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bhavaniv · 3 years ago

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Clusters and nodes in Amazon Redshift-Visualpath

An Amazon Redshift cluster include nodes. Each cluster has a pacesetter node and one or greater compute nodes. The chief node gets queries from customer applications, parses the queries, and develops question execution plans. The chief node then coordinates the parallel execution of those plans with the compute nodes and aggregates the intermediate outcomes from those nodes. It then ultimately returns the outcomes again to the customer applications. Amazon Redshift Certification Online Training Compute nodes run the question execution plans and transmit records amongst themselves to serve those queries. The intermediate consequences are dispatched to the chief node for aggregation earlier than being dispatched lower back to the purchaser applications. For greater records approximately chief nodes and compute nodes, see Data warehouse machine structure within side the Amazon Redshift Database Developer Guide. When you release a cluster, one alternative which you specify is the node kind. The node kind determines the CPU, RAM, garage capacity, and garage force kind for every node. Amazon Redshift gives extraordinary node sorts to deal with your workloads, and we advocate selecting RA3 or DC2 relying on the desired performance, facts size, and predicted facts growth. Amazon Redshift Online Training RA3 nodes with controlled garage permit you to optimize your information warehouse via way of means of scaling and buying compute and controlled garage independently. With RA3, you select the range of nodes primarily based totally for your overall performance necessities and simplest pay for the controlled garage which you use. Size your RA3 cluster primarily based totally on the quantity of information you system daily. You release clusters that use the RA3 node kinds in a digital non-public cloud (VPC). You cannot release RA3 clusters in EC2-Classic. For greater information, see creating a cluster in a VPC. Amazon Redshift course RA3 nodes with controlled garage permit you to optimize your information warehouse via way of means of scaling and buying compute and controlled garage independently. With RA3, you select the range of nodes primarily based totally for your overall performance necessities and simplest pay for the controlled garage which you use. Size your RA3 cluster primarily based totally on the quantity of information you system daily. You release clusters that use the RA3 node kinds in a digital non-public cloud (VPC). You cannot release RA3 clusters in EC2-Classic. For greater information, see creating a cluster in a VPC. DC2 nodes allow you to have compute-extensive information warehouses with neighborhood SSD garage included. You pick out the variety of nodes you want primarily based totally on information length and overall performance requirements. DC2 nodes save your information regionally for excessive overall performance, and because the information length grows, you could upload extra compute nodes to growth the garage potential of the cluster. For datasets below 1 TB (compressed), we propose DC2 node sorts for the quality overall performance at the bottom rate. If you count on your information to grow, we propose the usage of RA3 nodes so that you can length compute and garage independently to acquire progressed rate and overall performance. You release clusters that use the DC2 node sorts in a digital personal cloud (VPC). You cannot release DC2 clusters in EC2-Classic. For extra information, see creating a cluster in a VPC. For More Information Click Here Contact Us +91-9989971070

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swanandcmiprs · 5 years ago

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STORAGE IN BIG DATA MARKET ANALYSIS (2019-2027)

Market Overview

Big data storage refers to a compute and storage architecture that gathers and operates vast data sets and allows real-time data analytics. Many companies employ big data analytics to collect greater intelligence from metadata. Big data storage allows the storage and sorting of big data in such a way that it can be easily used, accessed, and processed by applications and services working on big data. Moreover, big data can be flexibly scaled as required. Many end-use industries employ big data storage including BFIS, media and entertainment, IT and telecommunications, healthcare and medical, transportation, logistics, retail, etc.

The global Storage in Big Data Market was accounted for US$ 17,391.4 Mn in terms of value in 2019 and is expected to grow at CAGR of 20.4% for the period 2020-2027

Market Dynamics- Drivers

Increasing adoption of software-based storage options is expected to drive growth of the global storage in big data market during the forecast period

In software-based storage solution, the storage controller software is disassociated from hardware and takes advantage of industry-standard hardware platforms, in order to deliver a complete range of storage services. This allows different solutions for data storage, data access interfaces, services, and can be delivered in various forms including on cloud. According to Intel Corporation’s study 2016, enterprises are shifting towards software-based storage as performance, capital expenses and scaling are the top three factors considered by data center managers. However, there are several approaches that can be used while deploying software-based storage such as Do-It-Yourself solutions, turnkey solutions and converged and hyper-converged solutions. Hence, these factors are expected to support growth of the global storage in the big data market in the near future.

