Cortana Analytics Workshop: Operationalizing Your End-to-End Analytics Solution

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Wee Hyong Tok. With Azure Data Factory (ADF), existing data movement and analytics processing services can be composed into data pipelines that are highly available and managed in the cloud. In this demo-driven session, you learn by example how to build, operationalize, and manage scalable analytics pipelines. Go to https://channel9.msdn.com/ to find the recording of this session.

Data & Analytics

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Azure Data Factory
A managed cloud service for building &
operating data pipelines

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Using Azure Data Factory
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Analytics Solution

Cortana Analytics Workshop: Operationalizing Your End-to-End Analytics Solution

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Analyzing big data is a challenge, requiring lots of processing power and storage. Cloud Computing is an ideal platform to tackle this problem. HD Insight on Microsoft Azure deploys Hadoop and other open source big data tools to the cloud, making it easier to take advantage of the high scalability of this platform. In this session, you will learn what tools are available in HD Insight and how to use them to store, process, and analyze large amounts of data.

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Until recently, data was gathered for well-defined objectives such as auditing, forensics, reporting and line-of-business operations; now, exploratory and predictive analysis is becoming ubiquitous, and the default increasingly is to capture and store any and all data, in anticipation of potential future strategic value. These differences in data heterogeneity, scale and usage are leading to a new generation of data management and analytic systems, where the emphasis is on supporting a wide range of very large datasets that are stored uniformly and analyzed seamlessly using whatever techniques are most appropriate, including traditional tools like SQL and BI and newer tools, e.g., for machine learning and stream analytics. These new systems are necessarily based on scale-out architectures for both storage and computation. Hadoop has become a key building block in the new generation of scale-out systems. On the storage side, HDFS has provided a cost-effective and scalable substrate for storing large heterogeneous datasets. However, as key customer and systems touch points are instrumented to log data, and Internet of Things applications become common, data in the enterprise is growing at a staggering pace, and the need to leverage different storage tiers (ranging from tape to main memory) is posing new challenges, leading to caching technologies, such as Spark. On the analytics side, the emergence of resource managers such as YARN has opened the door for analytics tools to bypass the Map-Reduce layer and directly exploit shared system resources while computing close to data copies. This trend is especially significant for iterative computations such as graph analytics and machine learning, for which Map-Reduce is widely recognized to be a poor fit. While Hadoop is widely recognized and used externally, Microsoft has long been at the forefront of Big Data analytics, with Cosmos and Scope supporting all internal customers. These internal services are a key part of our strategy going forward, and are enabling new state of the art external-facing services such as Azure Data Lake and more. I will examine these trends, and ground the talk by discussing the Microsoft Big Data stack.

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The developer world is changing as we create and generate new data patterns and handling processes within our applications. Additionally, with the massive interest in machine learning and advanced analytics how can we as developers build intelligence directly into our applications that can integrate with the data and data paths we are creating? The answer is Azure Databricks and by attending this session you will be able to confidently develop smarter and more intelligent applications and solutions which can be continuously built upon and that can scale with the growing demands of a modern application estate.

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Embarking on building a modern data warehouse in the cloud can be an overwhelming experience due to the sheer number of products that can be used, especially when the use cases for many products overlap others. In this talk I will cover the use cases of many of the Microsoft products that you can use when building a modern data warehouse, broken down into four areas: ingest, store, prep, and model & serve. It’s a complicated story that I will try to simplify, giving blunt opinions of when to use what products and the pros/cons of each.

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