Amazon Kinesis Data Streams
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Amazon Kinesis Data Streams is a scalable, long-lasting, and low-cost streaming data solution from Amazon. Kinesis Data Streams can gather terabytes of data every second from tens of thousands of sources, including internet clickstreams, database event streams, financial transactions, social media feeds, IT logs, and location-tracking events. Real-time dashboards, real-time anomaly detection, and dynamic pricing are all possible with the collected data, which is available in milliseconds.
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Log Analytics with Amazon Elasticsearch Service & Kibana
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Big Data Architectural Patterns and Best Practices on AWS
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¿Quién es Amazon Web Services?
Esta sesión está enfocada en mostrar cómo las empresas pueden optimizar sus recursos a través de las soluciones basadas en la nube, poniendo foco en la diferenciación, la innovación y reducción de riesgos en la infraestructura.
Por Ricardo Rentería de Amazon
AWS Big Data Solution Days
Join us for a series of introductory and technical sessions on AWS Big Data solutions. Gain a thorough understanding of what Amazon Web Services offers across the big data lifecycle and learn architectural best practices for applying those solutions to your projects.
We will kick off this technical seminar in the morning with an introduction to the AWS Big Data platform, including a discussion of popular use cases and reference architectures. In the afternoon, we will deep dive into Machine Learning and Streaming Analytics. We will then walk everyone through building your first Big Data application with AWS.
What's new in AWS?
This presentation from the AWS Lab at Cloud Expo Europe 2014 contains details of newly announced services from Amazon Web Services, including Amazon Kinesis, Amazon WorkSpaces, AWS CloudTrail (beta), Amazon AppStream and Amazon RDS for PostgreSQL (beta)
Big Data Solutions Day - Calgary
This is the complete deck presented at the Westin Calgary Hotel, on August 16th, 2016.
It covers the current state of the AWS Big Data Solution set. Contains several use cases of Big Data, Machine Learning, and a tutorial on how to implement and use Big Data on the AWS Cloud Platform.
Introduction to Amazon Relational Database Service (Amazon RDS)
by Yoav Eilat, Product Marketing Manager, AWS
AWS Migration or 24x7 Support
High Standard of Reliability and Proficiency in Cloud COmputing in Amazon Web Services
Introducing Amazon Kinesis: Real-time Processing of Streaming Big Data (BDT10...
"This presentation will introduce Kinesis, the new AWS service for real-time streaming big data ingestion and processing.
We’ll provide an overview of the key scenarios and business use cases suitable for real-time processing, and discuss how AWS designed Amazon Kinesis to help customers shift from a traditional batch-oriented processing of data to a continual real-time processing model. We’ll provide an overview of the key concepts, attributes, APIs and features of the service, and discuss building a Kinesis-enabled application for real-time processing. We’ll also contrast with other approaches for streaming data ingestion and processing. Finally, we’ll also discuss how Kinesis fits as part of a larger big data infrastructure on AWS, including S3, DynamoDB, EMR, and Redshift."
AWS FSI Symposium 2017 NYC - Moving at the Speed of Serverless ft Broadridge
AWS’ suite of serverless technology has enabled enterprises in Financial Services to move quickly from conception to reality. By leveraging AWS, you can run code without provisioning or managing servers—and you only pay for what you use. In this session, we will walk through how we worked with Broadridge to take their Experience Manager application from design to deployment and provide details around how numerous AWS services were leveraged, including Cognito, Lambda, S3, DynamoDB, and SES. We will also dive into how the use of serverless technology can enable developers to move quickly, while improving security postures, minimizing management, and simplifying operations.
2016 AWS Big Data Solution Days
Join us for a series of introductory and technical sessions on AWS Big Data solutions. Gain a thorough understanding of what Amazon Web Services offers across the big data lifecycle and learn architectural best practices for applying those solutions to your projects.
We will kick off this technical seminar in the morning with an introduction to the AWS Big Data platform, including a discussion of popular use cases and reference architectures. In the afternoon, we will deep dive into Machine Learning and Streaming Analytics. We will then walk everyone through building your first Big Data application with AWS.
Introduction to Amazon Relational Database Service
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 to focus on your applications and business. We’ll discuss Amazon RDS fundamentals, learn about the seven available database engines, and examine customer success stories.
Introduction to Amazon Relational Database Service
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 to focus on your applications and business. We’ll discuss Amazon RDS fundamentals, learn about the seven available database engines, and examine customer success stories.