Rising digitization of records supported by governments is expected to propel the global storage in big data market growth over the forecast period

Presence of stringent laws enforced by governments of various countries has led to a massive shift of many companies towards digital maintenance of records, especially in healthcare industry. Increasing digital data volumes is leading to companies increasingly adopting various data storage options. For instance, laws such as American Recovery and Reinvestment Act and Health Information Technology for Economic and Clinical Health (HITECH) Act has been promoting adoption of digital storage of records by hospitals and clinics in the U.S. Thus, these factors are expected to boost the market growth over the forecast period.

North America region dominated the global Storage in Big Data Market in 2019, accounting for 47.4 % share in terms of value, followed by Europe, Asia Pacific, Middle East and Africa and Latin America.

Market Dynamics- Restraints

High total cost for ownership of flash storage is expected to restrain growth of the global storage in big data market during the forecast period

Flash provides substantial increase in latency and input/output performance as compared to hard disk drive. As a result of this, popularity of systems and servers with flash storage for different range of workloads has been increasing significantly in the recent past. However, as the total cost of ownership of an SSD (solid-state drive) is relatively higher than a HHD (hard disk drive), adoption rate of flash storage in small scale enterprises continues to remain relatively low. Therefore, these factors are expected to restrict growth of the market in the near future.

Reduced budget for data storage is expected to hinder the global storage in big data market growth over the forecast period

Advancements in technology have made a positive impact on economic conditions in developed as well as emerging economies. However, there are various macroeconomic factors that can adversely affect growth of the big data storage market. For instance, economic downturn can significantly impact investments on high and external storage systems. As a result of reduced storage budget, companies are seeking for most cost-efficient and effective methods to store big data. Thus, these factors are expected to hinder the global storage in big data market growth over the forecast period.

Market Opportunity

Providing intelligent and dynamic big data storage platforms can present major business opportunities

Majority of organizations are focused on keeping track of all historical data, deleting duplicate entries, and gathering all data. For this purpose, scalable intelligent storage system is required to store exabytes of data, which will help in detecting duplicate entries, and enable swift retrieval of information. Thus, storage system providers should lay emphasis on improving their product offerings based on changing consumer requirements.

Offering storage servers close to end users to reduce latency time can provide significant growth opportunities

Establishing storage servers within proximity of end user location can make it easier to retrieve data thereby reducing latency time. Various industries such as media and communication can gain advantages from this setting by saving content in different geographical locations based on popularity of a certain channel or program.

Market Trends

Availability of storage convergence on hybrid cloud

Hybrid cloud storage is the most realistic option available for data storage, particularly for projects that include a large number of data points. Enterprises collect data from different sources including data that is stored over the public cloud, private cloud and data centers, which is then aggregated and moved from one location to another. For instance, in September 2016, IBM Corporation launched z system storage and software and expanded open software ecosystem to accelerate hybrid cloud integration. Moreover, in August 2016, VMware, Inc. and IBM Corporation extended their partnership to enable easy hybrid cloud adoption. This has enabled over 500 of their clients to extend their existing workloads and apps to the cloud in relatively short periods of time.

Growing utilization of erasure coding for big data management

Due to increasing adoption of cloud-based storage and high capacity hard disk drives, exabytes of data need to be examined and stored can be done with the help of erasure code technology. Erasure codes are widely used to protect against catastrophic failures, such as total loss of data. For instance, in February 2016, Microsoft Corporation published a whitepaper highlighting the benefits of using of new set of codes for erasure coding called Local Reconstruction Codes (LRC), which the company is implementing in Windows Azure Storage (WAS).

Segment information:

In global Storage in Big Data Market, by Segment, hardware sub-segment dominated the global market in 2019, accounting for 44.3% share in terms of value, followed by Software and Services respectively.

