Kickstart your data strategy for 2018: Getting started with Amazon Redshift

Parallel processing, columnar structure, and petabyte scaling make Amazon Redshift a powerful data warehousing platform. Get the full value of your company's analytics with Amazon Redshift and Matillion ETL. - Get an overview of the latest Amazon Redshift features - Find out how massive amounts of data can be loaded in a matter of minutes with Matilion ETL - Learn best practices for cloud data warehousing with Amazon Redshift Matillion ETL is the fastest, easiest, and most efficient way to leverage these benefits and achieve the full potential of your business.

Using ELT to load 1 Billion Rows of Data in 15 Minutes

Matillion ETL, which uses push-down ELT, is the fastest way to build end-to-end, production-ready big data stacks in Amazon Redshift, Google BigQuery, and Snowflake. Watch us create a finished fact table in fifteen minutes using one billion rows of data from multiple source systems—live. Watch the webinar to - Learn how to process big data faster with Matillion ETL. - Learn how to leverage Matillion ETL for Amazon Redshift, - Google BigQuery, and Snowflake - Find out how Matillion ETL makes loading and preparing massive amounts of data easier and more efficient - Discover how brands like GE, Converse, and Docusign use Matillion ETL to deliver low-cost, high-powered data solutions

Simplifying Your Journey to the Cloud: The Benefits of a Cloud-Based Data War...

As companies grow, so does the volume of their data. Without the proper solutions in place to quickly store, measure and analyze that data, its usefulness quickly declines. See our latest webinar to learn about how companies are increasingly turning towards cloud-based data warehousing to derive more value out of their data and apply their findings to make smarter business decisions. The webinar covers core topics including: - The benefits of using Snowflake’s unique architecture for interacting with data. - How Matillion can help you quickly load and transform your data to maximize its value. - Expert advice on how to apply data warehousing and ETL best practices. Watch the full webinar: https://youtu.be/mIOm3j431OQ

Lets Talk Google BigQuery

Google BigQuery is one of the largest, fastest, and most capable cloud data warehouses on the market. In this webinar, we review BigQuery best practices and show you how Matillion ETL can help you get the most out of the platform to gain a competitive edge. In this webinar: - Discover how to work quickly and efficiently with Google BigQuery - Find out the best ways to monitor and control costs - Hear tips and tricks for loading and transforming massive amounts of data in BigQuery with Matillion ETL - Get expert advice on improving your performance in BigQuery for quicker data analysis - Learn how to optimize BigQuery for your marketing analytics needs

Pick a Winner: How to Choose a Data Warehouse

This document summarizes a webinar about choosing a cloud-based data warehouse. It discusses evaluating different data warehouse technologies like MPP vs SMP and columnar vs row-oriented storage. It also introduces Matillion, a company that provides ETL tools for loading and transforming data in Amazon Redshift, Snowflake and Google BigQuery data warehouses. The webinar agenda includes discussing when a data warehouse is needed, criteria for evaluating options, different technologies, and a demonstration of Matillion's software.

Get Savvy with Snowflake

In this webinar you'll learn how to quickly and easily improve your business using Snowflake and Matillion ETL for Snowflake. Webinar presented by Solution Architects Craig Collier (Snowflake) adn Kalyan Arangam (Matillion). In this webinar: - Learn to optimize Snowflake and leverage Matillion ETL for Snowflake - Discover tips and tricks to improve performance - Get invaluable insights from data warehousing pros

Google BigQuery Best Practices

In this webinar you'll learn about the best practices for Google BigQuery—and how Matillion ETL makes loading your data faster and easier. Find out from our experts how to leverage one of the largest, fastest, and most capable cloud data warehouses to improve your business and save money. In this webinar: - Discover how to work fast and efficiently with Google BigQuery - Find out the best ways to monitor and control costs - Learn to leverage Matillion ETL and optimize Google BigQuery - Get tips and tricks for better performance

This document provides an overview of building a serverless data lake architecture on AWS. It discusses using AWS S3 for storage, AWS Glue for data cataloging and ETL processing, AWS Athena for running SQL queries, and Jupyter Notebooks for exploratory analysis. The full architecture shown brings these services together to allow for ingesting, storing, processing, and analyzing large amounts of data in a serverless and cost-effective manner.

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Google Bigtable

GirdhareeSaran

Bigtable is Google's proprietary large-scale database system that handles structured data. It uses Google File System, Chubby Lock Service, SSTable and other Google technologies. Bigtable allows storing terabytes or petabytes of time-series, financial, IoT, graph and other data. It is used prominently in Google Search, App Engine, Google Analytics, Google Earth and other applications. Quickstarts are provided to write and read data from Bigtable tables using the cbt tool or HBase shell. Key features include large storage capacity, integration with machine learning tools, high throughput, availability and scalability.

