Big Data
in the Cloud

In any enterprise, one of the most prevalent security risks revolves around who has access to which resources. Whether data is being stored in a cloud solution or on-premises, there is a large challenge in knowing how to provide the correct privileges to associates. By using machine learning and clustering algorithms like the Louvain Method, we can group similar users our network and create two valuable features: (1) automated onboarding and (2) automated "rogue access" detection. With the utilization of machine learning, we have allowed our company to become a more well-managed company, and have reduced a major cybersecurity threat. This talk will be a deep dive into the model, data engineering and productionization of the web application interface.

Saavn is India’s leading music streaming service. Since context is key to music, we have built a system called Sniper that lets us identify cohorts of users in real-time and target them for marketing, advertising and recommendation purposes. This system allows us to understand user behavior by quantifying their engagement characteristics such as stream consumption, affinities or ads. Speed and scalability are critical to its design. This talk will cover our motivations behind building such a system and how big data technologies have helped us architect it.

Enterprises are building big data solutions with Azure Data Lake, an on-demand, real-time stream processing service with a no-limits data lake built to support massively parallel analytics. Patterns of enterprise solutions are emerging and evolving as customers migrate their analytics workloads to the cloud and embrace new business opportunities. With an overview of Azure Data Lake, this webinar briefly explores some of the choices customers are making in building big data solutions with Azure Data Lake.

Today, 91% of companies are ingesting data from third party partners to run their businesses. Additionally, 70% of companies either currently send or plan to send data to partners. This inter-company data collaboration powers insights, machine learning, and better consumer experiences. But, it also increases workloads for strapped engineering teams and creates challenges to data access. Learn how companies are streamlining and even automating their Data Operations to accelerate the time from data to business value.

At BloomReach we process around 100 million products everyday across all of our customers. For each customer, the feed processing needs to be fast and reliable, and while indexing there shouldn't be any impact on serving. We will walk over how we've built this in BloomReach while also making sure that the cost is minimal.

Data governance, discovery and lineage help data scientists find and integrate data of interest to uncover otherwise hidden trends, anomalies, and powerful predictors of business successes and failures. Comcast’s Streaming Data Platform comprises a wide variety of ingest, transformation, and storage services. Peer-reviewed Apache Avro schemas support end-to-end data governance. Apache Atlas is our metadata repository for data discovery and lineage. We have extended Atlas with custom data and process types, eg.: avro schemas; AWS S3 buckets and prefixes; kafka topics; and kinesis streams. Custom asynchronous messaging libraries notify Atlas of new data and schema entities and lineage links as they are created.

Continuous streams of data are generated in every industry from sensors, manufacturing IoT devices, business transactions, social media, network devices, clickstream logs, and more. Found within these streams of data are critical business insights that are waiting to be unlocked. Attend this session and learn how customers are creating solutions for fleet monitoring, smart grid, network monitoring, recommendations, and other real-time solutions to analyze multiple concurrent streams of data-in-motion into insights and actions for competitive advantage. In this session you will see demos and learn how services like Azure Event Hubs, Stream Analytics, Machine Learning, and other Azure services work seamlessly together to create your end to end real-time analytics solutions.

Today’s organizations are tasked with managing multiple data types, coming from a wide variety of sources. Faced with massive volumes and heterogeneous types of data, organizations are finding that in order to deliver insights in a timely manner, they need a data storage and analytics solution that offers more agility and flexibility than traditional data management systems. Every use case might be different and different use cases might need different tools. AWS provides a variety of options for your needs from RDS, EMR, Redshift, Athena and Quicksight. In this talk we will discuss the different technologies available on AWS and its application.

Once considered the "black magic" of digitally-born retailers like Amazon, personalizing the customer experience has now become table stakes for any retailer interested in surviving in the era of digital transformation. the techniques, tools and scalable platforms necessary to optimize customer interactions are now available and accessible for use by companies of any shape or size. We'll discuss how the use of big data technology in the cloud eases the implementation of common retail use cases as well as how it helps to avoid typical pitfalls.

The question is not much whether to migrate to the cloud or not. That question has likely already been answered by many organizations and the answer is a resounding full steam ahead. But the start of the journey can be daunting especially with a lot of ‘as-a-service’ terminology floating around. Please join James Curtis, senior analyst at 451 Research, as he discusses not only some industry trends and what many organizations are doing but also a simplified approach to understanding cloud services and how that might best fit your organization. Because it’s not so much buyer beware; it’s more about buyer understand.

Cloud has changed the game when it comes to data analytics. Previously, organizations had to lock themselves into a particular architecture and level of capacity for three to seven years and do all the lifting themselves. Cloud on the other hand allows them to experiment with different hardware and software options, get more of the solution as a service and scale up and down to meet project spikes and accelerate busy jobs at will. This makes it much more viable for any company to get the advantages of advanced analytics against large data sets, without an oversize IT staff or huge capital investments. Oracle cloud is specifically designed to help enterprises take advantage of cloud for data analytics—it offers massive non-variable performance, predictable low cost and broad choice of deployment and software options. Oracle and Qubole work together to deliver a new breed of data platform—capable of taming the scale, performance, cost and complexity issues associated with gaining business insight from data of all types.Watch this webinar to understand: - Summary of industry trends for big data on the cloud - How Oracle Cloud Infrastructure is optimized for big data workloads from a cost, performance and flexibility perspective - How Oracle Cloud Big Data solutions compare with on-premises and competing cloud options