Prague data management meetup 2017-09-26
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This document discusses current trends in data management that were presented at the Prague Data Management Meetup on September 26, 2017. It begins with listing the agenda and history of past meetup events. Then, the main section analyzes trends in data governance, big data, data science, machine learning, artificial intelligence, data lakes, self-service BI, smartphone BI, advanced analytics, collaborative BI, appliances, visual data discovery, data storytelling, augmented analytics, cloud integration, cloud analytics, advanced data platform architectures, internet of things, data warehouse modernization, automation, analytical databases, and data source federation. It concludes with a final joke.
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The document discusses Prague Data Management Meetup, an open professional group that meets monthly to discuss topics related to data management. It then provides an agenda and history for past meetup events, covering subjects like data lakes, dark data, self-service BI, and data warehouse modeling. The remainder of the document focuses on data warehouse modeling, including comparisons of operational databases versus data warehouses, different data modeling approaches, and best practices for data warehouse design like using standard naming conventions and domain types.
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The document summarizes a Prague data management meetup. The agenda included an introduction about the group, and a presentation on the data lake concept. A data lake is defined as a massive, easily accessible data repository for storing big data without dropping attributes below aggregation levels. It aims to retain all attributes without knowing the scope or use of the data in advance. Quotes from industry experts provide perspectives on data lakes being large storage repositories and one of the more controversial ways to manage big data. Key factors for data lakes include metadata, data quality, technology used, value added, costs, security, governance, and data load processes.
Prague data management meetup 2015 11-23
The document summarizes a meetup about dark data. It defines dark data as data that is collected and stored by organizations but not used for insights or decision making. Examples of typical dark data sources are log files, customer information, previous employee data, and old documents. Reasons why dark data grows include legal risks, lost opportunities, and open-ended exposure. Estimates suggest 80-90% of organizational data is dark. Tips to manage dark data include implementing data governance, ongoing data assessment, retention policies, and specifically auditing dark data for security.
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Prague data management meetup 2017-09-26
- 1. Aktuální trendy v data managementu #17 26. 9. 2017 Prague Data Management Meetup
- 2. Agenda • Prague Data Management Meetup • Aktuální trendy v data managementu • Diskuze o hledání dalších témat
- 3. Prague Data Management Meetup Data Management Data Acquisition Data Storing Data Integration Data Analytics Data Usage • Open professional group • Everyone is welcomed • There are no bad topics☺ • Since September 2015
- 4. History # Date Topics 1 10. 9. 2015 Data Management 2 14. 10. 2015 Data Lake 3 23. 11. 2015 Dark Data (without Dark Energy and Dark Force) 4 12. 1. 2016 Data Lake 5 7. 3. 2016 Sad Stories About DW Modeling (sad stories only) 6 23. 3. 2016 Self-service BI Street Battle 7 27. 4. 2016 Let's explore the new Microsoft PowerBI! 8 22. 9. 2016 Data Management pro začátečníky Data Management for Beginners 9 17. 10. 2016 Small Big Data 10 22. 11. 2016 Základy modelování DW DW Modeling Basics # Date Topics 11 23.1.2017 Komponenty datových skladů Data Warehouse Components 12 28.2.2017 Operational Data Store 13 28.3.2017 Metadata v DW/BI DW/BI Metadata 14 25.4.2017 Jak se stát DW/BI konzultantem Be DW/BI Consultant 15 16.5.2017 SQL 16 29.5.2017 From IoT to AI: Applications of time series data 17 26.9.2017 Aktuální trendy v data managementu
- 8. Aktuální trendy v data managementu
- 10. Google Trends
- 12. Data Governance
- 13. Big Data
- 15. Data Science
- 16. Machine Learning
- 18. Data Lake
- 19. Self-service BI
- 20. Smartphone BI
- 22. Collaborative BI
- 23. Appliances
- 28. Cloud Analytics
- 32. Automation
- 35. Final Joke