us show how to use AWS services for registration, parental consent, leaderboards, in-game chat, payment and game analytics – all the boring stuff that you need to support your game’s viral success.
Telenor Connexion started as an M2M platform company and later became a service provider focused on a commodity business. To innovate again, they began an AWS pilot project to validate AWS for their use cases like customer service, cost reduction, and risk mitigation. They went live with a serverless Cloud Connect 2.0 platform based on AWS IoT for device management, data management, and became an all-in AWS customer. Key success factors included piloting early, attracting developers, and using AWS services like IoT, Redshift, Kinesis, and Lambda.
This document discusses cost optimization strategies on AWS. It provides examples of cost savings achieved by companies that migrated applications to AWS including a 14 million dollar annual savings for GE. It outlines approaches for architecting efficiently for cost, optimizing usage costs over time, and taking advantage of AWS pricing benefits like reserved instances, spot instances, and different storage options. The document emphasizes optimizing through proactive monitoring and billing tools, leveraging the various EC2 pricing plans, and combining options for further savings.
This document summarizes an AWSome Day event for AWS partners. The agenda included:
1. Discussing the partnership vision and AWS value proposition for partners.
2. Highlighting business opportunities partners can pursue by working with AWS, such as managed services, migration services, and software solutions.
3. Providing guidance on how partners can successfully work with AWS, including training staff, focusing on automation, and leveraging AWS pricing models.
4. Explaining how partners can effectively go to market with AWS, such as aligning with AWS best practices, highlighting the partnership, and leveraging AWS marketing and support resources.
The document discusses how the traditional IT model of on-premises infrastructure is no longer suitable due to increasing disruption, security risks, and the shortening lifespan of companies. It argues that a new IT model using cloud computing allows companies to focus on innovation, reduce costs and risks, and gain flexibility and agility. Some key benefits highlighted include reducing technical debt, scaling instantly based on demand, and paying only for resources used. The document provides examples of how various companies are leveraging AWS cloud services to digitally transform and gain competitive advantages.
Ian Massingham from Amazon Web Services discusses designing and building applications for the Internet of Things using AWS services. The document outlines how AWS IoT provides scalable connectivity and management for IoT devices, services for processing device data like DynamoDB and Kinesis, and Greengrass for running AWS Lambda functions locally on devices. Greengrass allows using the same AWS programming model on devices and in the cloud to address challenges of intermittent connectivity and limited capabilities of edge devices.
In this presentation, you will get a look under the covers of Amazon Redshift, a fast, fully-managed, petabyte-scale data warehouse service for less than $1,000 per TB per year. Learn how Amazon Redshift uses columnar technology, optimized hardware, and massively parallel processing to deliver fast query performance on data sets ranging in size from hundreds of gigabytes to a petabyte or more. We'll also walk through techniques for optimizing performance and, you’ll hear from a specific customer and their use case to take advantage of fast performance on enormous datasets leveraging economies of scale on the AWS platform.
Real-time data processing serverless architecture can eliminate the need to provision and manage servers required to process files or streaming data in real time. In this session, we will cover the fundamentals of using AWS Lambda to process data in real-time from push sources such as AWS Iot and pull sources such as Amazon DynamoDB Streams or Amazon Kinesis. We'll also discuss best practices and do a deep dive into AWS Lambda real-time stream processing.
The document provides an overview of security best practices for AWS. It discusses AWS' shared security responsibility model and outlines many of the built-in security features available, such as role-based access control, multi-factor authentication, encryption, and security groups. Recommendations are given for securing access, defining assets, designing an information security management system, managing accounts and roles, protecting data at rest and in transit, securing operating systems and applications, monitoring, and incident response. The presentation encourages leveraging AWS services like IAM, VPC, and CloudTrail to improve security posture in the cloud.
Data warehousing in the era of Big Data: Deep Dive into Amazon RedshiftAmazon 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 all of your 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.
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.
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/
Data Warehousing in the Era of Big Data: Deep Dive into Amazon RedshiftAmazon Web Services
by Tony Gibbs, Data Warehouse Specialist SA, 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 all of your 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.
This spring, the data warehouse team at Ancestry, flawlessly migrated and validated nearly half a trillion records from Actian Matrix to Amazon Redshift. During this session, the Ancestry team will describe how they orchestrated the entire migration in less than four months, the technical challenges they faced and overcame along the way, as well as share tips and tricks to break through common pitfalls of data warehouse migrations. They will also highlight how they tuned and optimized the Amazon Redshift environment, adopted Redshift Spectrum, and how they leverage their collaboration with Amazon to deliver a powerful customer experience.
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.
AWS SSA Webinar 20 - Getting Started with Data Warehouses on AWSCobus Bernard
In this session, we will take you through setting up an Amazon Redshift cluster and at the ways you can populate it with data. We will start by using AWS DMS to replicate the data as-is as well as doing some ETL on it. This will be followed by AWS Glue where you can do more advanced ETL operations. Lastly, we will look at how you can use Amazon Kinesis Firehose to stream event directly to the Redshift cluster.
