Scalibility

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This document discusses various techniques for scaling systems, including vertical scaling by increasing a single server's resources, horizontal scaling by adding more servers behind a load balancer, and database scaling techniques like replication, load balancing, partitioning, and sharding. It also covers caching with Memcached and Redis, high availability, and data center and availability zone-based load balancing.

Engineering

THANKYOU David Malan teaching CS75 lecture 9, Scalability.
http://www.cs75x.net/

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Amazon SageMaker는 머신러닝 프로젝트를 위한 통합 플랫폼입니다. SageMaker의 기능 중 Amazon SageMaker Studio는 머신러닝 통합 개발환경을 제공하여, 데이터를 준비에서부터 모델을 빌드, 교육 및 배포하는 데 필요한 모든 단계를 수행할 수 있습니다. Amazon EMR은 Apache Spark, Apache Hive 및 Presto와 같은 오픈 소스 분석 프레임워크를 사용하여 대규모 분산 데이터 처리 작업, 대화형 SQL 쿼리 및 ML 애플리케이션을 실행하기 위한 빅 데이터 플랫폼입니다. 이 세션에서는 데이터 과학자와 ML 엔지니어가 ML 워크플로우에서 분산 빅 데이터 프레임워크를 쉽게 사용할 수 있도록 상호 서비스 간의 통합에 대하여 데모를 통해 알아봅니다.

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Apache Airflow는 복잡한 데이터 처리 파이프라인의 전체적인 프로세스를 자동화하기 위한 워크플로우 관리 플랫폼이며 오픈 소스 커뮤니티에서 활발하게 기여하고 있는 top-level 프로젝트 입니다. AWS는 최근에 Amazon Managed Workflow for Apache Airflow (MWAA) 서비스를 정식 출시하였고, 본 강연에서는 Apache Airflow 및 MWAA를 소개하고 어떻게 AWS 서비스와 연동하여 데이터 처리 워크플로우를 구축할 수 있는지 데모를 통해 알려 드립니다.

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기업들은 데이터로부터 insight를 얻기 위해서 부단한 노력을 하고 있습니다. 이를 위해 조직의 데이터를 한 곳에 모아서 보관하는 Data Lake의 구축은 데이터 분석을 위한 중심으로 자리잡고 있습니다. 본 세션에서는 AWS에서 S3를 활용하여 민첩하고 비용효율적인 Data Lake를 구축하는 방법을 소개합니다. 또한 이를 기반으로 AWS의 다양한 데이터 분석 서비스와 연동하는 법을 살펴봅니다. 대상 : 빅 데이터 및 데이터 분석 담당자, AWS 기반 데이터 분석에 관심 있는 모든 분 발표자 : 문종민 솔루션즈 아키텍트, AWS

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-GPU로 가속된 초-거대(>1PB) 정형데이타 분석에 최적화된 SQL DB -대규모 확장성 -SQL 데이타베이스 -ML/AL로 확장 -RDBMS: Oracle,DB2,SQL Server,Informix,Sybase ASE -MPP(EDW):Teradata, MongoDB,Vertica,Redshift,Oracle-Exadata,IBM-Netezza,Greenplum,Sybase IQ -In-Memory: Omnisci(MapD), inetica Aerospike,SAP HANA,VoltaDB,IBM-DB2 BLU,MemSQL,Altibase -Massive Data: SQreamDB,Hadoop-Hive,Snowflake,BigQuery -NO-SQL: 1) WIDE COLUMN DATABASE(Store) Hbase, GoogleBigTable, Vertica, Druid, Accumulo, Hypertable 2) GRAPH DATABASE Neo4j, Blazegraph, OrientDB 3) DOCUMENT DATABASE(Store) MongoDB, Azure CosmosDB, CouchDB, MarkLogic, OrientDB 4) KEY-VALUE DATABASE(Store) Cassandra, LevelDBRocksDB, Redis, Oracle NoSQLDB,Voldemorte, Oracle BerkeleyDB, Memcached, Hazelcast