BDW16 London - Alex Bordei, Bigstep - Building Data Labs in the Cloud

Building Data Labs in the Cloud summarizes how to build data labs in the cloud by connecting on-premise services through VPN or targeted firewalls, integrating identity services between on-premise and cloud realms, enabling single sign-on with two-factor authentication, using encryption with cloud or on-premise HSMs, leveraging Spark for data science, SQL, ETL, machine learning and graph processing, adopting a multi-context architecture for maintenance and efficiency, and ensuring real-time systems provide performance, stability, serviceability and fault tolerance.

How to become an big data rockstar in 15 minutes - Akmal Chaudhri

Dataconomy Media

This document provides an overview of how to become a big data rockstar in 15 minutes by using Apache Ignite. It discusses how Ignite can be used to turbocharge SQL queries, share data and state across Spark jobs, leverage its machine learning library for data science tasks, and ease DevOps dilemmas with Kubernetes integration. Ignite is an open source in-memory computing platform that can be used for caching, processing, and analyzing large datasets.

AWS Community Day Nordics 2018 - Aino Health: Transition to serverless and le...

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BDW16 London - Deenar Toraskar, Think Reactive - Fast Data Key to Efficient C...

The Basel Committee on Banking Supervision (BCBS) and local regulators has been focussed on making banks more safe and resilient. A whole raft of new capital charges and constraints on liquidity and leverage have been introduced: Basel II.5, Basel III, Dodd-Frank, FRTB (“Basel IV”), etc. These have significantly increased the risk data management capabilities banks must have—capabilities that only big data tools can provide. This talk will cover the challenges of building a position-aware risk management platform that properly aggregates all intra-day trading activity, monitors exposures and risk. The fast data stack can help banks create such a platform and provide a robust foundation to achieve compliance and, ultimately a significant competitive edge by making efficient use of capital.

Snowflake Company Presentation

AndrewJiang18

The document discusses Snowflake, a cloud data warehouse company. Snowflake addresses the problem of efficiently storing and accessing large amounts of user data. It provides an easy to use cloud platform as an alternative to expensive in-house servers. Snowflake's business model involves clients renting storage and computation power on a pay-per-usage basis. Though it has high costs, Snowflake has seen rapid growth and raised over $1.4 billion from investors. Its competitive advantages include an architecture built specifically for the cloud and a focus on speed, ease of use and cost effectiveness.

ELT is Better. Here's Why.

The demand for speed in data loading and transformation has never been greater—companies that can’t keep up will be left behind. Traditional ETL is being outpaced by cloud-native ELT tools which are both faster and more efficient. In this webinar, we examine the differences between ETL and ELT, and explain what they mean for your data and your business. - Learn what makes ELT different from traditional ETL - See how a modern ELT architecture outperforms ETL - Explore how the latest trends in ELT technology affect your business - Discover how Matillion’s ELT solutions can help you quickly load and transform massive amounts of data

Presto Summit 2018 - 08 - FINRA

kbajda

Altis AWS Snowflake Practice

SamanthaSwain7

Euronext_AWS_talend_connect_paris_2018.pdf

Euronext, the 1st European stock exchange with €3.7 trillion in market cap, built a governed data lake based on Amazon AWS to analyze data from one of the largest databases in Europe enriched with 1.5 billion new messages every day. Euronext uses Talend and AWS services - Amazon S3, Amazon Redshift and Amazon EMR for better agility, elasticity, breadth of functionality and cost savings, compared to the previous Netezza-based solution, while guaranteeing data governance and regulatory compliance.

Building Data Lakes with Apache Airflow

Gary Stafford

Making Sense of Remote Sensing

The document discusses efficient exploitation of remote sensing data. It summarizes Grega Milcinksi's presentation on Sentinel Hub, a platform for accessing and processing satellite data. It notes that large volumes of remote sensing data are created daily but pre-processing into "data cubes" limits flexibility. The document recommends processing data on-demand using cloud computing. Sentinel Hub is highlighted as an example that provides open data access through APIs and applications using AWS services. It processes over 50 million requests per month from various data sources. The document concludes that public data should be openly available in the cloud and reasonable business models are needed for commercial data.

Cloud Developer Days - BigQuery

Wlodek Bielski

This document provides an overview and agenda for a presentation on Google BigQuery. It includes sections on BigQuery internals and architecture, use cases, and demos. The presentation will cover an overview of Google Cloud Platform and BigQuery, their internals including storage, execution engines, and compute resources, common use cases, and end with a demo in Google Cloud Platform console and BigQuery. It is presented by an expert in BI, analytics, and cloud platforms.

Comparing Apache Ignite and Cassandra for Hybrid Transactional/Analytical Pro...