Best practices for Data warehousing with Amazon Redshift - AWS PS Summit Canb...Amazon Web Services
Get a look under the hood: Understand how to take advantage of Amazon Redshift's columnar technology and parallel processing capabilities to improve your delivery of queries and improve overall database performance. You’ll also hear about how the University of Technology Sydney (UTS) are using Redshift. The University of Technology Sydney will describe how utilizing Amazon Redshift enabled agility in dealing with Data Quality, a capacity to scale when required, and optimizing development processes through rapid provisioning of Data Warehouse environments.
Speaker: Ganesh Raja, Solutions Architect, Amazon Web Services with Susan Gibson, Manager, Data and Business Intelligence, UTS
Level: 300
Best Practices for Migrating Your Data Warehouse to Amazon RedshiftAmazon Web Services
by Darin Briskman, Technical Evangelist, AWS
You can gain substantially more business insights and save costs by migrating your existing data warehouse to Amazon Redshift. This session will cover the key benefits of migrating to Amazon Redshift, migration strategies, and tools and resources that can help you in the process. We’ll learn about AWS Database Migration Service and AWS Schema Migration Tool, which were recently enhanced to import data from six common data warehouse platforms. Level: 200
Amazon Redshift is a data warehouse service that runs on AWS. It has a leader node that coordinates queries and compute nodes that store and process the data in parallel. The compute nodes can use either HDD storage optimized for large datasets or SSD storage optimized for fast queries. Data is stored in columns and compressed to reduce I/O. Queries are optimized using statistics on the data distribution, sort keys and other metadata. The EXPLAIN command and STL tables provide visibility into query plans and performance.
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
Best Practices and Performance Tuning of U-SQL in Azure Data Lake (SQL Konfer...Michael Rys
The document discusses best practices and performance tuning for U-SQL in Azure Data Lake. It provides an overview of U-SQL query execution, including the job scheduler, query compilation process, and vertex execution model. The document also covers techniques for analyzing and optimizing U-SQL job performance, including analyzing the critical path, using heat maps, optimizing AU usage, addressing data skew, and query tuning techniques like data loading tips, partitioning, predicate pushing and column pruning.
This document provides an overview of DFSMS Basics and Data Set Fundamentals. It discusses the structure of data sets on direct access storage devices (DASD) including volumes, tracks, cylinders, the volume table of contents (VTOC), catalogs, and data set names. It also summarizes the different types of data set organizations including non-VSAM (direct, sequential, partitioned) and VSAM (KSDS, ESDS, RRDS, linear) as well as newer technologies like PDSEs, HFS, and zFS. The document concludes with discussions on common data set uses, limitations, extended format features, and defining data set attributes in JCL or with IDCAMS.
This document summarizes a lecture on key-value storage systems. It introduces the key-value data model and compares it to relational databases. It then describes Cassandra, a popular open-source key-value store, including how it maps keys to servers, replicates data across multiple servers, and performs reads and writes in a distributed manner while maintaining consistency. The document also discusses Cassandra's use of gossip protocols to manage cluster membership.
Best Practices for Migrating your Data Warehouse to Amazon RedshiftAmazon Web Services
You can gain substantially more business insights and save costs by migrating your existing data warehouse to Amazon Redshift. This session will cover the key benefits of migrating to Amazon Redshift, migration strategies, and tools and resources that can help you in the process. We’ll learn about AWS Database Migration Service and AWS Schema Migration Tool, which were recently enhanced to import data from six common data warehouse platforms.
Best Practices for Migrating your Data Warehouse to Amazon Redshift Amazon Web Services
This document provides best practices for migrating a data warehouse to Amazon Redshift. It discusses why companies migrate to Redshift due to its scalability, performance and cost advantages. Example migration stories are provided from companies that achieved significant improvements after migrating large datasets from Oracle, Greenplum and SQL on Hadoop to Redshift. The document also outlines the Redshift cluster architecture, data loading best practices including file splitting and column encoding, schema design considerations and available migration tools.
SQL Server 2014 Memory Optimised Tables - AdvancedTony Rogerson
Hekaton is large piece of kit, this session will focus on the internals of how in-memory tables and native stored procedures work and interact – Database structure: use of File Stream, backup/restore considerations in HA and DR as well as Database Durability, in-memory table make up: hash and range indexes, row chains, Multi-Version Concurrency Control (MVCC). Design considerations and gottcha’s to watch out for.
The session will be demo led.
Note: the session will assume the basics of Hekaton are known, so it is recommended you attend the Basics session.