SQream-GPU가속 초거대 정형데이타 분석용 SQL DB-제품소개-박문기@메가존클라우드

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Apache Spark is a popular in-memory data analytics engine because of its speed, scalability, and ease of use. It also fits well with DevOps practices and cloud-native software platforms. It’s good for data exploration, interactive analytics, and streaming use cases. However, Spark, like other data-processing platforms, is not one size fits all. Different versions of Spark support different feature sets, and Spark’s machine-learning libraries can also vary in important ways between versions, or may lack the right algorithm. In this webinar, you’ll learn: - How to integrate data warehouse workloads with Spark - Which workloads are better for Greenplum and for Spark - How to use the Greenplum-Spark connector Presenter: Kong Yew Chan, Product Manager, Pivotal

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VMware Tanzu

Organizations need to perform increasingly complex analysis on their data — streaming analytics, ad-hoc querying and predictive analytics — in order to get better customer insights and actionable business intelligence. However, the growing data volume, speed, and complexity of diverse data formats make current tools inadequate or difficult to use. Apache Spark has recently emerged as the framework of choice to address these challenges. Spark is a general-purpose processing framework that follows a DAG model and also provides high-level APIs, making it more flexible and easier to use than MapReduce. Thanks to its use of in-memory datasets (RDDs), embedded libraries, fault-tolerance, and support for a variety of programming languages, Apache Spark enables developers to implement and scale far more complex big data use cases, including real-time data processing, interactive querying, graph computations and predictive analytics. In this session, we present a technical deep dive on Spark running on Amazon EMR. You learn why Spark is great for ad-hoc interactive analysis and real-time stream processing, how to deploy and tune scalable clusters running Spark on Amazon EMR, how to use EMRFS with Spark to query data directly in Amazon S3, and best practices and patterns for Spark on Amazon EMR.

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규모의 경제에서 속도의 경제로 이동하고 있는 요즘, 기업의 경쟁우위를 위하여 클라우드 마이그레이션과 애플리케이션 및 프로세스 현대화는 생존을 위한 필수조건이 되었습니다. 본 세션에서는 마이그레이션과 현대화를 위한 30여 가지 아이디어를 살펴보고자 합니다. 제약 이론, 도요타 생산방식과 같이 개선을 위한 기반 사상에서 DevOps, FinOps, SRE 등의 프랙티스와 그를 구성하는 SLA, Loosely Couple Architecture, CI/CD 등의 메카니즘, 그리고 마이그레이션을 위한 Transitional Architecture, Data Migration Architecture 등 30여 가지 아이디어를 공유합니다.

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Haodong Tang from Intel gave this talk at the 2018 Open Fabrics Workshop. "Efficient network messenger is critical for today’s scale-out storage systems. Ceph is one of the most popular distributed storage system providing a scalable and reliable object, block and file storage services. As the explosive growth of Big Data continues, there're strong demands leveraging Ceph build high performance & ultra-low latency storage solution in the cloud and bigdata environment. The traditional TCP/IP cannot satisfy this requirement, but Remote Direct Memory Access (RDMA) can. "In this session, we'll present the challenges in today's distributed storage system posed by network messenger with the profiling results of Ceph All Flash Array system showing the networking already become the bottleneck and introduce how we achieved 8% performance benefit with Ethernet RDMA protocol iWARP. We'll first present the design of integrating iWARP to Ceph networking module together with performance characterization results with iWARP enabled IO intensive workload. The send part, we will explore the proof-of-concept solution of Ceph on NVMe over iWARP to build high-performance and high-density storage solution. Finally, we will showcase how these solutions can improve OSD scalability, and what’s the next optimization opportunities based on current analysis." Watch the video: https://wp.me/p3RLHQ-ikV Learn more: http://intel.com and https://insidehpc.com/2018/04/amazon-libfabric-case-study-flexible-hpc-infrastructure/ Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter

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HDFS has several strengths: horizontally scale its IO bandwidth and scale its storage to petabytes of storage. Further it provides very low latency metadata operations and scales to over 60K concurrent clients. One of its limitations is scaling number of files and blocks in the system. We describe a radical change to Hadoop’s storage infrastructure with the upcoming Ozone technology. It allows Hadoop to scale to tens of billions of files and blocks and, in the future, to every larger number of smaller objects. Ozone fundamentally separates the namespace layer and the block layer allowing new namespace layers to be added in the future. Further, the use of RAFT protocol has allowed the storage layer to be self-consistent. It also means that the HA solution for the Metadata server (the Keyspace manager/future NN). We show how this technology helps a Hadoop user and also what it means for evolving HDFS in the future. We will also cover the technical details of Ozone. Speaker Sanjay Radia, Chief Architect and Founder, Hortonworks

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AWS 클라우드는 IT의 새로운 기준을 정립하며 클라우드 컴퓨팅 산업을 혁신하고 있습니다. 본 온라인 세미나에서는 클라우드 컴퓨팅의 개념과 AWS가 제공하는 서비스 소개 및 주요 활용 사례에 대해 소개합니다. 특히 국내에 설립된 서울 리전(Region, 데이터센터 클러스터)에 대한 소개와 더불어 다양한 IT 업무를 위한 AWS 대표 서비스들을 중점적으로 다룰 예정입니다.

