Best Practices for Database Migration to the Cloud: Improve Application Performance & Accelerate Data Insights

This document discusses data mesh, a distributed data management approach for microservices. It outlines the challenges of implementing microservice architecture including data decoupling, sharing data across domains, and data consistency. It then introduces data mesh as a solution, describing how to build the necessary infrastructure using technologies like Kubernetes and YAML to quickly deploy data pipelines and provision data across services and applications in a distributed manner. The document provides examples of how data mesh can be used to improve legacy system integration, batch processing efficiency, multi-source data aggregation, and cross-cloud/environment integration.

AWS offers a variety of data migration services and tools to help you easily and rapidly move everything from gigabytes to petabytes of data. We can provide guidance and methodologies to help you find the right service or tool to fit your requirements, and we share examples of customers who have used these options in their cloud journey.

This is the first in a series of five webinars that look 'under the covers' of Denodo's industry leading Data Virtualization Platform. The webinar will provide an overview of the architecture and key modules of the Denodo Platform - subsequent webinars in the series will take a deeper look at some of the key modules and capabilities of the platform, including performance, scalability, security, and so on. More information and FREE registrations to this webinar: http://goo.gl/fLi2bC To learn more click to this link: http://go.denodo.com/a2a Join the conversation at #Architect2Architect Agenda: The Denodo Platform Platform Architecture Key Modules Connectors Data Services and APIs

This webinar is to discuss how AWS Database Migration Service helps you migrate local database to the AWS Cloud environment, quickly and securely, and make sure the source database remains fully operational during the migration, minimizing downtime to applications that reply on the database. You will also learn how to lay down the plan for database migration for your company. This is a level 200 webinar that covers introduction to AWS Database Migration Service (DMS) and AWS Schema Conversion Tool (SCT), tips to swiftly migrate existing databases to cloud, best practices of database management on cloud plus a number of successful use cases.

This document outlines an agenda for a 90-minute workshop on Snowflake. The agenda includes introductions, an overview of Snowflake and data warehousing, demonstrations of how users utilize Snowflake, hands-on exercises loading sample data and running queries, and discussions of Snowflake architecture and capabilities. Real-world customer examples are also presented, such as a pharmacy building new applications on Snowflake and an education company using it to unify their data sources and achieve a 16x performance improvement.

Many organizations focus on the licensing cost of Hadoop when considering migrating to a cloud platform. But other costs should be considered, as well as the biggest impact, which is the benefit of having a modern analytics platform that can handle all of your use cases. This session will cover lessons learned in assisting hundreds of companies to migrate from Hadoop to Databricks.

Finance Data Lake objective is to create a centralized enterprise data repository for all Finance and Supply Chain data. It serves as the single source of truth. It enables a self-service discovery Analytics platform for business users to answer adhoc business questions and derive critical insights. The data lake is based on open source Hadoop big data platform and a very cost effective solution in breaking the ERP data silos and simplifying the data architecture in the enterprise. POCs were conducted on in-house Hortonworks Hadoop data platform to validate the cluster performance for Production volumes. Based on business priorities, an initial roadmap was defined using 3 data sources including 2 SAP ERPs and Peoplesoft (OLTP systems). Development environment was established in AWS Cloud for agile delivery. The near real time data ingestion architecture for the data lake was defined using replication tools and custom SQOOP based micro-batching framework and data persisted in Apache Hive DB in ORC format. Data and user security is implemented using Apache Ranger and sensitive data stored at rest in encryption zones. Business data sets were developed in Hive scripts and scheduled using Oozie. Multiple reporting tools connectivity including SQL tools, Excel and Tableau were enabled for Self-service Analytics. Upon successful implementation of the initial phase, a full roadmap is established to extend the Finance data lake to over 25 data sources and enhance data ingestion to scale as well as enable OLAP tools on Hadoop.

This document discusses migrating databases from Oracle to PostgreSQL using AWS Database Migration Service (DMS) and AWS Schema Conversion Tool (SCT). It provides an overview of DMS and SCT, the migration process which involves assessing the database with SCT and then using DMS to replicate the data, and resources available to customers for both services. It also provides background on PostgreSQL, describing it as an open-source, object-relational database management system.