ABD304-R-Best Practices for Data Warehousing with Amazon Redshift & SpectrumAmazon Web Services
This document provides an overview and best practices for using Amazon Redshift and Redshift Spectrum for data warehousing. It covers the history and development of Redshift, key concepts like columnar storage, compression, sorting and distribution styles. It provides examples and recommendations for table design, workload management, and query optimization techniques.
Best Practices for Supercharging Cloud Analytics on Amazon RedshiftSnapLogic
In this webinar, we discuss how the secret sauce to your business analytics strategy remains rooted on your approached, methodologies and the amount of data incorporated into this critical exercise. We also address best practices to supercharge your cloud analytics initiatives, and tips and tricks on designing the right information architecture, data models and other tactical optimizations.
To learn more, visit: http://www.snaplogic.com/redshift-trial
Amazon Redshift는 속도가 빠른 페타바이트 규모의 완전관리형 데이터 웨어하우스로, 간편하고 비용 효율적으로 모든 데이터를 기존 비즈니스 인텔리전스 도구를 사용하여 분석할 수 있게 해줍니다. 이 강연에서는 대량 병렬처리를 가능하게 하는 RedShift의 분산 처리구조를 살펴보고, 다양한 데이터 소스 및 포맷으로 부터의 데이터 통합 및 로드를 위한 모범 사례에 대하여 실습을 통하여 학습할 예정입니다.
연사: 김상필, 아마존 웹서비스 솔루션즈 아키텍트
Come costruire servizi di Forecasting sfruttando algoritmi di ML e deep learn...Amazon Web Services
Il Forecasting è un processo importante per tantissime aziende e viene utilizzato in vari ambiti per cercare di prevedere in modo accurato la crescita e distribuzione di un prodotto, l’utilizzo delle risorse necessarie nelle linee produttive, presentazioni finanziarie e tanto altro. Amazon utilizza delle tecniche avanzate di forecasting, in parte questi servizi sono stati messi a disposizione di tutti i clienti AWS.
In questa sessione illustreremo come pre-processare i dati che contengono una componente temporale e successivamente utilizzare un algoritmo che a partire dal tipo di dato analizzato produce un forecasting accurato.
Big Data per le Startup: come creare applicazioni Big Data in modalità Server...Amazon Web Services
La varietà e la quantità di dati che si crea ogni giorno accelera sempre più velocemente e rappresenta una opportunità irripetibile per innovare e creare nuove startup.
Tuttavia gestire grandi quantità di dati può apparire complesso: creare cluster Big Data su larga scala sembra essere un investimento accessibile solo ad aziende consolidate. Ma l’elasticità del Cloud e, in particolare, i servizi Serverless ci permettono di rompere questi limiti.
Vediamo quindi come è possibile sviluppare applicazioni Big Data rapidamente, senza preoccuparci dell’infrastruttura, ma dedicando tutte le risorse allo sviluppo delle nostre le nostre idee per creare prodotti innovativi.
Ora puoi utilizzare Amazon Elastic Kubernetes Service (EKS) per eseguire pod Kubernetes su AWS Fargate, il motore di elaborazione serverless creato per container su AWS. Questo rende più semplice che mai costruire ed eseguire le tue applicazioni Kubernetes nel cloud AWS.In questa sessione presenteremo le caratteristiche principali del servizio e come distribuire la tua applicazione in pochi passaggi
Vent'anni fa Amazon ha attraversato una trasformazione radicale con l'obiettivo di aumentare il ritmo dell'innovazione. In questo periodo abbiamo imparato come cambiare il nostro approccio allo sviluppo delle applicazioni ci ha permesso di aumentare notevolmente l'agilità, la velocità di rilascio e, in definitiva, ci ha consentito di creare applicazioni più affidabili e scalabili. In questa sessione illustreremo come definiamo le applicazioni moderne e come la creazione di app moderne influisce non solo sull'architettura dell'applicazione, ma sulla struttura organizzativa, sulle pipeline di rilascio dello sviluppo e persino sul modello operativo. Descriveremo anche approcci comuni alla modernizzazione, compreso l'approccio utilizzato dalla stessa Amazon.com.
Come spendere fino al 90% in meno con i container e le istanze spot Amazon Web Services
L’utilizzo dei container è in continua crescita.
Se correttamente disegnate, le applicazioni basate su Container sono molto spesso stateless e flessibili.
I servizi AWS ECS, EKS e Kubernetes su EC2 possono sfruttare le istanze Spot, portando ad un risparmio medio del 70% rispetto alle istanze On Demand. In questa sessione scopriremo insieme quali sono le caratteristiche delle istanze Spot e come possono essere utilizzate facilmente su AWS. Impareremo inoltre come Spreaker sfrutta le istanze spot per eseguire applicazioni di diverso tipo, in produzione, ad una frazione del costo on-demand!
In recent months, many customers have been asking us the question – how to monetise Open APIs, simplify Fintech integrations and accelerate adoption of various Open Banking business models. Therefore, AWS and FinConecta would like to invite you to Open Finance marketplace presentation on October 20th.