Introduction to Amazon Relational Database Service
by Dhanraj Pondicherry, Sr. Manager of Solution Architecture, AWS
AWS re:Invent 2016: AWS Database State of the Union (DAT320)
Raju Gulabani, vice president of AWS Database Services (AWS), discusses the evolution of database services on AWS and the new database services and features we launched this year, and shares our vision for continued innovation in this space. We are witnessing an unprecedented growth in the amount of data collected, in many different shapes and forms. Storage, management, and analysis of this data requires database services that scale and perform in ways not possible before. AWS offers a collection of such database and other data services like Amazon Aurora, Amazon DynamoDB, Amazon RDS, Amazon Redshift, Amazon ElastiCache, Amazon Kinesis, and Amazon EMR to process, store, manage, and analyze data. In this session, we provide an overview of AWS database services and discuss how our customers are using these services today.
Introduction to Amazon Relational Database Service
by Joyjeet Banerjee, Enterprise Solution Architect, AWS
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 to focus on your applications and business. We’ll discuss Amazon RDS fundamentals, learn about the seven available database engines, and examine customer success stories. Level 100
Amazon Kinesis Platform – The Complete Overview - Pop-up Loft TLV 2017
Real-Time Streaming Analytics became popular amongst many verticals and use cases. In AdTech, Gaming, Financial Service and IoT, AWS customers are leveraging Amazon Kinesis platform to ingest billions of events every day and process them in real-time. In this session, we will discuss Amazon Kinesis Streams, Amazon Kinesis Firehose and Amazon Kinesis Analytics. We will show best practice and design patterns in integrating Amazon Kinesis platform with other services like Amazon EMR, Redshift, Amazon Elasticsearch and AWS lambda as well as 3rd party connectors like storm, Spark and more.
Building Data Analytics pipelines in the cloud using serverless technology
Big Data analytics is well known to uncover hidden insights that gives an organization an edge over the competition. But data does not need to be big in order to be useful. Smaller companies and startups may lack the volume of data that qualifies as big data, yet the variety of data can still yield a trove of insights that helps in driving the business strategies of a company. Startups may also lack the resources to fund an additional, seemingly expensive development project. The key is in simplicity, start small, simple and architect for scalability and performance. But how do you start? In this presentation, we share our experience in building a cost effective, AWS serverless data analytics platform that became an invaluable tool for sales, marketing and operational efficiencies.Serverless architectures simplify development work where servers and software are managed by a third party cloud provider. Developers can focus on just building the data wrangling and data analysis logic where critical aspects like scalability and high availability are guaranteed by the cloud provider. Besides, serverless services offer the pay as you go model, where you pay only based on the amount of resources you use. This turns out to be another attractive aspect where costs can be managed based on the usage. In this presentation we will focus on techniques and best practices to build a big data analytics platform using AWS serverless services like Lambda, DynamoDB, S3, Kinesis, Athena, QuickSight and Amazon ML. We will highlight the strengths of each of these services and what role each plays in the data analytics pipeline. We compare and contrast these services with some of the other popularly used big data technologies like Hadoop, Spark and Kafka. We also demonstrate the usage of these services to build intelligent components that detect anomalies, yield recommendations, simulate chat bots and generate predictive analytics.
High Performance Databases
Learn more about the high performance databases available on Amazon Web Services.
MSC203_How Citrix Uses AWS Marketplace Solutions To Accelerate Analytic Workl...
Find out how Citrix built a solution using Matillion ETL for Amazon Redshift from AWS Marketplace to load all data into an Amazon Redshift cluster, allowing them to do their analytics on the entire environment at a single time. We’ll discuss the transition made to consolidate multiple disparate databases in order to run analytic workloads, get a holistic view of all their data sources, and prevent inconsistent data from being captured.
How Citrix Uses AWS Marketplace Solutions to Accelerate Analytic Workloads on...
Find out how Citrix built a solution using Matillion ETL for Amazon Redshift from AWS Marketplace to load all data into an Amazon Redshift cluster, allowing them to do their analytics on the entire environment at a single time. We’ll discuss the transition made to consolidate multiple disparate databases in order to run analytic workloads, get a holistic view of all their data sources, and prevent inconsistent data from being captured.
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Introduction to Amazon Relational Database Service
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More from Elif Nurber Karakaş
Amazon Lake Formation
What is AWS Lake Formation?
AWS Lake Formation is a fully managed service that makes creating, maintaining, and managing data
lakes a breeze. AWS Lake Formation automates most of the data lake construction process, reducing
the time it takes from months to weeks. The service acts as a single point of control for identifying,
retrieving, cleaning, and transforming data from thousands of sources, as well as enforcing security
regulations across various services and acquiring and managing fresh data. AWS Lake Formation is
controlled via a single dashboard from which you can configure and alter all data lake lifecycle phases
and operations.
Amazon Glue
What is AWS Glue?
AWS Glue is an ETL (Extract, Transform, and Load) data integration solution that is fully managed.
The process of preparing and merging data for analytics, machine learning, and application
development is known as data integration. It's made to make it simple and inexpensive to not just
categorize your data, but also to clean, enhance, and transfer it.
