Learn how to run successful big data projects, how to resource your teams, and how the teams should work with each other to be cost effective. This book introduces the three teams necessary for successful projects, and what each team does.
Most organizations fail with big data projects and the failure is almost always blamed on the technologies used. To be successful, organizations need to focus on both technology and management.
Making use of data is a team sport. It takes different kinds of people with different skill sets all working together to get things done. In all but the smallest projects, people should be organized into multiple teams to reduce project failure and underperformance.
This book focuses on management. A few years ago, there was little to nothing written or talked about on the management of big data projects or teams. Data Teams shows why management failures are at the root of so many project failures and how to proactively prevent such failures with your project.
What You Will Learn
Discover the three teams that you will need to be successful with big dataUnderstand what a data scientist is and what a data science team doesUnderstand what a data engineer is and what a data engineering team doesUnderstand what an operations engineer is and what an operations team doesKnow how the teams and titles differ and why you need all three teamsRecognize the role that the business plays in working with data teams and how the rest of the organization contributes to successful data projects
Who This Book Is For
Management, at all levels, including those who possess some technical ability and are about to embark on a big data project or have already started a big data project. It will be especially helpful for those who have projects whichmay be stuck and they do not know why, or who attended a conference or read about big data and are beginning their due diligence on what it will take to put a project in place.
This book is also pertinent for leads or technical architects who are: on a team tasked by the business to figure out what it will take to start a project, in a project that is stuck, or need to determine whether there are non-technical problems affecting their project.
Jesse Anderson serves in three roles at Big Data Institute: data engineer, creative engineer, and managing director. He works on big data with companies ranging from startups to Fortune 100 companies. His work includes training on cutting-edge technologies such as Apache's Kafka, Hadoop, and Spark. He has taught over 30,000 people the skills needed to become data engineers.
Jesse is widely regarded as an expert in the field and for his novel teaching practices. He has published for OReilly and Pragmatic Programmers. He has been covered in prestigious publications such as: The Wall Street Journal, CNN, BBC, NPR, Engadget, andWired. He has spent the past 6+ years observing, mentoring, and working with data teams. He has condensed this knowledge of why teams succeed or fail into this book.
Part 1: Introducing Data Teams.- Chapter 1: Data Teams.- Chapter 2: The Good, the Bad, and the Ugly Data Teams.- Part 2: Building Your Data Team.- Chapter 3: The Data Science Team.- Chapter 4: The Data Engineering Team.- Chapter 5: The Operations Team.- Chapter 6: Specialized Staff.- Part 3: Working Together and Managing the Data Teams.- Chapter 7: Working as a Data Team.- Chapter 8: How the Business Interacts with Data Teams.- Chapter 9: Managing Big Data Projects.- Chapter 10: Starting a Team.- Chapter 11: The Steps for Successful Big Data Projects.- Chapter 12: Organizational Changes.- Chapter 13: Diagnosing and Fixing Problems.- Part 4: Case Studies and Interviews.- Chapter 14: Interview with Eric Colson and Brad Klingenberg, Stitch Fix.- Chapter 15: Interview with Dmitriy Ryaboy, Twitter, Cloudera, Zymergen.- Chapter 16: Interview with Bas Geerdink, ING, Rabobank.- Chapter 17: Interview with Harvinder Atwal, Moneysupermarket.- Chapter 18: Interview with a Large British Telecommunications Company.- Chapter 19: Interview with Mikio Braun, Zalando.-