Event Agenda :
Open banking so far (short recap)
• PSD2, OB UK, OB Australia, OB LATAM, OB Israel
Intro to Open Finance marketplace
• Scope
• Features
• Tech overview and Demo
The role of the Cloud
The Future of APIs
• Complying with regulation
• Monetizing data / APIs
• Business models
• Time to market
One platform for all: a Strategic approach
Q&A
Rendi unica l’offerta della tua startup sul mercato con i servizi Machine Lea...Amazon Web Services
Per creare valore e costruire una propria offerta differenziante e riconoscibile, le startup di successo sanno come combinare tecnologie consolidate con componenti innovativi creati ad hoc.
AWS fornisce servizi pronti all'utilizzo e, allo stesso tempo, permette di personalizzare e creare gli elementi differenzianti della propria offerta.
Concentrandoci sulle tecnologie di Machine Learning, vedremo come selezionare i servizi di intelligenza artificiale offerti da AWS e, anche attraverso una demo, come costruire modelli di Machine Learning personalizzati utilizzando SageMaker Studio.
OpsWorks Configuration Management: automatizza la gestione e i deployment del...Amazon Web Services
Con l'approccio tradizionale al mondo IT per molti anni è stato difficile implementare tecniche di DevOps, che finora spesso hanno previsto attività manuali portando di tanto in tanto a dei downtime degli applicativi interrompendo l'operatività dell'utente. Con l'avvento del cloud, le tecniche di DevOps sono ormai a portata di tutti a basso costo per qualsiasi genere di workload, garantendo maggiore affidabilità del sistema e risultando in dei significativi miglioramenti della business continuity.
AWS mette a disposizione AWS OpsWork come strumento di Configuration Management che mira ad automatizzare e semplificare la gestione e i deployment delle istanze EC2 per mezzo di workload Chef e Puppet.
Scopri come sfruttare AWS OpsWork a garanzia e affidabilità del tuo applicativo installato su Instanze EC2.
Microsoft Active Directory su AWS per supportare i tuoi Windows WorkloadsAmazon Web Services
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.
Dal riconoscimento facciale al riconoscimento di frodi o difetti di fabbricazione, l'analisi di immagini e video che sfruttano tecniche di intelligenza artificiale, si stanno evolvendo e raffinando a ritmi elevati. In questo webinar esploreremo le possibilità messe a disposizione dai servizi AWS per applicare lo stato dell'arte delle tecniche di computer vision a scenari reali.
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.
1) The document discusses building a minimum viable product (MVP) using Amazon Web Services (AWS).
2) It provides an example of an MVP for an omni-channel messenger platform that was built from 2017 to connect ecommerce stores to customers via web chat, Facebook Messenger, WhatsApp, and other channels.
3) The founder discusses how they started with an MVP in 2017 with 200 ecommerce stores in Hong Kong and Taiwan, and have since expanded to over 5000 clients across Southeast Asia using AWS for scaling.
This document discusses pitch decks and fundraising materials. It explains that venture capitalists will typically spend only 3 minutes and 44 seconds reviewing a pitch deck. Therefore, the deck needs to tell a compelling story to grab their attention. It also provides tips on tailoring different types of decks for different purposes, such as creating a concise 1-2 page teaser, a presentation deck for pitching in-person, and a more detailed read-only or fundraising deck. The document stresses the importance of including key information like the problem, solution, product, traction, market size, plans, team, and ask.
This document discusses building serverless web applications using AWS services like API Gateway, Lambda, DynamoDB, S3 and Amplify. It provides an overview of each service and how they can work together to create a scalable, secure and cost-effective serverless application stack without having to manage servers or infrastructure. Key services covered include API Gateway for hosting APIs, Lambda for backend logic, DynamoDB for database needs, S3 for static content, and Amplify for frontend hosting and continuous deployment.
This document provides tips for fundraising from startup founders Roland Yau and Sze Lok Chan. It discusses generating competition to create urgency for investors, fundraising in parallel rather than sequentially, having a clear fundraising narrative focused on what you do and why it's compelling, and prioritizing relationships with people over firms. It also notes how the pandemic has changed fundraising, with examples of deals done virtually during this time. The tips emphasize being fully prepared before fundraising and cultivating connections with investors in advance.
AWS_HK_StartupDay_Building Interactive websites while automating for efficien...Amazon Web Services
This document discusses Amazon's machine learning services for building conversational interfaces and extracting insights from unstructured text and audio. It describes Amazon Lex for creating chatbots, Amazon Comprehend for natural language processing tasks like entity extraction and sentiment analysis, and how they can be used together for applications like intelligent call centers and content analysis. Pre-trained APIs simplify adding machine learning to apps without requiring ML expertise.
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.