Competitive Section

Key players operating in the global storage in big data market are MemSQL Inc., Google Inc., Hitachi Data Systems Corporation, Microsoft Corporation, Hewlett Packard Enterprise, Amazon Web Services, Inc., Teradata Corporation, VMware, Inc., SAP SE, IBM Corporation, Oracle Corporation, Dell EMC, and SAS Institute Inc.

Key Developments

Major companies in the market are focused on business expansion, in order to gain competitive edge in the market. For instance, in September 2017, Hitachi Data Systems Corporation created an IoT focused company name Hitachi Vantara.

Key players in the market are involved in product launches, in order to enhance product portfolio. For instance, in December 2019, Amazon Web Services introduced Redshift RA3 to let customers scale computer and storage separately.

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un-enfant-immature · 6 years ago

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AWS speeds up Redshift queries 10x with AQUA

At its re:Invent conference, AWS CEO Andy Jassy today announced the launch of AQUA (the Advanced Query Accelerator) for Amazon Redshift, the company’s data warehousing service. As Jassy noted in his keynote, it’s hard to scale data warehouses when you want to do analytics over that data. At some point, as your data warehouse or lake grows, the data starts overwhelming your network or available compute, even with today’s highspeed networks and chips. So to handle this, AQUA is essentially a hardware-accelerated cache and promises up to 10x better query performance than competing cloud-based data warehouses.

“Think about how much data you have to move over the network to get to your compute,” Jassy said. And if that’s not a problem for a company today, he added, it will likely become one soon, given how much data most enterprises now generate.

With this, Jassy explained, you’re bringing the compute power you need directly to the storage layer. The cache sits on top of Amazon’s standard S3 service and can hence scale out as needed across as many nodes as needed.

AWS designed its own analytics processors to power this service and accelerate the data compression and encryption on the fly.

Unsurprisingly, the service is also 100% compatible with the current version of Redshift.

In addition, AWS also today announced next-generation compute instances for Redshift, the RA3 instances, with 48 vCPUs and 384GiB of memory and up to 64 TB of storage. You can build clusters of these with up to 128 instances.

#TechCrunch

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autosignal247 · 5 years ago

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globalmediacampaign · 5 years ago

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How Zendesk tripled performance by moving a legacy system onto Amazon Aurora and Amazon Redshift

