Big Data for Data Warehouse and Business Intelligence Professionals.
How to solve common data warehouse headaches with big data concepts and technologies.
Do you have a background in data warehousing or relational databases? Are you about to embark on a big data journey? Do you want to learn more about how you can benefit from big data concepts and technologies but also want to understand the limitations of big data technologies? Then this training course is for you.
Audience: This training course introduces data warehouse professionals to the concepts of big data tools and technologies. It helps them to solve common problems in the Enterprise Data Warehouse. It is aimed at data warehouse managers, architects, and developers. Anyone who is involved in data warehousing and business intelligence.
Agenda: You can download the detailed agenda from our website (PDF).
What you will learn? Below I have list of only some of the questions that we will answer.
- What are the problems with the traditional approach to data warehousing?
- What are the limitations of a relational database (RDBMS) for data warehousing?
- What are the limitations of Hadoop for data warehousing?
- What storage types do we have on Hadoop and an RDBMS? What use cases do they support?
- Is the data warehouse obsolete?
- Data modelling on Hadoop vs RDBMS. What are the differences?
- What types of indexes do we have on Hadoop?
- What is the next generation data warehouse architecture?
- What is the difference between an RDBMS and a NoSQL database? What about NewSQL databases?
- What do we mean by read consisteny in a distributed system? How is read consistency different from transactional consistency?
- When should we use a graph database? What about search engines such as Solr/Lucene? Time series databases anyone?
- Is streaming architecture only a good fit for real time analytics?
- Is batch processing still needed?
- What are exactly once processing semantics, event time/processing times, event windows, exactly once end to end semantics, Kappa/Lambda architecture etc.
- When do we need approximate query engines?
- What innovations in data analytics can we expect over the next few years?
- What is data preparation and discovery?
- What are the seven different types of advanced analytics that will let you answer common business questions?
Benefits: The traditional approach to data warehousing has served us well over the last 25 years. However, various cracks have shown up over the last couple of years and we as data warehouse managers, architects, and developers are faced with a multitude of problems.
- The data warehouse is too slow to turn around questions by the business. Anything between 3-9 months is spent to get a new subject area into the data warehouse. 80-90% of enterprise data never makes it into the data warehouse. Important business decisions are made without seeing the full picture.
- The digitisation of all aspects of life has led to an explosion of data volumes. Machine generated data fuels the flames of data growth. Our data warehouse is bursting at the seams. License costs are soaring.
- Traditional data warehouse architecture and technology does not cater well for certain workloads, e.g. unstructured data, streaming data, and graph data.
- Advanced analytics has always been a secondary concern in the EDW. This is somewhat strange as advanced analytics applications deliver the greatest ROI.
In this course, we will describe these headaches in detail and prescribe various remedies.
Coursework: The training is based on slides. Some demonstrations will be delivered during the training.
Duration: 1 day. Training can be delivered onsite or through our virtual classroom facilities.
Pre-requisites: Understanding of data warehouse and relational database concepts.
Are you a consulting company? We are always looking for resellers of the course.
The Trainer: Uli Bethke
Uli has 17 years’ hands on experience as a consultant, architect, manager, and developer in the data industry. He frequently speaks at conferences. You might have seen him at Predict 2016 in Dublin. Uli has architected and delivered Big Data projects in Europe, North America, and South East Asia. He understands both the traditional world of data warehousing AND the brave new world of Big Data.
Uli is a regular contributor to blogs and books, holds an Oracle ACE award, and chairs the Hadoop User Group Ireland (~1,100 members, www.hugireland.org). He is also a co-founder and VP of the Irish chapter of DAMA, a non for profit global data management organization. He is also a co-founder of the Irish Oracle Big Data User Group.
He holds degrees from Freie Universit?t Berlin, Albrecht Ludwigs Universit?t Freiburg, and the University of Ulster, Coleraine.
Azure(AZ-900,AZ-204), C#,T-SQL, Angular, TypeScript/JavaScript, >10 years
7 年Maybe this is just technological hype . This will be automatically cured when a new "technology of solve an every problem and sell some new tools with SAS" appear. Maybe in next year :)
Scrum Master, Senior Project Leader at Nordea
7 年I would say that you can have a headache with both cases as well. There is no silverbullet solution. ??
Contract and Consultancy Data Engineering / Architecture / DBA
7 年Two different toolsets, two different mindsets. To compare the two is to compare a hammer to a screwdriver. A great deal of data is structured, especially business data. Show me a use-case where the relational model beats the non-relational model for structured data and I'll doff my cap to you. Until then, roll on SQL.
Senior ETL Specialist at National Treasury Management Agency
7 年ah ah I'm not so sure about that!
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