Using Large Language Models in Public Services (Past Tense)
#smart_conference #Nile_University #IEEE #AI #LLM #NLP
The presentation explored the transformative potential of large language models (LLMs) in revolutionizing public service delivery. As artificial intelligence and natural language processing technologies advanced, LLMs offered unprecedented opportunities to streamline operations, enhance citizen engagement, and drive innovative solutions for pressing societal challenges.
This Presentations defines communication skills as the ability to exchange information via the use of language, both receptively and expressively. It examines several forms of communication based on organizational linkages and flow. Semantic concerns, emotional/psychological considerations, corporate policies, and personal attitudes can all operate as communication barriers. Effective communication is two-way, with active listening and feedback, and it is clear, concise, complete, concrete, respectful, and accurate. Good communication skills are essential for career success, dispute resolution, connection building, and increased productivity.
Pass AWS Certified Developer Associate with new exam dumps 2024SkillCertProExams
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Trapbone Routing Plan created by Marcus Davis JrMarcusDavisJr1
This is a mock routing plan I made for musical artist Trapbone. The project was made while pursuing a music business bachelor's degree from Full Sail University.
The goal of Meet Mack Monday Zoom meetings is to inform residents of township issues that impact them and to get feedback and comments from residents about such issues. This helps me keep better informed of residents’ concerns when I vote on the issues at Board of Supervisors meetings. This meeting focused on Anti Chick-fil-A “on” the Bypass Petition Update, Wawa Coming Soon – Will It Sell Beer/Wine 24/7? LI/O-LI District Overlay Problems: High Density Housing, More Traffic Congestion, Pedestrian Crosswalk Improvements – Why the Delays? Corners at Newtown “Garage Core” Apartments: Should We Amend JMZO to Allow This New Use in the Town Center District?, Pollinator Garden” in Roberts Ridge Park, Indoor Pickleball Club Proposed for Vacant Bed, Bath, and Beyond Site
PSUG 3 - 2024-07-15 - Splunk & AI with Philipp DriegerTomas Moser
Once in a life time opportunity for Prague Splunk User Group and Splunkers in Czechia and abroad. Join us to discover Splunk AI and Machine Learning (ML) capabilities in a rare session presented by Philipp Drieger, Global Principal Machine Learning Architect at Splunk. With AI hype all over the world these days this is a unique moment and a chance to bring together those already familiar with Splunk universal machine data platform but without any AI/ML knowledge or experience and seasoned or full time data scientists interested in Splunk and its AI/ML capabilities.
Part 1: Introduction to Splunk AI (45min)
Get to know Splunk AI first hand from Philipp, Global Principal Architect for Machine Learning at Splunk. He will share a easy to understand overview of Splunk's key AI components and also highlight some real world customer use cases.
Open Q&A
Part 2: Splunk AI demos and open AMA session (45min)
Join Philipp showing live demos including Splunk's Machine Learning Toolkit, the Splunk App for Data Science and Deep Learning and the latest Splunk AI Assistant.
Open AMA session: Ask Me Anything about Splunk AI
PSUG 3 - 2024-07-15 - Splunk & AI with Philipp Drieger
Deep Dive on Amazon Redshift
1. Deep Dive on Amazon Redshift
Storage Subsystem and Query Life Cycle
Eric Ferreira, Principal Database Engineer, Amazon Redshift
Mar 2017
2. Deep Dive Overview
• Amazon Redshift History and Development
• Cluster Architecture
• Concepts and Terminology
• Storage Deep Dive
• Design Considerations
• Query Life Cycle
• New & Upcoming Feature
• Open Q&A
13. Designed for I/O Reduction
• Columnar storage
• Data compression
• Zone maps
aid loc dt
1 SFO 2016-09-01
2 JFK 2016-09-14
3 SFO 2017-04-01
4 JFK 2017-05-14
• Accessing dt with row storage:
– Need to read everything
– Unnecessary I/O
aid loc dt
CREATE TABLE loft_deep_dive (
aid INT --audience_id
,loc CHAR(3) --location
,dt DATE --date
);
14. Designed for I/O Reduction
• Columnar storage
• Data compression
• Zone maps
aid loc dt
1 SFO 2016-09-01
2 JFK 2016-09-14
3 SFO 2017-04-01
4 JFK 2017-05-14
• Accessing dt with columnar storage:
– Only scan blocks for relevant