This document provides an introduction to Amazon Aurora, AWS's managed relational database service. It discusses how Aurora was built to provide the speed and availability of commercial databases at the simplicity and cost-effectiveness of open source databases. The document outlines key Aurora features like automatic scaling, continuous backups, replication across Availability Zones, and integration with other AWS services. Customer case studies show how Aurora provides better performance at lower costs than alternative database options. The document also covers migration options and how Aurora offers a simpler, more cost-effective database solution than on-premises or self-managed options.

With the aid of any number of data management and processing tools, data flows through multiple on-prem and cloud storage locations before it’s delivered to business users. As a result, IT teams — including IT Ops, DataOps, and DevOps — are often overwhelmed by the complexity of creating a reliable data pipeline that includes the automation and observability they require. The answer to this widespread problem is a centralized data pipeline orchestration solution. Join Stonebranch’s Scott Davis, Global Vice President and Ravi Murugesan, Sr. Solution Engineer to learn how DataOps teams orchestrate their end-to-end data pipelines with a platform approach to managing automation. Key Learnings: - Discover how to orchestrate data pipelines across a hybrid IT environment (on-prem and cloud) - Find out how DataOps teams are empowered with event-based triggers for real-time data flow - See examples of reports, dashboards, and proactive alerts designed to help you reliably keep data flowing through your business — with the observability you require - Discover how to replace clunky legacy approaches to streaming data in a multi-cloud environment - See what’s possible with the Stonebranch Universal Automation Center (UAC)

Presentation on Data Mesh: The paradigm shift is a new type of eco-system architecture, which is a shift left towards a modern distributed architecture in which it allows domain-specific data and views “data-as-a-product,” enabling each domain to handle its own data pipelines.

This document provides an overview of building a modern cloud analytics solution using Microsoft Azure. It discusses the role of analytics, a history of cloud computing, and a data warehouse modernization project. Key challenges covered include lack of notifications, logging, self-service BI, and integrating streaming data. The document proposes solutions to these challenges using Azure services like Data Factory, Kafka, Databricks, and SQL Data Warehouse. It also discusses alternative implementations using tools like Matillion ETL and Snowflake.

Build a simple data lake on AWS using a combination of services, including AWS Glue Data Catalog, AWS Glue Crawlers, AWS Glue Jobs, AWS Glue Studio, Amazon Athena, Amazon Relational Database Service (Amazon RDS), and Amazon S3. Link to the blog post and video: https://garystafford.medium.com/building-a-simple-data-lake-on-aws-df21ca092e32

This document provides an agenda and overview for a workshop on building a data lake on AWS. The agenda includes reviewing data lakes, modernizing data warehouses with Amazon Redshift, data processing with Amazon EMR, and event-driven processing with AWS Lambda. It discusses how data lakes extend traditional data warehousing approaches and how services like Redshift, EMR, and Lambda can be used for analytics in a data lake on AWS.

There is a lot of interest these days in migrating data from commercial relational databases to open-source relational databases. PostgreSQL is a great choice for migration, offering advanced features, high performance, rock-solid data integrity, and a flexible open-source license. PostgreSQL is compliant with ANSI SQL. It supports drivers for nearly all development languages, and it has a strong community of active committers and companies to provide support. In this talk, we demonstrate an overall approach for migrating an application from your current Oracle database to an Amazon Aurora PostgreSQL database.

This document discusses how to build a successful data lake by focusing on the right data, platform, and interface. It emphasizes the importance of saving raw data to analyze later, organizing the data lake into zones with different governance levels, and providing self-service tools to find, understand, provision, prepare, and analyze data. It promotes the use of a smart data catalog like Waterline Data to automate metadata tagging, enable data discovery and collaboration, and maximize business value from the data lake.

