Making Big Data Work

The promise of big data is great, but the gap between promise and reality may be even greater. Getting value from big data begins with a business case that drives choices of the right data among many big data possibilities—social media data, machine-to-machine data, e-commerce data, geospatial data, and more.

This TDWI Seminar in Washington, DC, offers a sequence of carefully selected courses to help you do just that. Join us to understand the scope and complexity of big data, to learn the specifics of Hadoop technologies, and to see a variety of big data application opportunities and case studies.

Monday, April 20 - TDWI Big Data Fundamentals | Big data is a hot topic in BI and analytics. Yet it is a complex topic that is still in the early stages of evolution. Successful big data projects that deliver real business value are challenged by multiple definitions and rapidly shifting technologies. Achieving good return on your big data investment requires strategy that focuses on purpose, people, and process before exploring data and technologies. Strategy drives planning and architecture to ensure that big data complements and does not disrupt the existing BI and analytics environment. To prepare for success with big data, start by understanding all of the pieces and how they fit together.

You Will Learn

• Common definitions of big data and the implications of each

• Key characteristics of big data and why size is not among the top five

• The structures that can be found in “unstructured” data

• Types of big data sources—streaming data, social data, sensor data, etc.

• Value opportunities and common applications for big data

• Considerations when adapting architectures, organizations, and cultures to incorporate big data

• The scope of big data processes, tools, and technologies

Geared To: Business and data analysts; BI and analytics program and project managers; BI and data warehouse architects, designers, and developers; data governance and data quality professionals getting started with big data; anyone seeking to cut through the hype to understand the opportunities, challenges, and realities of the big data phenomenon

Tuesday, April 21 - Understanding Hardoop  | The advent of big data has changed the world of analytics forever. Big data challenges scalability and big data platforms reshape BI and analytics infrastructure. Hadoop has taken center stage in the big data revolution, and we’ll all need to understand the platform, its ecosystem, and how to work with it. The enterprise adoption of Hadoop is met with mixed responses. Join us to learn Hadoop basics of Hadoop, understand the realities, sort out the conflicts, and find out where and how Hadoop fits into your BI and analytics future. We will discuss the ecosystem and its intricacies, look at where it will help, and discuss how companies have embraced its usage.

You Will Learn

• The what and why of Hadoop

• Hadoop components

• Technical architecture

• Core components (MapReduce, HDFS, YARN)

• Hadoop tools (Hbase, Hive, Pig, Mahout, Impala)

• Hadoop setup and configuration

• Hadoop administration and management

• Using Hadoop: Applications and examples

Geared To: Architects, developers, anyone interested in Hadoop

Wednesday, April 22 - Big Data Applications | The Internet of things (IoT) is the subject of hype and wild projections. According to McKinsey Global Institute, the Internet of things will have a potential economic impact of $2.7 trillion to $6.2 trillion annually by 2025. The IoT is not a futuristic, aspirational technology or business trend or wave. We have started realizing the value of these types of applications and trends in life today in the devices, sensors, cloud infrastructure, and data and business intelligence tools we are already using. 

Rather than thinking about the IoT in terms of everything—such as billions of devices and sensors—focus on what matters most to you. Instead of thinking about the massive amount of data being produced, think about how one piece of data can provide value to your business and create impact on profit and value. There are a number of fantastic use cases with implementations in manufacturing, healthcare, pharmacy, transportation, customer safety, 911 systems, and much more.

Big data is now more than buzz, and it represents the next phase of decision support platform from both operational and analytical perspectives. The big questions haunting executives now are related to the “data science” concept. Is this a team or a unicorn—or do we even need this type of skill in the real world? These question are the focus of this session. The role has been declared the next sexiest job, but where are we heading and how do we succeed?

Machine learning is the future of data management, especially considering the volume, veracity, and velocity of data. Are we ready? Do we know what and how to apply this technology?

You Will Learn

• The Internet of things: What is it?

• Machine learning

• Data science: myth and reality

• Risks and points of failure

• The enterprise journey

• Case studies

Geared To: Business innovators; enterprise architects; data science teams