column
aid loc dt
CREATE TABLE loft_deep_dive (
aid INT --audience_id
,loc CHAR(3) --location
,dt DATE --date
);
15. Designed for I/O Reduction
• Columnar storage
• Data compression
• Zone maps
aid loc dt
1 SFO 2016-09-01
2 JFK 2016-09-14
3 SFO 2017-04-01
4 JFK 2017-05-14
• Columns grow and shrink independently
• Effective compression ratios due to like data
• Reduces storage requirements
• Reduces I/O
aid loc dt
CREATE TABLE loft_deep_dive (
aid INT ENCODE LZO
,loc CHAR(3) ENCODE BYTEDICT
,dt DATE ENCODE RUNLENGTH
);
16. Designed for I/O Reduction
• Columnar storage
• Data compression
• Zone maps
aid loc dt
1 SFO 2016-09-01
2 JFK 2016-09-14
3 SFO 2017-04-01
4 JFK 2017-05-14
aid loc dt
CREATE TABLE loft_deep_dive (
aid INT --audience_id
,loc CHAR(3) --location
,dt DATE --date
);
• In-memory block metadata
• Contains per-block MIN and MAX value
• Effectively prunes blocks which cannot
contain data for a given query
• Eliminates unnecessary I/O
17. SELECT COUNT(*) FROM LOGS WHERE DATE = '09-JUNE-2013'
MIN: 01-JUNE-2013
MAX: 20-JUNE-2013
MIN: 08-JUNE-2013
MAX: 30-JUNE-2013
MIN: 12-JUNE-2013
MAX: 20-JUNE-2013
MIN: 02-JUNE-2013
MAX: 25-JUNE-2013
Unsorted Table
MIN: 01-JUNE-2013
MAX: 06-JUNE-2013
MIN: 07-JUNE-2013
MAX: 12-JUNE-2013
MIN: 13-JUNE-2013
MAX: 18-JUNE-2013
MIN: 19-JUNE-2013
MAX: 24-JUNE-2013
Sorted By Date
Zone Maps
18. Terminology and Concepts: Data Sorting
• Goals:
• Physically order rows of table data based on certain column(s)
• Optimize effectiveness of zone maps
• Enable MERGE JOIN operations
• Impact:
• Enables rrscans to prune blocks by leveraging zone maps
• Overall reduction in block I/O
• Achieved with the table property SORTKEY defined over one or more columns
• Optimal SORTKEY is dependent on:
• Query patterns
• Data profile
• Business requirements
19. Terminology and Concepts: Slices
• A slice can be thought of like a “virtual compute node”
– Unit of data partitioning
– Parallel query processing
• Facts about slices:
– Each compute node has either 2, 16, or 32 slices
– Table rows are distributed to slices
– A slice processes only its own data
20. Data Distribution
• Distribution style is a table property which dictates how that table’s data is
distributed throughout the cluster:
• KEY: Value is hashed, same value goes to same location (slice)
• ALL: Full table data goes to first slice of every node
• EVEN: Round robin
• Goals:
• Distribute data evenly for parallel processing
• Minimize data movement during query processing
KEY
ALL
Node 1
Slice 1 Slice 2
Node 2
Slice 3 Slice 4
Node 1
Slice 1 Slice 2
Node 2
Slice 3 Slice 4
Node 1
Slice 1 Slice 2
Node 2
Slice 3 Slice 4
EVEN
21. Data Distribution: Example
CREATE TABLE loft_deep_dive (
aid INT --audience_id
,loc CHAR(3) --location
,dt DATE --date
) DISTSTYLE (EVEN|KEY|ALL);
CN1
Slice 0 Slice 1
CN2
Slice 2 Slice 3
Table: loft_deep_dive
User Columns System Columns
aid loc dt ins del row
22. Data Distribution: EVEN Example
CREATE TABLE loft_deep_dive (
aid INT --audience_id
,loc CHAR(3) --location
,dt DATE --date
) DISTSTYLE EVEN;
CN1
Slice 0 Slice 1
CN2
Slice 2 Slice 3
INSERT INTO loft_deep_dive VALUES
(1, 'SFO', '2016-09-01'),
(2, 'JFK', '2016-09-14'),
(3, 'SFO', '2017-04-01'),
(4, 'JFK', '2017-05-14');
Table: loft_deep_dive
User Columns System Columns
aid loc dt ins del row
Table: loft_deep_dive
User Columns System Columns
aid loc dt ins del row
Table: loft_deep_dive
User Columns System Columns
aid loc dt ins del row
Table: loft_deep_dive
User Columns System Columns
aid loc dt ins del row
Rows: 0 Rows: 0 Rows: 0 Rows: 0
(3 User Columns + 3 System Columns) x (4 slices) = 24 Blocks (24MB)
Rows: 1 Rows: 1 Rows: 1 Rows: 1
23. Data Distribution: KEY Example #1
CREATE TABLE loft_deep_dive (
aid INT --audience_id
,loc CHAR(3) --location
,dt DATE --date
) DISTSTYLE KEY DISTKEY (loc);
CN1
Slice 0 Slice 1
CN2
Slice 2 Slice 3
INSERT INTO loft_deep_dive VALUES
(1, 'SFO', '2016-09-01'),
(2, 'JFK', '2016-09-14'),
(3, 'SFO', '2017-04-01'),
(4, 'JFK', '2017-05-14');
Table: loft_deep_dive
User Columns System Columns
aid loc dt ins del row
Rows: 2 Rows: 0 Rows: 0
(3 User Columns + 3 System Columns) x (2 slices) = 12 Blocks (12MB)
Rows: 0Rows: 1
Table: loft_deep_dive
User Columns System Columns
aid loc dt ins del row
Rows: 2Rows: 0Rows: 1
24. Data Distribution: KEY Example #2
CREATE TABLE loft_deep_dive (
aid INT --audience_id
,loc CHAR(3) --location
,dt DATE --date
) DISTSTYLE KEY DISTKEY (aid);
CN1
Slice 0 Slice 1