This is a guest post by James Byrne, Engineering Leader at Zendesk, focusing on data pipeline development and operations for the Zendesk Explore analytics product, and Giedrius Praspaliauskas, AWS Solutions Architect. Zendesk is a CRM company that builds support, sales, and customer engagement software designed to foster better customer relationships. From large enterprises to startups, we believe that powerful, innovative customer experiences should be within reach for every company, no matter the size, industry, or ambition. Zendesk serves more than 150,000 customers across a multitude of industries in over 30 languages. Zendesk is headquartered in San Francisco and operates 17 offices worldwide. Zendesk Explore provides analytics for businesses to measure and improve the entire customer experience. With Zendesk Explore, businesses get instant access to the customer analytics that matter and a deeper understanding of their customers and business that comes with it. This post discusses how we moved our legacy system onto Amazon Aurora and Amazon Redshift. We detail the process and architecture that allowed us to build a new data store and triple performance. Deciding to migrate In 2015, Zendesk acquired Business Intelligence startup, BIME Analytics. The BIME product served as the building blocks for our current reporting product, Zendesk Explore. Zendesk Explore processes and analyzes multiple data types from various Zendesk products, such as Zendesk Support, Talk, Chat, and Guide. It extracts data from each product and denormalizes, transforms, and loads it into a datastore. A visualization layer sits on top of this datastore, which provides Zendesk customers with a user interface to access the data for analysis. Users can create their own data visualizations and dashboards by simply pointing and clicking. When the Zendesk team set out to build the foundations for Explore, we began by looking at the tools available to implement data extract, transform, and load (ETL) and analytics in AWS. We focused on Amazon Aurora PostgreSQL to handle the amount of data we had, and Amazon Redshift for larger-scale datasets and fast analytical queries. We could connect to the products and APIs that we needed to extract data from, and denormalize data for better performance. Within a year, we could build a full ETL pipeline using Aurora PostgreSQL for our customers up to a certain size. After extensive load, stress, and performance testing, we hit our sweet spot at around 60 million customer tickets per single Aurora cluster (running at 80% of CPU). We knew that a small percentage of our biggest customers’ datasets would not be a good fit for Aurora PostgreSQL, because we run data transformations in parallel to complex queries, and a tool optimized for ETL and complex analytics would be a better fit for that pattern at the largest scale. We use Amazon Redshift as a backend data storage and querying solution for those customers. This approach allowed us to handle the load in the most cost-effective manner, even in multi-tenant implementation, in which multiple customers of various sizes share an underlying Amazon Redshift or Aurora cluster. Approach The following diagram shows, at the high level, how the Zendesk Explore team implemented data ingestion, transformation, load, and analytics. The following services perform various functions: Amazon EMR, AWS Step Functions, and AWS Lambda ingest and transform static data from Zendesk Incremental APIs Apache Flink, Amazon Kinesis, and Amazon ECS ingest near-real-time data from database binlogs Aurora and Amazon Redshift serve as data storage Amazon ECS hosts a custom query engine that users access through Elastic Load Balancing Data ingestion Zendesk Explore ingests data from two main sources: public APIs for static data (older than 1 hour) and near-real-time log stream (10 seconds–1 hour). We have a scheduled process to query Incremental Export API endpoints, which pulls data modified since the last run. The Explore ETL process, which runs on Amazon EMR, consumes the data. Log stream originates as a database binlog that is streamed to Amazon Kinesis using Maxwell and processed or aggregated on the way using Apache Flink. The data is aggregated into bins, which the Explore ETL process picks up every 10 seconds, and stored in the Aurora cluster. ETL Zendesk Explore runs ETL for thousands of customers on a per-customer basis (for each customer, some ETL logic is executed). We can transform hundreds of millions of rows into thousands of records with only a few columns. For example, if a customer has 10 million records (tickets updates), we join them during transformation with other tables, aggregate the data, and present this aggregation in just a thousand records. Our legacy ETL process used Scala and SQL queries that ran data transformation on the PostgreSQL cluster. As part of refactoring, we moved legacy implementation of the data loading and transformations to Spark on Amazon EMR to offload that processing to the tool that is more suitable for ETL. This way, we could dedicate Aurora and Amazon Redshift completely to data storage and querying. This allowed us to host multiple tenants on the clusters without degrading performance by running data transformations in parallel. This approach helped us co-locate up to 600 tenants on a single Aurora cluster, compared to the initial limit of 100–200. We use Step Functions and Lambda to translate the data transformation process into a large graph. This triggers data transformation steps that execute as Apache Spark applications that run on Amazon EMR. This process repeats every hour for every customer to process data retrieved using Incremental APIs. Data storage Explore uses Aurora PostgreSQL and Amazon Redshift for data storage. We base the decision of which storage solution to use on the customer dataset size, usage patterns, and resulting performance. Aurora PostgreSQL hosts small and medium customers (up to 3–6 million tickets). Amazon Redshift hosts large customers. We use query performance tracing and look at the performance (customer wait time) of the core queries using internal administration tools. As customers’ datasets grow, they may move from one data storage to another. Both Aurora PostgreSQL and Amazon Redshift use a multi-tenant approach with up to hundreds of customers co-located on a single cluster. This approach allows cost-effective storage of customer data without affecting query performance. Also, co-locating multiple customers on a single large Amazon Redshift cluster allows better parallel query performance needed for the dashboards, in which multiple parallel queries are running for a single web page. Analytics Zendesk Explore provides dashboards for both static and near-real-time data. You can use prebuilt dashboards, visualizations, and queries, or use the query builder to build visualizations with metrics you choose using data across multiple tables. To optimize these queries, Zendesk Explore uses an intermediate custom application layer that can rewrite queries, route queries to the different tables based on the predicates used, and also has an application cache. This query engine is written in Scala and runs on an ECS cluster. Summary This post walked you through Zendesk’s implementation of a multi-tenant analytics solution. It showed how you can use multiple databases and analytics solutions to serve datasets of varying sizes and temperature cost-efficiently and flexibly. You can evolve this approach further by using the latest developments AWS announced at re:Invent 2019—the Amazon Redshift Federated Query feature, Advanced Query Accelerator (AQUA) for Amazon Redshift, and Amazon Redshift RA3 instances with managed storage. If you have questions or suggestions, please leave your thoughts in the comments. https://probdm.com/site/MjA3MDY

