Data driven products
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In this presentation I talk about challenges in building data driven products. They range from technical to non technical. In high tech industry of big data we need great people with good communications skills as never before.
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LN Renganarayana, Architect, ML Platform and Services and Madhura Dudhgaonkar...
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Complex sentences; nominal clauses; subject clauses; extraposed subject
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Guía de entrenamiento para hablar en público.
Elena Garijo, de Lambda Soluciones de Gestión, impartió una sesión formativa el 26/11/16 en nuestro espacio en la que ofreció claves para gestionar los nervios y superar el miedo escénico, además de técnicas para preparar una exposición clara y precisa a la hora de hablar en público.
Frases diversas xxii
A empresa anunciou um novo produto que combina hardware e software para fornecer uma solução completa para clientes. O produto oferece recursos avançados de inteligência artificial e aprendizado de máquina para ajudar os usuários a automatizar tarefas complexas. Analistas acreditam que o produto pode ser um sucesso comercial se for fácil de usar e entregar resultados precisos como prometido.
Social Media Marketing
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Direct and indirect object clauses
This document discusses different types of nominal clauses that can perform the function of a direct or indirect object in complex sentences. It describes relative clauses introduced by relative pronouns like "that", dependent questions using subordinators like "if" and "whether", dependent wh-questions, non-finite clauses using infinitives or gerunds, and indirect object clauses introduced by "whoever". Examples are provided to illustrate each type of nominal object clause.
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Watch full webinar here: https://bit.ly/3ssplHS
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Artur Suchwalko “What are common mistakes in Data Science projects and how to...
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Cahyo is a data geek, gamer and comic nerd.
Excel and Database are his favorite since his middle school.
Having graduated from a Vocational High School of Informatics and Technology
made him able to start his career early and led many DWH BI projects at his early 20.
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***
This slide was shared at Tech in Asia Product Development Conference 2017 (PDC'17) on 9-10 August 2017.
Get more insightful updates from TIA by subscribing techin.asia/updateselalu
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Data-Ed Online: Data Architecture Requirements
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Webinar: Designing a schema for a Data Warehouse
Are you new to data warehouses (DWH)? Do you need to check whether your data warehouse follows the best practices for a good design? In both cases, this webinar is for you.
A data warehouse is a central relational database that contains all measurements about a business or an organisation. This data comes from a variety of heterogeneous data sources, which includes databases of any type that back the applications used by the company, data files exported by some applications, or APIs provided by internal or external services.
But designing a data warehouse correctly is a hard task, which requires gathering information about the business processes that need to be analysed in the first place. These processes must be translated into so-called star schemas, which means, denormalised databases where each table represents a dimension or facts.
We will discuss these topics:
- How to gather information about a business;
- Understanding dictionaries and how to identify business entities;
- Dimensions and facts;
- Setting a table granularity;
- Types of facts;
- Types of dimensions;
- Snowflakes and how to avoid them;
- Expanding existing dimensions and facts.
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Data driven products
- 1. Data products One feature at a time May 4th 2016 by Nataliya Nadtoka
- 2. About Centro http://www.centro.net/ image above belongs to Centro
- 3. Change the World one ETL at a time made with What I think I do
- 4. What I really do DATA BUGDATA BUGFEATUREBUG
- 5. Challenges in Building Data products • Business - related • Technical • Non - technical Disclaimer: opinions expressed in this presentation are my own. They do not reflect the situation at any of my current/former/future employers.
- 6. Business - related challenges • Lack of predictability of the outcome of Data Science/ML projects • “Just throw some data at it” approach • Scale and fault tolerance may be a solved problem, but it comes with a hefty price-tag • Moving fast versus focusing on data quality and then paying big for large unusable data
- 7. http://www.mammothdb.com/the-big-data-landscape-by-mammothdb/ It’s only a partial picture, there is more to it • Too much choice • Not everything is enterprise ready • Tech is a moving target Technical challenges
- 8. Technical challenges Lack of standardized data formats (IOT)
- 9. Technical challenges Building first, thinking of security and/or scalability later I wish I thought of it!
- 10. Non - technical challenges • Requires a strong advocacy and investment from the leadership • Lack of focus “Too many great ideas” • The quest for Data Scientist aka the Unicorn
- 11. I’m not saying that unicorns don’t exist…
- 12. Non technical challenges • The dream of Agile Data Science • Regulations of data ownership differ by country • Data ownership is still being defined
- 13. Some solutions • Build multidisciplinary Data team instead of searching for unicorns • Ruthlessly prioritize ideas • Focus on actionable data, not on data size • Communicate across the organization
- 14. Some solutions • Keep an eye on the budget • Build with necessary security/scalability from day one • Stay informed with trends, but take decisions that work for your organization
- 15. Conclusions • Keep your focus • Design with necessary security/scalability in mind • Communicate
- 16. Do we have time for questions?..