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Tomer Shiran est le fondateur et chef de produit (CPO) de Dremio. Tomer était le 4e employé et vice-président produit de MapR, un pionnier de l'analyse du Big Data. Il a également occupé de nombreux postes de gestion de produits et d'ingénierie chez IBM Research et Microsoft, et a fondé plusieurs sites Web qui ont servi des millions d'utilisateurs. Il est titulaire d'un Master en génie informatique de l'Université Carnegie Mellon et d'un Bachelor of Science en informatique du Technion - Israel Institute of Technology. Le Modern Data Stack meetup est ravi d'accueillir Tomer Shiran. Depuis Apache Drill, Apache Arrow maintenant Apache Iceberg, il ancre avec ses équipes des choix pour Dremio avec une vision de la plateforme de données “ouverte” basée sur des technologies open source. En plus, de ces valeurs qui évitent le verrouillage de clients dans des formats propriétaires, il a aussi le souci des coûts qu’engendrent de telles plateformes. Il sait aussi proposer un certain nombre de fonctionnalités qui transforment la gestion de données grâce à des initiatives telles Nessie qui ouvre la route du Data As Code et du transactionnel multi-processus. Le Modern Data Stack Meetup laisse “carte blanche” à Tomer Shiran afin qu’il nous partage son expérience et sa vision quant à l’Open Data Lakehouse.

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Digital Communication: Digital communication refers to the transmission of information using digital signals, where data is represented by discrete symbols or values. It encompasses various techniques for encoding, transmitting, and decoding digital data, including text, images, audio, and video. Digital communication systems offer advantages such as improved signal quality, noise immunity, and the ability to multiplex multiple data streams. Differential Pulse Code Modulation (DPCM): DPCM is a digital modulation technique used for encoding analog signals into digital form. In DPCM, the difference between the current sample and the predicted value of the sample is quantized and encoded, rather than encoding the absolute sample value directly. This technique can reduce the amount of data required for transmission compared to standard pulse code modulation (PCM), especially for signals with correlation between successive samples. Delta Modulation (DM): Delta modulation is a simple form of analog-to-digital conversion where the difference (delta) between the current sample and the previous sample is quantized and encoded. In DM, only the sign of the difference (positive or negative) is transmitted, which simplifies the encoding process. However, DM may suffer from quantization noise and limited signal fidelity, especially for rapidly changing or high-frequency signals. Adaptive Delta Modulation (ADM): Adaptive delta modulation is an extension of delta modulation that dynamically adjusts the step size or quantization level based on the characteristics of the input signal. Unlike fixed-step delta modulation, ADM can adapt to changes in signal amplitude or frequency, resulting in improved signal fidelity and reduced quantization error. ADM is particularly useful for encoding signals with varying dynamics or signal-to-noise ratios

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Scalibility

1. Scalability - OmVikramThapa

2. URL

3. DNS

4. DNS

5. BIND

6. ROUTE 53

7. Route 53 + ELB

8. SCALABILITY TYPES

9. VERTICAL SCALING

10. RAID 0-10

11. RAID 10

12. HORIZONTAL SCALING

13. LOAD BALANCER

14. STICKY SESSIONS

15. CACHING MEMCACHED

16. CACHING REDIS

17. HIGH AVAILABILITY CACHE

18. DATABASE

19. DATABASE

20. MASTER MASTER REPLICATION

21. DB LOAD BALANCER

22. DB PARTITIONING

23. DB SHARDING

24. DB SHARDING

25. DATA CENTER

26. AZ - GEO BASED LOAD BALANCING

27. THANKYOU David Malan teaching CS75 lecture 9, Scalability. http://www.cs75x.net/

Scalibility

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