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selfhowcom · 5 years ago

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Amazon Redshift, ra3.4xlarge 인스턴스 추가 업데이트 (서울 리전 포함)

#셀프하우 #lens.selfhow.com #Amazon Web Services 한국 블로그

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oom-killer · 5 years ago

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2020/03/30-31, 2020/04/01-05

*Zoom、パスワード強化と「待機室」追加　“Zoombombing”対策で https://www.itmedia.co.jp/news/articles/2004/05/news009.html >なお、待機室機能についてはカナダのトロント大学の研究者が脆弱性を >Zoomに指摘しており、Zoomがこの脆弱性に対処するまでは待機室機能を >無効にし、パスワードだけを使うようユーザーに勧めて��る（関連記事）。

>Zoomによると、2019年12月末の時点で約1000万人だったユーザー数が、 >3月には2億人以上に増加したという。

*Lambda で Redshift から S3 に UNLOAD する https://yohei-a.hatenablog.jp/entry/20200405/1586053151

*Python から Redshift に接続してみる https://yohei-a.hatenablog.jp/entry/20200403/1585878053

*EC2インスタンスの３つの死活監視 http://blog.serverworks.co.jp/tech/2020/04/03/ec2/ >状態には、 >pending、running、stopping、stopped、shutting-down、terminated >があります。

>リタイアの監視設定 > 突然の障害だと仕方がないのですが、事前にわかっている場合は、 > メール通知があります。

>EC2インスタンスに対して、まずは３種類の監視を行いましょう。 > > - 状態 > - ステータス > - リタイア

*DB インスタンスのフェイルオーバーが発生しました。原因を特定する方法を教えてください。 https://dev.classmethod.jp/articles/tsnote-support-rds-failover-001/ >Service Health Dashboard の確認 >Personal Health Dashboard の確認 >AWS レイヤーの確認 >上位レイヤーの確認 >上記すべて確認したが原因不明の場合

*Amazon EC2 インスタンスに構築した環境への負荷テストを実施するには申請が必要でしょうか？ https://dev.classmethod.jp/articles/tsnote-support-ec2-testing/ >申請が必要なケース > > - 1 分以上連続して 1Gbps(bits per second) を超えるトラフィックを生成する > - 1Gpps(packets per second) を超えるトラフィックを生成する > - 悪意を持った、もしくは迷惑行為にみえるようなトラフィックを生成する > - 予期されるターゲット以外のエンティティ(ルーティングや共有サービス基盤) > に対し潜在的に影響のあるトラフィックを生成する > >申請が不要なケース > > - 1Gbps 以下のトラフィック > - 本番環境、商用環境あるいはそれに準ずるトラフィック > - 本番環境のワークロードに準ずる負荷テスト(ネットワーク負荷による > 試験が主目的ではない場合)

*Amazon Redshift 新型『RA3』に1/4のスケールのra3.4xlarge インスタンスが追加になりました https://dev.classmethod.jp/articles/20200403-amazon-redshift-new-ra3_4xlarge/ >RA3ノードタイプはAWS Nitroと階層ストレージ採用した新インスタンスで、 >最新のホットデータを高性能SSDストレージにキャッシュし、参照頻度の >低いコールドデータをS3に自動的にすることで、��フォーマンスとコスト >を両立したノードタイプです。

*Red Hat 社から公式に提供されている Red Hat Enterprise Linux (RHEL) の AMI の ID の見つけ方を教えてください https://dev.classmethod.jp/articles/tsnote-support-ec2-linux-rhel-001/ >コンソールを使用して見つける > > 1. https://console.aws.amazon.com/ec2/ で Amazon EC2 コンソールを開きます。 > 2. ナビゲーションペインで [AMIs] を選択します。 > 3. 最初のフィルタで、[パブリックイメージ] を選択します。 > 4. 検索バーで、 "所有者:309956199498" 選択します。

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