Learning Objectives: - Learn how to migrate Oracle databases to the cloud - Learn how to run additional components of the Oracle stack on AWS - Get acquainted with other database options on AWS

In this session, we will show you how to understand what data you have, how to drive insights, and how to make predictions using purpose-built AWS services. Learn about the common pitfalls of building data lakes and discover how to successfully drive analytics and insights from your data. Also learn how services such as Amazon S3, AWS Glue, Amazon Redshift, Amazon Athena, Amazon EMR, Amazon Kinesis, and Amazon ML services work together to build a successful data lake for various roles, including data scientists and business users. Speakers: Sachin Punyani, Solutions Architect, AWS Sachin Arora, Head BD, Big Data & Analytics, AWS

Whatever your mission, you need to empower your team to make smart decisions. Effective organizations use self-service analytics that combine data from multiple sources, to inform data-driven, timely problem-solving. Join AWS databases, analytics, machine learning, and blockchain expert, Darin Briskman, to explore how citizens in Canada and beyond are better served by organizations that promote decision-making through data.

In this session we will discuss the ideal use cases for relational and nonrelational data services, including Amazon ElastiCache for Redis, Amazon DynamoDB, Amazon Aurora, Amazon Neptune, Amazon ElasticSearch Service, Amazon TimeStream, Amazon QLDB, and Amazon DocumentDB. This session will focus on how to evaluate a new workload for the best managed database option.

The document discusses choosing the appropriate database for different types of workloads. It outlines several AWS databases and their best use cases based on data shape, size, and compute needs. Examples include using DynamoDB for key-value workloads, DocumentDB for flexible schemas, and Neptune for graph queries. The document emphasizes starting with application requirements and choosing a database tailored to the specific workload.

Speaker: Renee Lo, Head of Big Data, Analytics, and AI, ASEAN, AWS Customer Speaker: Natalia Kozyura, Head of Innovation Center, FWD Group We discuss architectural principles that simplify big data analytics. We'll apply these principles to various stages of big data processing: collect, store, process, analyse, and visualise. We'll discuss how to choose the right technology in each stage based on criteria such as data structure, query latency, cost, request rate, item size, data volume, durability, and so on. Finally, we provide reference architectures, design patterns, and best practices for assembling these technologies to solve your big data problems at the right cost.

In this session, we discuss architectural principles that help simplify big data analytics. We'll apply principles to various stages of big data processing: collect, store, process, analyze, and visualize. We'll discuss how to choose the right technology in each stage based on criteria such as data structure, query latency, cost, request rate, item size, data volume, durability, and so on. Finally, we provide reference architectures, design patterns, and best practices for assembling these technologies to solve your big data problems at the right cost.

Many organizations have adopted or are in the process of adopting DevOps methodologies in their quest to accelerate the delivery of software capabilities, features, and functionalities to support their organizational objectives. By applying the same practices, DataOps aims to provide the same level of agility in delivering data and information to the organization. AWS Lake Formation, in coordination with other AWS Services, enables DevOps methodologies to be realized through the Data Supply Chain Pipeline.

Business analysts require easy access to data from across different parts of the business. In this session, learn why more customers have adopted Amazon Redshift than any other cloud-native Data Warehouse, and how they are building a broader analytics capability with data lakes on AWS. Understand how AWS built machine learning (ML) into the services, taking away many of the time-intensive tasks of building an analytics platform. We cover why these customers choose Amazon Redshift for the accessibility to analysts, business reporting, deep security, ability to scale from GB to PB, and integration with the broader platform. Learn about these customers who are increasingly opening insights to data analysts for data discovery and data scientists for machine learning. We also share how the AWS services such as AWS Glue and the coming ML-enabled AWS Lake Formation take away most of the heavy lifting,

Securely setting up and managing data lakes today involves complicated and time-consuming tasks. AWS Lake Formation is a new service that makes it easy to set up a secure data lake in just days.This session will focus on the security of the data lake as well as performing deduplication and fuzzy matching of your data. You will be able to ingest, catalog, cleanse, transform, and secure your data. AWS Lake Formation makes it easier to combine analytic tools, like Amazon EMR, Amazon Redshift, Amazon Athena, Amazon Sagemaker, and Amazon QuickSight, on data in your data lake.