Tom Diederich

Dmitriy Setrakyan, founder and Chief Product Officer at GridGain, delivered this talk during the April 11 Bay Area In-Memory Computing Meetup. Abstract: The 10x growth of transaction volumes, 50x growth in data volumes -- along with the drive for real-time visibility and responsiveness over the last decade -- have pushed traditional technologies including databases beyond their limits. Your choices are either buy expensive hardware to accelerate the wrong architecture, or do what other companies have started to do and invest in technologies being used for modern hybrid transactional/analytical processing (HTAP). This presentation covered: * The requirements for real-time, high volume HTAP * Architectural best practices, including how in-memory computing fits in and has eliminated tradeoffs between consistency, speed and scale * A detailed comparison of Apache Ignite and GridGain® for HTAP

BigQuery for Beginners

Better&Stronger

An short introduction on Big Query. With this presentation you'll quickly discover : How load data in BigQuery How to build dashboard using BigQuery How to work with BigQuery and, at last but not least, we've added some best practices We hope you'll enjoy this presentation and that it will help you to start exploring this wonderful solution. Don't hesitate to send us your feedbacks or questions

Replatform your Teradata to a Next-Gen Cloud Data Platform in Weeks, Not Years

VMware Tanzu

This document discusses Datometry, a virtualization platform that allows enterprises to replatform their legacy data warehousing workloads to the cloud at a fraction of the typical cost, time, and risk of conventional replatforming methods. It notes that Datometry keeps existing applications and logic, allowing businesses to move rapidly to the cloud while preserving years of infrastructure investment. Charts show that Datometry reduces both the upfront switching costs and ongoing annual costs of replatforming projects compared to conventional approaches. The document promotes Datometry as a way to redefine the economics of modernizing data management applications and proprietary systems like Teradata.

Hadoop Data Warehouse

Kalyana Miriyala

This document discusses building a scalable and performable data warehouse on Hadoop for business intelligence and analytics. It describes using a AWS r3 8Xlarge 4 Node Cluster and Oracle Exadata Appliance for the data tier stack. The data warehouse contains 100GB of fact table data from 650 million sales transactions across 9 dimensions involving 12 million customers. Tools like Cloudera's TPC-DS Toolkit and Impala are used to access and query the data warehouse. Challenges include integrating Hive and HBase with Impala and slow conversion from text to the more efficient Parquet format.

Leadership Session: AWS Database and Analytics (DAT206-L) - AWS re:Invent 2018

Raju Gulabani, Vice President of Databases, Analytics, Machine Learning, and Blockchain at AWS, presented on AWS databases and analytics services. He discussed AWS's strategy of having a broad and deep portfolio of purpose-built analytics services including Redshift, Athena, EMR, QuickSight, and SageMaker. He also provided examples of customers like Epic Games and Anthropic using these services to build analytics solutions at large scale.

Modern Cloud Data Warehousing ft. Intuit: Optimize Analytics Practices (ANT20...

This document discusses Amazon Redshift and Intuit's modernization of their cloud data warehousing practices on AWS. It provides an overview of AWS databases and analytics services including Redshift, Athena, EMR, and more. It then discusses improvements to Redshift's speed, scale, simplicity and security. Finally, it discusses Intuit's transition from an on-premises datacenter to using AWS cloud services.

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ELT is Better. Here's Why.

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Comparing Apache Ignite and Cassandra for Hybrid Transactional/Analytical Pro...

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Using AWS Purpose-Built Databases to Modernize your Applications

As you look to modernizing your applications, you will need to consider your database options to meet the new application requirements. AWS offers a series of purpose-built databases that include relational, key value, document, graph and cache use cases to help you deliver new and enhanced functionalities. In this webinar session, we share the different modern application architectures, and how to combine different database services to meet your requirements. Understand how to modernize your relational databases through easy upgrades with Amazon Relational Database Service and learn how to migrate from one database to another with AWS Database Migration Service and AWS Schema Conversion Tool. Speaker: Blair Layton, Business Development Manager, Amazon Web Services

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In this session, we provide an update on Amazon Redshift, and look at a case study from Equinox Fitness Clubs. We show you how Amazon Redshift queries data across your data warehouse and data lake, without the need or delay of loading data, to deliver insights you cannot obtain by querying independent data silos. Discover how Equinox Fitness Clubs transitioned from on-premises data warehouses and data marts to a cloud-based, integrated data platform, built on AWS and Amazon Redshift. Learn about their journey from static reports, redundant data, and inefficient data integration to a modern and flexible data lake and data warehouse architecture that delivers dynamic reports based on trusted data.