CN2
Slice 2 Slice 3
INSERT INTO loft_deep_dive VALUES
(1, 'SFO', '2016-09-01'),
(2, 'JFK', '2016-09-14'),
(3, 'SFO', '2017-04-01'),
(4, 'JFK', '2017-05-14');
Table: loft_deep_dive
User Columns System Columns
aid loc dt ins del row
Table: loft_deep_dive
User Columns System Columns
aid loc dt ins del row
Table: loft_deep_dive
User Columns System Columns
aid loc dt ins del row
Table: loft_deep_dive
User Columns System Columns
aid loc dt ins del row
Rows: 0 Rows: 0 Rows: 0 Rows: 0
(3 User Columns + 3 System Columns) x (4 slices) = 24 Blocks (24MB)
Rows: 1 Rows: 1 Rows: 1 Rows: 1
25. Data Distribution: ALL Example
CREATE TABLE loft_deep_dive (
aid INT --audience_id
,loc CHAR(3) --location
,dt DATE --date
) DISTSTYLE ALL;
CN1
Slice 0 Slice 1
CN2
Slice 2 Slice 3
INSERT INTO loft_deep_dive VALUES
(1, 'SFO', '2016-09-01'),
(2, 'JFK', '2016-09-14'),
(3, 'SFO', '2017-04-01'),
(4, 'JFK', '2017-05-14');
Rows: 0 Rows: 0
(3 User Columns + 3 System Columns) x (2 slice) = 12 Blocks (12MB)
Table: loft_deep_dive
User Columns System Columns
aid loc dt ins del row
Rows: 0Rows: 1Rows: 2Rows: 4Rows: 3
Table: loft_deep_dive
User Columns System Columns
aid loc dt ins del row
Rows: 0Rows: 1Rows: 2Rows: 4Rows: 3
26. Terminology and Concepts: Data Distribution
• KEY
– The key creates an even distribution of data
– Joins are performed between large fact/dimension tables
– Optimizing merge joins and group by
• ALL
– Small and medium size dimension tables (< 2-3M)
• EVEN
– When key cannot produce an even distribution
28. Storage Deep Dive: Disks
• Redshift utilizes locally attached storage devices
• Compute nodes have 2.5-3x the advertised storage capacity
• 1, 3, 8, or 24 disks depending on node type
• Each disk is split into two partitions
– Local data storage, accessed by local CN
– Mirrored data, accessed by remote CN
• Partitions are raw devices
– Local storage devices are ephemeral in nature
– Tolerant to multiple disk failures on a single node
29. Storage Deep Dive: Blocks
• Column data is persisted to 1MB immutable blocks
• Each block contains in-memory metadata:
– Zone Maps (MIN/MAX value)
– Location of previous/next block
• Blocks are individually compressed with 1 of 10 encodings
• A full block contains between 16 and 8.4 million values
30. Storage Deep Dive: Columns
• Column: Logical structure accessible via SQL
• Physical structure is a doubly linked list of blocks
• These blockchains exist on each slice for each column
• All sorted & unsorted blockchains compose a column
• Column properties include:
– Distribution Key
– Sort Key
– Compression Encoding
• Columns shrink and grow independently, 1 block at a time
• Three system columns per table-per slice for MVCC
31. Block Properties: Design Considerations
• Small writes:
• Batch processing system, optimized for processing massive amounts of data
• 1MB size + immutable blocks means that we clone blocks on write so as not to
introduce fragmentation
• Small write (~1-10 rows) has similar cost to a larger write (~100 K rows)
• UPDATE and DELETE:
• Immutable blocks means that we only logically delete rows on UPDATE or DELETE
• Must VACUUM or DEEP COPY to remove ghost rows from table
32. Column Properties: Design Considerations
• Compression:
• COPY automatically analyzes and compresses data when loading into empty tables
• ANALYZE COMPRESSION checks existing tables and proposes optimal
compression algorithms for each column
• Changing column encoding requires a table rebuild
• DISTKEY and SORTKEY significantly influence performance (orders of magnitude)
• Distribution Keys:
• A poor DISTKEY can introduce data skew and an unbalanced workload
• A query completes only as fast as the slowest slice completes
• Sort Keys:
• A sortkey is only effective as the data profile allows it to be
• Selectivity needs to be considered
34. Storage Deep Dive: Slices
• Each compute node has either 2, 16, or 32 slices
• A slice can be thought of like a “virtual compute node”
– Unit of data partitioning
– Parallel query processing
• Facts about slices:
– Table rows are distributed to slices
– A slice processes only its own data
– Within a compute node all slices read from and write to all disks
37. Query Execution Terminology
• Step: An individual operation needed during query execution. Steps need to be
combined to allow compute nodes to perform a join. Examples: scan, sort,
hash, aggr
• Segment: A combination of several steps that can be done by a single process.