Data warehouses (DWs) are central repositories of integrated data from one or more disparate sources, used for reporting, data analysis, and business intelligence. In this session, we dive deep into concepts, strategies, and best practices for designing a cloud-based data warehousing solution using Amazon Redshift - the petabyte-scale data warehouse in AWS. Additionally, we review patterns in hybrid environments for collecting, storing, and preparing data for DWs using Amazon DynamoDB, AWS Database Migration Service, Amazon Kinesis Data Firehose, and Amazon Simple Storage Service (Amazon S3). Learn how Fannie Mae successfully migrated from its on-premises DW to Amazon Redshift and achieved its goals by implementing parallel query execution, key performance adjustments, and more.

The document discusses strategies for building a data lake architecture on AWS to support different types of data consumption like dashboards, ad-hoc analysis, reporting, and machine learning. It describes selecting the appropriate AWS services based on factors like data structure, usage patterns, data temperature, and the needs of different audiences like data scientists, business users, and developers. Examples of architectures are provided for dashboards, search-enabled dashboards, ad-hoc analysis, reporting, and machine learning workflows. The document emphasizes understanding your data and audience needs to enable effective data consumption from the data lake.

AWS has been supporting companies across Australia and New Zealand to put their most innovative tools and technologies to work to achieve their business needs and goals. AWS and our ecosystem of partners has helped the likes of CP Mining, IntelliHQ, WesCEF, Oz Minerals, Woodside and many more to modernise their analytics and data architecture in order to successfully generate business value from their data. This event series aimed to educate customers with a broader understanding of how to build next-gen data lakes and analytics platforms and make connections with AWS.

AWS provides a wide range of data analytics tools with the power to analyze vast volumes of customer, business, and transactional data quickly and at low cost. In this session, we provide an overview of AWS analytics services and discuss how customers are using these services today. We will also discuss the new database and analytics services and features we launched in the last year.

AWS provides a wide range of data analytics tools with the power to analyze vast volumes of customer, business, and transactional data quickly and at low cost. In this session, we provide an overview of AWS analytics services and discuss how customers are using these services today. We will also discuss the new database and analytics services and features we launched in the last year.

Learn about AWS business intelligence (BI) analytics, visualization, artificial intelligence, and machine learning services that can transform data into insights.

Most companies are overrun with data, yet they lack critical insights to make timely and accurate business decisions. They are missing the opportunity to combine large amounts of new, unstructured big data that resides outside their data warehouse with trusted, structured data inside their data warehouse. In this session, we discuss the most common use cases with Amazon Redshift, and we take an in-depth look at how modern data warehousing blends and analyzes all your data to give you deeper insights to run your business. Equinox Fitness Clubs joins us to share their journey from static reports, redundant data, and inefficient data intergration to a modern and flexible data lake and data warehouse architecture that delivers dynamic reports based on trusted data.

This document discusses how companies are increasingly data-centric and how data has become a strategic asset. It introduces several AWS database and data storage services like Amazon Aurora, DynamoDB, DocumentDB, ElastiCache, Neptune, Timestream, and QLDB. These services provide different data models and use cases like relational, key-value, document, in-memory, graph, time-series, and ledger data. The document highlights features of each service like performance, scalability, availability, security, and ease of use. It also discusses how the AWS Database Migration Service can help migrate databases to AWS.

The document discusses big data analytics and machine learning on AWS. It describes what big data is and the 3Vs of big data - variety, velocity, and volume. It provides examples of AWS services that can be used for big data analytics like S3, Redshift, EMR, Athena, and Kinesis. It also provides examples of customers like Sysco, FINRA, and Nasdaq that are using AWS services to build data lakes and leverage big data analytics.

Learn about data lifecycle best practices in the AWS Cloud, so you can optimize performance and lower the costs of data ingestion, staging, storage, cleansing, analytics and visualization, and archiving.

AWS provides the most comprehensive, secure, scalable, and cost-effective portfolio of services for building data lakes for analytics. In this session, learn how to discover, load, store, catalog, prepare, and secure your data in a data lake. Then, learn to analyze with the largest choice of analytics approaches, including big data, data warehouse, operational, real-time streaming analytics, and even ML and AI. Ensure that your needs are met for existing and future analytics use cases, and discover how leading companies found success with their data lake initiatives.

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.

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.

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

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.

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.