This session kicks off the Couchbase Connect Big Data track by answering some fundamental questions about the relationship between NoSQL and Hadoop. Both of these address the “big data” challenge but they target different parts of it. We’ll get concrete about where it makes sense to deploy NoSQL versus where it makes sense to deploy Hadoop. More importantly, we’ll discuss how NoSQL and Hadoop compliment each other and why they’re stronger together.
Corporate IT organizations in the financial industry have been tackling data challenges at scale for many years now. Traditional sources of data in banking include Customer Account data, Transaction Data, Wire Data, Trade Data, Customer relationship management (CRM), General Ledger and other systems supporting core banking functions. Shortly after these “systems of record” became established, enterprise data warehouse (EDW) based architectures began to proliferate with the intention of mining the trove of real world data that Banks possess with an intention of providing Business Intelligence (BI) capabilities across a range of use cases – Risk Reporting, Customer Behavior, Trade Lifecycle, Compliance Reporting etc. .Banks, insurance companies and securities firms that have begun to store and process huge amounts of data in Apache Hadoop have better insight into both their risks and opportunities.
So what capabilities do Hadoop & NoSQL add to existing RDBMS based technology that did not exist before?The answer is that using Hadoop & NoSQL a vast amount of information can be stored at much lower price point. Thus, Banks can not only generate insights using a traditional ad-hoc querying model but also build statistical models & leverage Data Mining techniques (like classification, clustering, regression analysis, neural networks etc) to perform highly robust predictive modeling. Such models encompass the Behavioral and Realtime paradigms in addition to the traditional Historical mode. This talk will explore such key business challenges and how Hortonworks/Couchbase are driving innovation in the industry around Descriptive Analytics and Data Science.
LinkedIn’s mission is to connect the world’s professionals to make them more productive and successful. LinkedIn members use the company’s products to get access to people, jobs, news, updates, and insights that help them be great at what they do. To support its goals on an engineering level, LinkedIn services must sustain high levels of QPS while providing data integrity. In this talk, we will discuss how LinkedIn uses Couchbase Server to help with read scaling and performance of its high impact services. We will also talk about some tooling we have created to integrate Couchbase Server into our systems and how we operationally manage our Couchbase Server clusters. Finally, we will explore some future uses of Couchbase Server within our environment.
Since being open sourced, Apache Kafka has been widely adopted by organizations ranging from web companies like Uber, Netflix, and LinkedIn to more traditional enterprises like Cerner, Goldman Sachs, and Cisco. These companies use Kafka in a variety of ways: 1) as a pipeline for collecting high-volume log data to load into Hadoop, 2) as a means of collecting operational metrics to feed monitoring/alerting applications, 3) for low latency messaging use cases, and 4) to power near real time stream processing. In this talk, Ewen and David will 1) discuss how companies are using Apache Kafka, 2) explore how its unique architecture enables it to be used for both real time processing and as a bus for feeding batch systems like Hadoop, and 3) describe where it fits in the Big Data ecosystem.
With NoSQL becoming an integral technology in Big Data infrastructure, the need for moving and transforming data between systems has accelerated. The Couchbase Connector for Informatica provides ETL integration that enables you to manage and transform data between Couchbase Server, a distributed elastic NoSQL document database, and any other relational or big data system.
Tableau enables people to ask questions of their data by combining analytics and visualization together with revolutionary technology. With N1QL, Tableau can now provide visual analytics to rich JSON data in Couchbase Server. In this session, you’ll learn how to pair Tableau with Couchbase Server to dramatically reduce the time-to-insight for analyzing unstructured operational data. The talk will focus on practical information such as the cycle of visual analysis, tips and tricks to drive smart and fast business insights, and interactive demos to put concepts into practice.
It’s one thing to collect a large amount user-generated data and process it. It’s another to ingest and store massive amounts of machine-generated data in real time. This session will focus on how machines and things are changing database requirements from the throughput and latency they require, to the sheer number of them, to their evolving data model, and the impact of location on network availability.
Running a site like PayPal requires both huge scale and a lot of processing to support a complex growing business. Over time, the PayPal business faces a challenge in managing the user information required to run PayPal services. By leveraging Couchbase Server, the PayPal Data Service team is able to accommodate fast access to user information at scale while streaming data into Hadoop. The solution is able to process millions of updates a minute while leveraging Kafka’s high throughput capabilities to get this user data into the Hadoop cluster.
Moody’s Analytics helps capital markets and risk management professionals worldwide respond to an evolving marketplace with confidence. Choosing the right technologies to capture, analyze and customize data is crucial to the company’s offerings. In this session, you’ll hear how we evaluated NoSQL and the challenges we faced. This presentation’s goal is to provide an overview of the nuances involved in first figuring out the problem, the challenges encountered when searching for that technology silver bullet, and ultimately where NoSQL fits into the data strategy roadmap.
Building enterprise ready customer data analysis systems can be challenging for newcomers as well as battle proven architects alike. Avalon takes the mystery out of connecting Couchbase Server into real time analytic architectures by providing a working architecture demonstrating a real world customer analysis case study. Come join Avalon as Andy and Chad take complex challenges and make them easy.