The smallest compilation unit executable by a slice. Segments within a stream
run in parallel.
• Stream: A collection of combined segments which output to the next stream or
SQL client.
39. client
JDBC ODBC
Leader Node
Parser
Query Planner
Code Generator
Final Computations
Generate code for
all segments of
one stream
Explain Plans
Compute Node
Receive Compiled Code
Run the Compiled Code
Return results to Leader
Compute Node
Receive Compiled Code
Run the Compiled Code
Return results to Leader
Return results to client
Segments in a stream are
executed concurrently.
Each step in a segment is
executed serially.
Query Lifecycle
40. Query Execution Deep Dive: Leader Node
1. The leader node receives the query and parses the SQL.
2. The parser produces a logical representation of the original query.
3. This query tree is input into the query optimizer (volt).
4. Volt rewrites the query to maximize its efficiency. Sometimes a single query will be
rewritten as several dependent statements in the background.
5. The rewritten query is sent to the planner which generates >= 1 query plans for the
execution with the best estimated performance.
6. The query plan is sent to the execution engine, where it’s translated into steps,
segments, and streams.
7. This translated plan is sent to the code generator, which generates a C++ function
for each segment.
8. This generated C++ is compiled with gcc to a .o file and distributed to the compute
nodes.
41. Query Execution Deep Dive: Compute Nodes
• Slices execute the query segments in parallel.
• Executable segments are created for one stream at a time. When the segments
of that stream are complete, the engine generates the segments for the next
stream.
• When the compute nodes are done, they return the query results to the leader
node for final processing.
• The leader node merges the data into a single result set and addresses any
needed sorting or aggregation.
• The leader node then returns the results to the client.
45. Recently Released Features
New Data Type - TIMESTAMPTZ
• Support for Timestamp with Time zone : New TIMESTAMPTZ data type to input complete
timestamp values that include the date, the time of day, and a time zone.
Eg: 30 Nov 07:37:16 2016 PST
Multi-byte Object Names
• Support for Multi-byte (UTF-8) characters for tables, columns, and other database object
names
User Connection Limits
• You can now set a limit on the number of database connections a user is permitted to have
open concurrently
Automatic Data Compression for CTAS
• All newly created tables will leverage default encoding
46. Amazon Redshift Workload Management
Waiting
BI tools
SQL clients
Analytics tools
Client
Running
Queue 1
Queue 2
4 Slots
2 Slots
Short queries go to
the head of the
queue
1
1
Coming Soon: Short Query Bias
47. Amazon Redshift Cluster
BI tools
SQL clients
Analytics tools
Client
Leader node
Compute node
Compute node
Compute node
2
2
2
2
All queries receive a
power start. Shorter
queries benefit the
most
Coming Soon: Power Start
48. Monitor and
control cluster
resources
consumed by a
query
Get notified, abort
and reprioritize
long-running / bad
queries
Pre-defined
templates for
common use cases
Coming Soon: Query Monitoring Rules
49. BI tools SQL clientsAnalytics tools
Client AWS
Redshift
ADFS
Corporate
Active Directory IAM
Amazon Redshift
ODBC/JDBC
User groups Individual user
Single Sign-On
Identity providers
New Redshift
ODBC/JDBC
drivers. Grab the
ticket (userid) and
get a SAML
assertion.
Coming Soon: IAM Authentication
50. Coming Soon: Lots More …
Automatic and Incremental Background VACUUM
• Reclaims space and sorts when Redshift clusters are idle
• Vacuum is initiated when performance can be enhanced
• Improves ETL and query performance
Automatic Compression for New Tables
• All newly created tables will leverage default encoding
• Provides higher compression rates
New Functions
• Approximate Percentile
010101010101
51. Resources
• https://github.com/awslabs/amazon-redshift-utils
• https://github.com/awslabs/amazon-redshift-monitoring
• https://github.com/awslabs/amazon-redshift-udfs
• Admin scripts
Collection of utilities for running diagnostics on your cluster
• Admin views
Collection of utilities for managing your cluster, generating schema DDL, etc.
• ColumnEncodingUtility
Gives you the ability to apply optimal column encoding to an established schema with
data already loaded
• Amazon Redshift Engineering’s Advanced Table Design Playbook
https://aws.amazon.com/blogs/big-data/amazon-redshift-engineerings-advanced-table-
design-playbook-preamble-prerequisites-and-prioritization/