Amazon Data Pipeline
What is Amazon Data Pipeline?
Amazon Data Pipeline is a web service that enables you to process and transport data across AWS
computing and storage services, as well as on-premises data sources, in a reliable manner. It
automates the extraction, transformation, combination, validation, and uploading of data for further
analysis and display. It can handle many data streams at the same time and delivers end-to-end
speed by removing mistakes or overcoming delays.
Amazon Quicksight
Amazon QuickSight is a cloud-based machine learning-powered business analytics tool provided by
Amazon Web Services. It allows businesses to make more informed, data-driven decisions.
Businesses may use Amazon QuickSight BI to generate and analyze data visualizations and receive
easy-to-understand insights to help them make better business decisions. These dynamic dashboards
may be integrated into a variety of apps, portals, and websites with ease. Because Amazon
QuickSight is scalable, it can handle tens of thousands of users without the need for extra
infrastructure or capacity planning. It is also device agnostic.
Amazon Redshift
What is Amazon Redshift?
Amazon Redshift is a fully managed, petabyte-scale, cloud-based data warehouse product designed for large-scale dataset storage and analysis. It is also used to perform large-scale database migrations. Amazon Redshift helps you predict your costs by providing all of them at a price performance that is up to three times better than other cloud data warehouses from the start.
Amazon OpenSearch Service
What is Amazon OpenSearch Service?
OpenSearch is a distributed, open-source search and analytics package that may be used for real-
time application monitoring, log analysis, and internet search, among other things. With OpenSearch
Dashboards, an integrated visualization tool that makes it easy for users to examine their data,
OpenSearch provides a highly scalable solution for quick access and reaction to massive amounts of
data. The Apache Lucene search library, as well as OpenSearch, Elasticsearch, and Apache Solr,
support it. Elasticsearch 7.10.2 and Kibana 7.10.2 were used to create OpenSearch and OpenSearch
Dashboards. The Apache License Version 2.0 applies to all software in the OpenSearch project (ALv2).
Amazon Managed Streaming for Apache Kafka
Amazon MSK is a fully managed service that makes it easy for you to build and run applications that use Apache Kafka to process streaming data. Apache Kafka is an open-source platform for building real-time streaming data pipelines and applications. With Amazon MSK, you can use native Apache Kafka APIs to populate data lakes, stream changes to and from databases, and power machine learning and analytics applications.
Amazon CloudSearch
Amazon CloudSearch is a search service that is completely controlled. It's easy to set up, use, and it's a cost-
effective search option. The fundamental text search engine of Amazon CloudSearch is Apache Solr. Full-text search, faceted search, real-time indexing, dynamic clustering, database integration, NoSQL
capabilities, and productive document handling are all supported by it.
Amazon Elastic MapReduce
Amazon EMR is an Amazon-managed cluster platform that makes it easier to handle and
analyze massive amounts of data using big data frameworks like Apache Hadoop and Apache
Spark on AWS. EMR may be used to process data for analytical and business intelligence
tasks in combination with Apache Hive and Apache Pig. EMR lets you transform and move
large amounts of data across AWS data stores and databases.
Amazon athena
Amazon Athena is a new serverless query service that makes it easy to analyze data in Amazon S3, using standard SQL. With Athena, there is no infrastructure to setup or manage, and you can start analyzing your data immediately. You don’t even need to load your data into Athena, it works directly with data stored in S3.
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Neuro-symbolic is not enough, we need neuro-*semantic*
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All of this illustrated with link prediction over knowledge graphs, but the argument is general.
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Amazon Kinesis Data Streams
- 1. AMAZON KINESIS DATA STREAMS Elif Nurber Karakaş
- 2. WHAT IS KINESIS DATA STREAMS? AMAZON KINESIS DATA STREAMS IS A SCALABLE, LONG-LASTING, AND LOW-COST STREAMING DATA SOLUTION FROM AMAZON.
- 3. FEATURES OF KINESIS Serverless Highly avaliable and durable Low latency Dedicated throughput per consumer Choose between on-demand and provisioned capacity mode Secure and compliant Integrated with other AWS services
- 4. WHAT CAN I DO WITH KINESIS DATA STREAMS? Accelerated log and data feed intake and processing Real-time metrics and reporting Real-time data analytics Complex stream processing
- 5. WHY USE KINESIS DATA STREAMS? As a streaming technology, Kinesis provides a number of specific advantages. It is, in particular, a managed service, which means that AWS, rather than developers, is in charge of most of the system administration. This allows developers to concentrate more on their code and less on system management.
- 6. There is no limit to the amount of streams you may have in your account while using provisioned mode. Before base64 encoding, a record's data payload can be up to 1 MB in size. Within 24 hours, you can switch between on-demand and provided capacity modes for any data stream in your AWS account twice. QUATOS AND LIMITS
- 7. CUSTOMERS