Getting Started with Apache Ignite as a Distributed Database

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This document summarizes a presentation about using Apache Ignite as a distributed database. It discusses the limitations of standard RDBMS and NoSQL databases, and how Ignite addresses these by providing a consistent and scalable solution. The presentation outlines Ignite's features and architecture, and shares reference cases from companies like ING Group and Ping An Insurance that have benefited from using Ignite. It concludes by providing additional Ignite learning resources and information on downloading a free trial.

TiVo: How to Scale New Products with a Data Lake on AWS and Qubole

TiVo: How to Scale New Products with a Data Lake on AWS and Qubole

Amazon Web Services LATAM

In our webinar, representatives from TiVo, creator of a digital recording platform for television content, will explain how they implemented a new big data and analytics platform that dynamically scales in response to changing demand. You’ll learn how the solution enables TiVo to easily orchestrate big data clusters using Amazon Elastic Cloud Compute (Amazon EC2) and Amazon EC2 Spot instances that read data from a data lake on Amazon Simple Storage Service (Amazon S3) and how this reduces the development cost and effort needed to support its network and advertiser users. TiVo will share lessons learned and best practices for quickly and affordably ingesting, processing, and making available for analysis terabytes of streaming and batch viewership data from millions of households.

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In this session, we take a deep dive on Amazon Redshift architecture and the latest performance enhancements that give you faster insights into your data. We also cover Redshift Spectrum, a feature of Redshift that enables you to analyze data across Redshift and your Amazon S3 data lake to deliver unique insights not possible by analyzing independent data silos. A customer is joining us to share how they were able to extend their data warehouse to their data lake to encompass multiple data sources and data formats. This modern architecture helps them tie together data sources to get actionable insights across their business units.

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Citrix Moves Data to Amazon Redshift Fast with Matillion ETL

Citrix moved large amounts of customer usage data to Amazon Redshift for analytics using Matillion ETL. Initially, Citrix built custom workflows to transform and load the data, but this required more maintenance. Using Matillion, Citrix can now load millions of rows into Redshift in minutes, allowing faster and more granular analysis of user data to optimize their applications. The speed and simplicity of Matillion has increased the efficiency of Citrix's analytics initiatives.

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This document discusses big data processing at scale using AWS services. It begins with an overview of the increasing volume of data being generated. Common architectures for collecting, storing, processing, analyzing and consuming big data on AWS are then presented. Specific AWS services for each step of the big data workflow like Kinesis, S3, EMR, Redshift and Glue are described. Common architectural patterns for building event-driven batch analytics and combining real-time and batch analytics on AWS are shown. The document concludes with success stories of customers like Netflix, FINRA and Nasdaq using AWS for big data and analytics workloads.

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The document discusses choosing the right database for different types of workloads. It covers operational databases like Amazon DynamoDB, Amazon RDS, Amazon ElastiCache and Amazon Neptune that are well-suited for transactional workloads. It also discusses analytic databases like Amazon Redshift, Amazon Athena, Amazon Kinesis Analytics and Amazon Elasticsearch Service that are well-suited for large-scale analytics and business intelligence workloads. The document emphasizes that AWS offers a variety of purpose-built databases and there is no need to pick just one, as different databases can be combined to solve different aspects of a problem.

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Amazon Redshift is a cloud data warehouse product built on top of ParAccel technology that handles large datasets and database migrations at petabyte scale. It differs from Amazon RDS in its ability to handle analytics workloads on big data using a columnar database. Redshift allows up to 16 petabytes of data storage compared to RDS Aurora's 128 terabytes. It uses parallel processing and compression to perform operations on billions of rows at once, making it useful for storing and analyzing large data volumes.

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We have recently seen some convergence of different database technologies. Many customers are evaluating heterogeneous migrations as their database needs have evolved or changed. Evaluating the best database to use for a job isn’t as clear as it was ten years ago. In this session, we discuss the ideal use cases for relational and nonrelational data services, including Amazon ElastiCache for Redis, Amazon DynamoDB, Amazon Aurora, and Amazon Redshift. This session digs into how to evaluate a new workload for the best managed database option.

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Snowflake is a cloud data platform company that was founded in 2012. It has over 640 employees, 1500+ customers, and has raised $923 million in funding. Snowflake provides an elastic data warehouse that allows customers to instantly scale compute and storage resources. It offers a fully managed service with no infrastructure to manage and allows customers to consolidate siloed datasets and analyze data across multiple cloud regions and accounts.

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NoSQL is a term used to describe high-performance, non-relational databases. NoSQL databases use a variety of data models, including document, graph, and key-value. NoSQL databases are recognized for ease of development, scalable performance, high availability, and resilience. With AWS, you can use fully-managed NoSQL database services such as Amazon DynamoDB for all applications that need consistent, single-digit millisecond latency at any scale; Amazon Neptune, a graph database built for the cloud; and Amazon ElastiCache, an in-memory data store service where you can choose Redis or Memcached to power your real-time applications.

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